johan fridstr omkth.diva-portal.org/smash/get/diva2:893822/fulltext01.pdfpassive sonar ranges were...

Some statistical properties of the

ambient noise in the Baltic Sea and

its relation to passive sonar

Johan Fridström

A thesis presented for the degree of

Master of Science

Royal Institute of TechnologySweden

2015

This thesis is part of an EU project financed by LIFE+

I

Abstract

The Baltic Sea Information on the Acoustic Soundscape (BIAS) is an European Unionfinanced research project coordinated by FOI. The goal is to determine the soundscapeof the Baltic Sea. This study is a part of BIAS and was focused on generating Wenzcurves for the Bothnian Sea, which is a part of the Baltic Sea. Wenz curves describethe spectral noise level at different sea states. The investigation of the soundscape wasdone for both summer and winter conditions when the hydrographical situations differ.Further investigations of the noise dependencies of the natural and anthropogenic soundsources were performed. Wind and ships were dominating in a broad frequency band.

The influence of ship noise on the ambient noise is dependent of frequency and distance.Ships within 5 km distance dominates the recorded noise levels and are not part of theambient noise. At distances longer than 5 km a single ship becomes non-distinguishableand part of the range independent noise floor.

Passive sonar ranges were calculated for two different sources. The range was shown tobe clearly dependent on the sea state. With an increase of wind speed from sea state 0.5to 3 the range increased with about 100%.

The results of this study will be used in BIAS and in related research projects. It maybe used for marine biologics but also for development of sonar and underwater systems.

II

Sammanfattning

Statistisk beskrivning av Östersjöns ljudlandskap – och dess p̊averkan p̊a räck-vidderna för passiva hydrofonsystem

BIAS är ett EU finaniserat projekt som koordinaeras av FOI och syftar till att beskrivaljudlandskapet i Östersjön. Denna uppsats är en del av BIAS med fokus p̊a att gener-era Wenzkurvor för Bottenhavet, vilket är en del av Östersjön. Wenzkurvor beskriverspektrala ljudegenskaper för olika väderlekar. Kurvorna är framtagna för b̊ade sommar-och vinterförh̊allanden. De dominerande ljudkällornas inverkan p̊a ljudbilden studerades.Resultaten visar att vind och fartyg är de dominerande faktorerna.

Fartygens bidrag till bakgrundsljudet visade sig bero p̊a b̊ade frekvens och avst̊andet tillmätpunkten. Fartyg innanför en radie p̊a 5 km dominerade de uppmätta ljudniv̊aerna.Utanför denna radie kunde inte enskilda fartyg med säkerhet idenfieras i ljuddata. Far-tygens ljud försvann in i trafikmullret som ständigt finns i Bottenhavet.

Utifr̊an de olika hydrografiska karaktärerna beräknades räckvidden för tv̊a ljudkällor fören passiv sonar. Räckvidden var klart beroende av väderförh̊allandet. Med en ökad vind-hastighet fr̊an sjötills̊and 0.5 till 3 ökade maximala detektionsavst̊andet för sonaren medungefär 100%.

Resultaten fr̊an den här studien kommer användas inom BIAS. De kan ocks̊a kommaatt användas av marinbiologer inom forskning p̊a djurlivet i Östersjön men kan ävenanvändas för utveckling av sonarsystem och andra undervattenssystem.

III

Preface

The work of this thesis was carried out at Totalförsvarets Forskningsinstitut in Kista,Stockholm. The task was a part of BIAS but also supported by FOI Underwater depart-ment. Professor Peter Sigray led the work with good help from PhD Leif K.G. Persson.

First I want to thank the entire Underwater department at FOI for all interesting discus-sions, nice coffee breaks and a very pleasant stay. Extra thank to PhD Jörgen Pihl whohelped me with sonar calculations. Also PhD Mats Nordin has earned extra gratitude forwithout any doubt recommended me for this job and for all good and guiding discussionsduring my entire study time at KTH.

I want to send special thanks to Professor Jakob Kuttenkeuler at KTH for the encour-agement and enthusiastic support during the work and MsD Sebastian Thuné for all theprofitable discussions.

I am most grateful for the help I got from Professor Peter Sigray and PhD Leif K.G.Persson who have helped me daily by answering question, provided me with good liter-ature, discussed solutions and results but most of all always prioritized my time beforetheir own making the time at FOI in Kista a very stimulating and funny period of my life.

Of course I also want to thank my parents, Inger and H̊akan, who always and doubtlesssupported me and made it possible to complete the Master Degree in Science. Also mygirlfriend Sandra owns my gratitude for all the positive support.

Stockholm June 2015

Johan Fridström

IV

Contents

1 Glossary and abbreviation 1

2 Introduction 4

3 Goals and structure of this thesis 7

4 Limitations 8

5 Theory Part I: Underwater acoustics 95.1 Basic acoustic properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 95.2 Relevant sources of noise in the Baltic Sea . . . . . . . . . . . . . . . . . 10

5.2.1 Sound propagation, refraction and absorption . . . . . . . . . . . 155.3 Ambient noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5.3.1 Rule of fives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165.3.2 Acoustics of the Baltic Sea . . . . . . . . . . . . . . . . . . . . . . 16

6 Theory Part II: Signal processing and analysing 196.1 Stationarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196.2 Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206.3 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216.4 Spectral analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226.5 Fourier analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226.6 Power Spectral Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226.7 Bandwidth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

7 Theory Part III: Passive sonar 247.1 Purpose and use of passive sonar . . . . . . . . . . . . . . . . . . . . . . 257.2 Passive sonar equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

8 Method 288.1 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

8.1.1 Noise recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288.1.2 Meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . 308.1.3 AIS data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

8.2 Signal processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308.2.1 Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308.2.2 Grubbs’ test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318.2.3 Kolmogorov-Smirnov two sample test of stationarity . . . . . . . . 328.2.4 Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

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8.3 Handling of different data sets . . . . . . . . . . . . . . . . . . . . . . . . 338.3.1 Combining ambient noise and meteorological data . . . . . . . . . 338.3.2 Combining ambient noise and shipping data . . . . . . . . . . . . 34

8.4 Method of determining ambient noise and itsdependencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358.4.1 Transformation from time to frequency plane . . . . . . . . . . . . 358.4.2 Correlation of wind, waves and ambient noise . . . . . . . . . . . 358.4.3 Wenz curves based on wind speed . . . . . . . . . . . . . . . . . . 368.4.4 Ambient noise dependency of significant wave height . . . . . . . 368.4.5 Ambient noise dependency of hydrography . . . . . . . . . . . . . 37

8.5 Sonar range calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

9 Results and discussion 399.1 Signal processing results . . . . . . . . . . . . . . . . . . . . . . . . . . . 399.2 Meteorological conditions at the measuring location . . . . . . . . . . . . 439.3 Ambient noise in different meteorological conditions . . . . . . . . . . . . 469.4 Shipping and ambient noise . . . . . . . . . . . . . . . . . . . . . . . . . 519.5 Range of passive sonar . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

10 Conclusions 59

References 61

A About the project A1A.1 BIAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A1

B The location A2B.1 Weather at the position . . . . . . . . . . . . . . . . . . . . . . . . . . . A2B.2 Hydrography of the location . . . . . . . . . . . . . . . . . . . . . . . . . A4

VI

1 Glossary and abbreviation

Ambient noise

Ambient noise is the noise background that is observed with a non-directional hydrophoneexcluding self-noise or identifiable localized source of [22]. In total absence of anthro-pogenic sounds the term natural ambient noise is used [23].

Anthropogenic

Means that (in this case) noise has its origin in the influence of human activity.

Bandwidth

Bandwidth is the range between frequency upper and lower frequency content of a signal.It is measured in Hz [23].

Noise

Noise is sound of random nature, which means that the spectrum contains no clear de-fined frequency components. Noise can also refer to unwanted signals. What is regardedas noise depends on the receiver and the context. [23].

Power Spectral Density:

A power representation of a signal with the amplitude energy/frequency. Often used forstationary random signals [20].

Octave

An octave is a doubling of frequency. Octave band is a frequency band with the midfrequency determining the name [25].

Refraction

The bending of sound due to environmental changes in the medium [5].

1

Root mean square

The squared mean value of the signal. It is often used to describe a quantity of a signalwith both positive and negative values [1].

Sea States

Sea states is defining different weather conditions at sea. It is ranged from zero to eightbased on wind speed and significant wave height [5].

Sound

Acoustic energy radiated through a medium from an object that vibrates. It can be eitherdesired signals or noise [23].

Sound pressure levels

The acoustic pressure relative the reference pressure 1 μPa squared measured in a loga-rithmic scale. Often used to express sound with a quantity [20].

Stationary

A signal whose statistical properties does not change with time is stationary [20].

Transient signal

A signal with a limited duration and a clear start and stop [25].

2

AIS Automatic Identification SystemBIAS Baltic Sea information on the Acoustic SoundscapeCDF Cumultative Distribution FunctionDFT Discrete Fourier TransformDSP Digital Signal ProcessingFMV Försvarets Materiellverk (Swedish Defence Material Administration)FOI Totalförsvarets Forskningsinstitut (Swedish Defence Research Agency)HELCOM Helsinki Commission, Baltic Marine Environment Protection CommissionHIRLAM High Resolution Limited Area ModelLOFAR Low Frequency Analysis RecorderPSD Power Spectral DensityPSU Practical Salinity Unit [g/kg = ppt]RMS Root Mean SquareSMHI Sveriges Meteorologiska och Hydrologiska Instut

(Swedish Meteorological and Hydrological Instute)SOFAR Sound Fixing and RangingSONAR Sound Navigation and RangingSPL Sound Pressure LevelSS Sea State

3

2 Introduction

The Element of Surprise is an effective tactic in warfare which was described in the Liadby Homeros. In the marine environment covert vessels will undoubtedly have a pointof advantage. The Swede Torsten Nordenfelt realized this fact and in 1883 he was thefirst person to build and design a steam engine driven torpedo-carrying submarine [27].Meanwhile the political arena of Europe got more and more infected by conflicts and inthe beginning of the 20th century became the start of a massive armament.

Submarines were used in naval battles for the first time in history. As a response, newstrategies were developed to detect and to combat the submarine threat. It becameimportant to acquire knowledge of the acoustic underwater environment. From a navalpoint of view it was important not only to address the sources (sound produced by sub-marines) but also to understand the properties of the ambient noise. The ability to “hideand seek” is strongly linked to these two properties. The Naval activities were howeverclassified and not open to the general public.

The Russians developed early a tool that used radio waves and hydro acoustics to deter-mine the distance to other ships. Their results were published almost simultaneously asthe British physicist Joly presented his method for determining distance and direction tounderwater sound sources. [7]. The Russian results were not recognized and the literaturetoday is based on results achieved by research performed by researchers in the westerncountries. The development continued and during the Second World War the listeningdevices were further developed. This in combination with an increased research in hydroacoustics resulted in a better understanding of the underwater sound environment. PostSecond World War a collection of papers written by researchers in the United Statesabout hydro acoustic behaviour were presented. This collection, Physics of Sound in theSea [2], became the keystone in the following development in the hydro acoustic field.

The civilian society regarded the underwater environment as silent, not at least high-lighted by the documentary movie The Silent World produced by the oceanographersJaques- Yves Cousteau and Louise Malle. The general public awareness of underwatersound was raised with the observation of the stranding whales, correlated with sonaractivities [6]. Presently, the awareness of the sound as a potential “pollution” is growing.

4

One of the most fundamental scientific investigation on underwater noise was done byWenz (1962). He showed that the ambient noise in water depends on many differentfactors. He summarized in a graph the variety of noise sources and their contributionto the ambient noise. This graph has been in use since then and is commonly known asWenz curves. His graph is shown in Fig. 2.1 and has been supplemented with hearingability for some species in the Baltic Sea.

Figure 2.1: Spectral sound levels in deep ocean adapted from the Wenz curves [26][18].Including anthropogenic and natural sources. The graph also shows the range of hearingthresholds for some animals of the Baltic Sea.

5

The graph of Wenz is still valid. It is used by researchers in hydroacoustics even thoughthe research is based on measurements undertaken in the years before 1962. The “silentworld” has however changed during the past fifty years. There are strong indications thatthe noise levels have increased [9]. An increased density of commercial shipping explainspart of this change but also the introduction of new types of propulsion systems. Further,the number of infrastructures at coastal and offshore areas has increased, compared tothe levels in the early sixties.

The research underlining the Wenz curves was based on noise in “deep” oceans. TheBaltic Sea is a shallow sea and the applicability of the Wenz curves in the Baltic Sea cantherefore to some degree be questioned. In his paper it is stated that a rise of the noiseintensity of 2-3 dB is expected in shallow waters. It should be underlined that he definedshallow as less than 100 m. Only a minor part of the Baltic Sea is deeper than 100 m, theactual average depth is 54 m. His forecast has been shown to be valid in the deep oceans.The Baltic Sea is a brackish sea where a strong thermocline develops during summer andit has a complex topography, which differs from the environments that Wenz results werebased on. This is one of the motivations for carrying through a study of the AmbientNoise of the Baltic Sea. One of the aims is to present an update of Wenz curves valid forthe Baltic Sea. However the generated Wenz curves would only be valid in peace time.A military conflict in the Northern Europe would probably reduce the shipping in theBaltic Sea which would result in a decrease of ambient noise levels.

As was eluded earlier, anthropogenic generated sound might have a negative impact onthe marine life. The focus of this thesis is to better understand the ambient sound and itsrole in the marine environment. Thus, the same result can be used both in environmen-tal research and for development of underwater systems such as submarines and sonarsystems. The work undertaken herein was a part of the Baltic Sea Information on theAcoustic Soundscape project (BIAS). The aim of BIAS was to establish the underwatersoundscape in accordance with the Marine Strategy Framework Directive, Descriptor 11,that declares that the member states of the European Union have to establish the baselineof sound levels before 2016 [23]. The Swedish Defence Research Agency (FOI) is coor-dinating the project and a more detailed presentation of BIAS is appended in Appendix A.

6

3 Goals and structure of this thesis

This thesis has two goals. The first is to develop tools for characterizing the ambientnoise. These were employed on data that were obtained in the Bothnian Sea. The secondgoal is to quantify the detection range of sound sources, based on the results from thefirst part.

The structure of this thesis follows the goals. In chapter 4 limitations of the study ispresented and is followed by the basic theory of hydro-acoustics which is introduced inchapter 5 and it begins with a general description of acoustics. A presentation of soundsources is given. The Wenz curves are introduced and specific properties of the uniqueacoustic environment of the Baltic Sea, and the Bothnian Sea in more detail, are dis-cussed. In chapter 6 signal processing theory is presented. The chapter starts with anintroduction of stationarity followed by explanations of outliers, correlation and spectralanalysis in text and illustrative examples. Chapter 7 contains a presentation of the sonarconcept. The passive sonar equation and the use of sonars is described. Chapter 8 con-sists of a comprehensive description of the methodology. The results are presented anddiscussed in chapter 9. In the final chapter, chapter 10, conclusions are made and anoutlook is given.

All research have been performed for the Bothnian Sea, well away from the coastline andshipping lanes. The following main topics have been investigated:

• Correlation of wind speed, wave height and ambient noise levels.

• Parametrization of the ambient noise.

• Establishment of the Wenz curves.

• Determination of the cumulative range-distribution.

• Establishment of detection ranges as function of frequencies and meteorologicalconditions for passive sonar.

7

4 Limitations

The recordings of the sound data were done with a sampling frequency of 32000 Hz. TheNyquist theorem restricts the analysis to frequencies lower than 16000 Hz. The lowerlimit of the bandwidth was set by the hardware of the autonomous recorder to 10 Hz.Limitations in battery and storage capacity only allowed recordings of 23 minutes everyhour. The obtained results are not complete and thus associated with statistical errors.

The results presented in this thesis are based on data from Bothnian Sea. The hydrophonewas located within 20 km from a shipping line. There were not enough of recordings withno ships within a 20 km radius to statistically determine the natural ambient noise.

At the location of the hydrophone some sea states never occurred. The sediment char-acteristics are also unknown. The meteorological data used were model based and notobtained from measurements. However, it was provided by SMHI and can for this studybe regarded as reliable.

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5 Theory Part I: Underwater acous-tics

Underwater acoustics is a broad discipline that encompasses many different applications.Here the study will be restricted to underwater phenomena that can be divided intosource, propagation and receiver. To achieve the aims oceanography and meteorologytheories and methods were required.

5.1 Basic acoustic properties

To generate sound a vibrating source and a medium with mass and elasticity are required.The vibrating source displaces adjacent particles in the medium. The elastic forces ofthe medium brings the particle back to its initial position. The initial displacementhas however forced neighbouring particles to move. The interaction between source andmedium results in a sound wave propagating from the source through the medium witha frequency determined by the vibrations of the source. Thus, sound is associated withpressure fluctuations and particle movements [25].

Sound pressure variations and particle motions are related through the impedance of themedium. Eq. 5.1 shows the relation [5]

p = uZ, (5.1)

where p is the acoustic pressure, u is the particle velocity and Z is the acoustic impedanceof the medium. The acoustic impedance is dependent on the properties of the medium.The equation of state for the sound speed is given by the density, salinity and temperature.The relation is not “obvious” and the sound speed is calculated by using mathematicalscripts. By tradition pressure is commonly expressed in relative form both in air and inwater. The decibel scale is used where pressure is related to a reference pressure. Theunderwater sound pressure level is calculated with Eq. 5.2. In this study sound pressurelevels (SPL) are used to express the ambient noise and is calculated as follows

SPL = 20 log10p

pref, (5.2)

where pref is 1 μPa for underwater acoustics [5]. Note that a different reference pressureis used in air acoustics.

9

In statistics a process such as a time series, is either stationary or non-stationary. Thecondition for stationary processes is that the probability distributions do not change withtime. Thus, it does not matter when the signal is recorded; its statistical properties willnot change. For example a linearly increasing signal is not stationary since the mean willchange with time. Sound might adhere to these two kinds of properties.Stationary signals are divided into two sub-groups, deterministic and random signals.At every moment in time the value of a deterministic signal can be predicted, whilefor random signals only statistical values such as the average is known. Non-stationarysignals are divided into continuous and transient signals. It is difficult to give a definitionof transients. It is often regarded as a short pulse where short is related to physicalphenomena. In contrast, a continuous signal appears during longer time intervals, relativeto physical phenomena. A signal can be regarded as transient or continuous depending onthe situation. The classification of the signal lies in the eyes of the beholder. A commondefinition is that transient signals can be dealt with in full, while a continuous signal isanalyzed in sections [20]. In this study the ambient noise of the Baltic Sea is investigatedand the sound signal is regarded as random in character.In the paper of Wenz [26], the underwater acoustic sound sources where divided in threecategories. In the Bothnian Sea the following sources composes the ambient noise:

• Water motion;

– wind,

– waves,

– bubbles,

– precipitation.

• Man-made (anthropogenic);

– shipping,

– industrial activities.

• Marine life;

– animals.

5.2 Relevant sources of noise in the Baltic Sea

The ambient noise levels depend on wind speed in the frequencies between 200 – 10 000Hz. Wenz (1962) found that the noise level maximum is in the interval 400 - 800 Hz. Theambient noise below 200 Hz is independent of wind speed except in shallow areas. Thenoise level in shallow water for the same sea state as for deep oceans is about 5 dB higher[26]. Urick (1983) [22] showed on the other hand that at calm winds the ambient noiselevels in shallow water are often lower than in deep and the opposite relation pertainsat high wind speeds. Poikonen (2010) [17] showed that the wind speed had a stronginfluence on ambient noise in shallow seas, especially at lower frequencies. His researchwas made within the archipelago, at an isolated place with no ship or industrial noiseinfluences.

10

Sea States are often used to describe meteorological conditions. Sea states are scaled fromzero to eight and each sea state is defined by wind speed and significant wave height. Inthis thesis the sea states defined by the Swedish Defence Material Administration (FMV)[5] are employed, cf. Table 5.1.

Table 5.1: Definitions of the sea states according to the Swedish Navy [5].

Sea State Wind Speed [m/s] Significant Wave Height [m]

0 0.0-0.2 -0.5 0.3-1.5 -1 1.6-3.3 0.62 3.4-5.4 0.83 5.5-7.9 1.24 8.0-10.7 1.95 10.8-13.8 2.3

5+ 13.9-17.1 2.76 17.2-20.7 -

6+ 20.8-24.4 -7 24.5-28.4 -

7+ 28.5-32.6 -

Defining sea states based on wind speed is not entirely correct. Water motions generatedby wind may vary. The wind speed alone does not suffice to explain the sea state, alsowind direction and duration has to be taken into account [26]. To keep the researchmethods in this study as similar as possible to Wenz (1962) exclusively wind speed wasused to define the sea states.

Sairanen (2014) [21] presented results from measurements made in the Finnish Bay, atthe border of the archipelago. She showed that there is dependence between wind direc-tion and ambient noise levels, see Fig 5.1. She noted as well a clear correlation betweennoise and wind speed. Sairanens results are in line with the results presented by Wenz(1962), Urick (1983) and Poiokonen (2010). Her research was part of BIAS. The dataorigins from the same type of sensors as were used in this study.

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Figure 5.1: Averaged noise levels as a function of wind speed for Jussarö, in the FinnishBay, in January in 1/3 octave bands. Collected from Sairanen (2014) [21].

The result of Poikonen (2010) [17] showed that the ambient noise was dependent on windspeed. He introduced a wind-speed dependent factor. The result was based on measure-ments in shallow water in the Baltic Sea. The dependence factor was found to be 2.5 for100 Hz and decreasing to 2 for 500 Hz and higher. These values were higher than thosepresented by Wenz (1962).

Even in totally calm weather micro sized bubbles in water add up to bigger and biggerbubbles that ascends to the surface, oscillating and generating noise [26]. One of themain sources of natural ambient sound at low frequencies are bubbles created by break-ing waves, which in turn are produced by wind. Water droplets are also created fromspray and spin drift. Precipitation, such as hail, sleet or water droplets, generates soundwhen penetrating the water surface. A rule of thumb states that precipitation over 2.54mm/h (1 in/h) raises the ambient noise levels. At sea state 1 and below when breakingwaves are rare, precipitation contributes to the noise levels. For Sea States above 1, noconclusions have been made due to the complexity of separating wind generated sprayand spindrift from precipitation noise [26]. The measurement of Poikonen (2010) [17]was made at an inshore place with no influence of ships. His result therefore shows themeteorological influence on the ambient noise and a strong decrease in the ambient soundlevels below 500 Hz. Further, results were presented on correlation between the ambientnoise curve with the noise spectrum of oscillating bubble clouds created by waves. Thelow sound levels of ambient noise below 500 Hz were attributed to the lack of ship andindustrial induced noise.

Ice is known to generate noise in a broad frequency range [26]. Urick (1983) [22] showedthat an ice covered sea could work as a band-pass filter. High and low frequencies are

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filtered out. Sairanen (2014) [21] noted a decrease in sound pressure levels due to ice inthe Baltic Sea. Unfortunately, during 2014 the Bothnian Sea was never covered with iceand therefore it was not possible to investigate ice in this study.

In acoustics sound sources can be assumed small if the distance to the source is muchlarger than the extension of the source. This was the case in this study where the ma-jority of ships were at a distance 5 km or longer.

Anthropogenic sound can further be categorized as intentionally or unintentionally. Ship-ping noise is unintentionally sound generation, since the noise from shipping is a by-product of its activity. Seismic surveys, on the other hand, may be regarded as inten-tionally generated noise since the sound is used to map out the sediment structure.

Shipping noise is a combination of noise generated by cavitation, turbulence and vibra-tions from on-board machinery. Propulsion systems are the most dominant part. Thenoise generated from ships are classed as low (1-10 Hz), medium (10-500 Hz) and high(500 Hz - 20 kHz) frequencies [26]. The higher frequency components in shipping noiseare not affecting the ambient noise levels with any significance but due to the high atten-uation of high frequency sound in water, it is only affecting the close vicinity of the ship.

It is important to distinguish between nearby and distant shipping. Distant shippingnoise is the noise from ships at a distance where a single ship cannot be attributed to thesound levels. The opposite prevails for a nearby ship. The sound levels will be dominatedby the ship and the source can be identified. Thus, ship noise dominates at frequenciesbetween 20-500 Hz but it has an influence on the ambient noise in the interval 10-1000 Hz[26]. At a well-defined shipping lane this distance will show where the sound levels willbe range dependent. Outside this distance the sound will be determined by the distantshipping. The results by Sairanen (2014) [21] showed a clear “knee” where ships didn’tsignificantly influence the ambient noise, see Fig. 5.2.

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Figure 5.2: Averaged noise levels as a function of distance to ships during January in theFinnish Bay in 1/3 octave bands. Collected from Sairanen (2014) [21].

In Fig. 5.2 the “knee” is clearly visible at 4-5 km distance. At lower distances the soundpressure levels are increasing and at longer distances they are independent on the distanceto the ships.

Ships generate sound below 50 Hz that emanates from the propeller and the hull. Thesound levels have be shown to be dependent on the depth of the two sources, i.e. thedraught of the ship. Due to boundary conditions the sources in the water will createimage sources at the water surface, which will add the noise level up, also known as theLloyd-mirror effect [26].

Industrial generated noises such as pile-driving, hammering and other intermittent ac-tivities may be regarded as ambient noise and not, depending on the purpose of themeasurements. Offshore wind farm generated noise may however be regarded as ambientnoise since it is always present.

Animals are known to produce sound to communicate, orient and to hunt. The soundshave many different characters. The cod is for example known to produce grunts, espe-cially when spawning. Other animals such as whales produce a repertoire of sounds, bothshort pulses and longer continuous songs. Biological noise varies with time, location andfrequency and is an important part of the ambient noise [26].

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5.2.1 Sound propagation, refraction and absorption

Sound propagates outwards from a sound source as a spherical wave. Since the Baltic Seais shallow the waves will interact with both the surface and the seabed. The propagationwill be altered and the spherical symmetry will be lost. Under certain circumstancesthe spreading will become cylindrical. Measurement of attenuation shows that often thespreading falls between the spherical and cylindrical geometry [5].

Sound waves produce relative motion between water particles. The kinetic energy istransformed to heat due to friction force. This transformation of energy is called absorp-tion and is especially relevant for high frequencies and long propagation distances [5].

Urick (1983) [22] gave three explanations for the absorption. First, magnesium sulfate inthe water absorbs the kinetic energy of sound. Second, the shear viscosity and third thevolume viscosity contributed to absorption. Urick concluded that absorption increaseswith increasing salinity and frequency. He also introduced the absorption depth-factorthat decreases the absorption with 2 percent with every 300 m of depth. The end product,the absorption coefficient, is approximately 0.02 dB/km at 500 Hz and 1 dB/km at 10kHz for the Baltic Sea with salinity of 7 PSU, practical salinity unit, and a temperatureof 5 ◦C [5]. Even at distances of 100 km the absorption is less than the errors introducedby the methodology. The absorption in shallow seas is rather dependent of the bottomcharacteristics. Sea floors such as clay increase the absorption of sound massively [22].The low salinity, the short propagation distances of the Baltic Sea and the dominatingabsorption by the sediment makes the Baltic Sea soundscpae environment complex.

The phenomena investigated in this study are related to frequencies between 10 Hz and10 kHz, shallow depth and low salinity. The absorption in the Baltic Sea is dominatedby the properties of the sediment; thus, absorption in water can be neglected.

As presented in chapter 5.1, sound speed increases with increasing salinity, pressure andtemperature. The temperature has the largest impact on sound speed. During summerseason the surface layer in the Baltic Sea is heated by the influx of the sun, which resultsin a temperature rise at the surface, the so called thermocline is developed, often locatedat 15- 20 m depth. At larger depth a halocline (change in salinity) is separating the bot-tom layer from the other water volume all year around [5]. This results in a high soundspeed at the surface and at the bottom layers and lower sound speed in between the twolayers. Sound waves that propagate between two layers will refract towards the centerof the water column and thus be trapped therein. This phenomenon is named a soundchannel and the most known is the Sound Fixing and Ranging (SOFAR) channel, foundat a depth about 1000 m, in the deep oceans [22]. Sound might travel long distances inthese channels. In a SOFAR channel ship sounds can propagate up to a few 1000 km[26]. Sound channels acts as a low-pass filters but too low frequencies components ofsounds are cancelled out [22]. The top boundary tends to keep air borne sounds fromentering the channel. Sound channels are well known by both Navies and whales, theformer using it for surveillance and the later for communicating long distances. TheBaltic Sea is somewhat more complex and has a seasonal component that has to be takeninto account. Under certain circumstances sound channels exists, especially during thesummer seasons [5].

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5.3 Ambient noise

After the second World War the field of underwater theatre started to be systematicallyexploited. The Physics of sound in the sea was published the same year as Knudsen etal. (1945) investigated the noise dependency of the sea states and Wenz (1962) updatedtheir results almost 20 years later.

The curves of Wenz are spectral presentations based on average sound pressure levelsproduced by a number of independent noise sources. Wenz based his research on theresults by Knudsen and he described Knudsens result as Knudsen curves. Today, theresult are known as Wenz curves and is illustrated in Fig. 2.1.

A less used graph presented by Wenz describe average noise levels for deep and shallowwater at no traffic and average traffic situations. This graph shows clearly that shippingnoise is dominant in 20-500 Hz frequency band as presented in chapter 5.1. It is includedin Fig. 2.1. The traffic noise fields (pale blue and blue) are corresponding to these results.That information is also of importance for navies since the hydro acoustic profile changeswith a war scenario, and it is above all the shipping that changes.

5.3.1 Rule of fives

In the frequency interval of 500 Hz- 5 kHz Wenz (1962) formulated an empirical formulathat described the behaviour of the ambient noise. The rule was formulated based on re-search done in deep water without any information of wind direction and duration or thebottom characteristics [26]. The estimate was named rule of fives and has been appliedon others research with good accuracy. The first rule says that between 500-5000 Hz theambient sea-noise spectrum levels decrease 5 dB per octave with increasing frequency.The second rule says that between 500- 5000 Hz the ambient sea- noise spectrum levelsincreases 5 dB with each doubling of wind speed in the range 2.5- 40 knots.

5.3.2 Acoustics of the Baltic Sea

The Baltic Sea is located in Northern Europe. Border states are Sweden, Denmark,Finland, Russia, Poland, Lithuania, Estonia, Latvia and Germany. The Baltic Sea canbe divided into seven sub regions, which are illustrated in Fig. 5.3 [24]. This study isperformed in the Bothian Sea and many Baltic Sea characteristics are the same for theBothnian Sea.

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Figure 5.3: The map illustrates the division of the Baltic Sea in sub regions. It is collectedfrom HELCOM [9].

The Baltic Sea is a small isolated sea only connected with the North Sea through Baeltand Öresund. Baelt and Öresund are both narrow sounds which restricts the exchangeof water between the Baltic Sea and the North Sea. Althought the Baltic Sea has a richinflow of fresh water from rivers, lakes and precipitation. This in combination with thelow inflow of ocean water makes the salinity of the Baltic Sea low compared to the oceans.The water in the Baltic Sea is not salt, but brackish. The salinity varies in the Baltic Seaboth with location and time of year. Bothnian Sea is located north in the Baltic Sea andis much less saline than the southern parts. For comparison, the salinity in Bothnian Seais about 5 PSU and 8 PSU at Bornholm Deep at the same time [24].

The Baltic Sea is also shallow compared to the oceans with a maximum depth of about459 m at Landsortsdjupet. The mean depth is about 54 m, which is less than what Wenzreferred to as shallow. Also the coastline with the many islands and the stratificationpatterns of the water makes the sea unique [18].

The bottom characteristic varies through the Baltic Sea. In the Bothnian Sea, pertinentto this study the seabed consists of strata formed at the quaternary period. Below a 100m thick layer from Ordovician is found and below that a layer formed at the Cambrianperiod. A bit east of the study area the bottom consists mostly of Jotninan sandstone [24].

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The warming of the Baltic Sea is often rapid and a thermocline is created at a depth of 15-20 m. At the autumn, the surface temperature drops and the influence of the thermoclineweakens. Together with the commonly recurring autumn storms the thermocline disap-pears and the top layer of the Baltic Sea gets well mixed. This is essential for oxygenationof the water. During winter the thermocline is absent. The mixing can be expected tobe effective down to a depth of 70 m. At 60 - 70 m a halocline is present dividing thetop layers and the saline bottom layer [24]. The halocline and the thermocline make upthe two boundaries that constitute the sound channel in the Baltic Sea. Their presencewill change the wave propagation of low frequency sound and has to be taken into account.

The oceanographically characteristics of the Baltic Sea environment and the fact that theBaltic Sea is one of the most densely trafficked seas [9], makes the ambient noise situationunique. The noise levels are expected to differ compared to the large oceans [18].

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6 Theory Part II: Signal processingand analysing

Recorded information from physical properties often results in complex signals in thepresence of unknown noise. To understand the signals, conversion into digital form fol-lowed by analysis using various algorithms called digital signal processing (DSP) and timeseries analysis, is required. It causes a need of careful planning and a conceived strategy.To realize a complete analysis all prior knowledge of recorded physical and noise prop-erties is vital [1]. In this study the recorded signals and noise are an electrical quantitydelivered by transducers that transforms the acoustical pressure to electrical energy. Theelectrical quantity is in a linear relation with the acoustical pressure and therefore soundsare also referred to as both signals and noise in this thesis.

In real life the recording of a significant amount of acoustic sounds require large datastorage space. The data is often unmanageable to handle and interpret. To extract fea-tures that describe the data, signal processing methods were applied. Signal processingmethods were also applied to make quality check of the data. To describe the signal ithas to be described in both time and frequency domain.

6.1 Stationarity

The knowledge of statistical properties of recorded data is fundamental in time seriesanalysis. An important statistical property is stationarity of the probability distribution.Stationarity answer the question of how much is the statistical underlying mechanism ex-pected to temporal vary. Stationarity is fulfilled in systems that achieved a steady-state[19]. Commonly used statistical methods such as correlation and Fourier transform areonly valid if the assumption of stationarity holds true within the estimation window [1].Thus, it is important to test whether the signal is stationary or not and to what degree.

From a philosophical point of view a signal is either stationary or non-stationary. Therequirement is that the statistical estimate of the stationary process does not change overtime [19]. This can be illustrated with an example: A time series is divided in two datasets x1, ..., xn and x1+t, ..., xn+t. If the probability density function of the two sets areequal, then the sets are strict stationary, if not, they are non-stationary [12].

In an practical perspective stationarity can be classed as strict stationarity, n:th orderstationarity or wide-sense stationarity. Strict stationarity means that the joint probabil-ity does not change over time, and neither does the mean or variance. Not all random

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processes and real recorded data fulfill this requirement but shows stationary behavior.

The weakest form of stationarity is the wide-sense stationarity which is also called weakstationarity. Wide-sense stationarity means that the mean of the signal (first order statis-tical moment) is constant and the covariance is only dependent on the time lag (secondorder statistical moment), not time itself [12]. A stronger form of stationarity is n:thorder stationarity and means that all statistical moments up to order n are stationary.

In spectral analysis, second order forms a break point between strict and n:th order sta-tionarity, which implies that the stationarity of a signal does not contribute significantlymore to the concept of stationarity with a higher order. Thus, the stationarity of secondorder is comparable with strict stationary and higher orders are not further investigatedin this study [19].

Temporal stationarity is a function of time and amount of data samples. However, thecontribution of the sound sources changes both temporally and spatially which affectsthe stationarity of the signal. Also the amount of samples affects the validity and the sig-nificance of stationarity. Levonen (2005) [11] concluded that a time window of 1.5 s wasappropriate to use in underwater acoustic ambient noise analysis. Choosing an appropri-ate size of time window is important since if poorly selected, the time series may deviateto much from the assumed stationarity and the results gets invalid [19]. Levonen (2006)[12] also presented that the ambient acoustic noise in a shallow bay of the Baltic Sea wasstationary for 0.4 s and with decreasing depth the stationarity decreased. Levonen (2003)[10] also showed that stationarity of ambient noise may have a dependency on time-of-day.

6.2 Outliers

An outlier is a data observation or value that lies at an abnormal distance from the meanof other values in a data set. The recorded data in BIAS consists of samples within acertain amplitude range and normally less than 1% of all values are exceeding this range.These extreme values are treated here as outliers.

Outliers are known, or strongly suspected, to be due to effects that are not from a physi-cal underwater acoustical measured quantity [4]. One such effect is electronic noise in therecording system. When dealing with signal processing, measures for determine whetherthe signal contain outliers and to what extent is needed. Due to large data sets automaticprocessing methods for outlier removal is appropriate.

Great care has to be taken when defining outliers. However, the outliers might be a resultof the experiment and should therefore be included in the data for signal processing. Itcould also be ”a result of gross deviation from the prescribed experimental procedure” asGrubbs (1969) [8] stated it. If the outlier is a bi-product that has nothing to do with theassumed measured signal the outlier should be removed prior the estimation of underwa-ter acoustical measures [8].

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Some recorded data may contain many multiple outliers. In such case the cause of theoutliers has to be identified. There is a risk that identified outliers belong to the actualsignal, and if so, the sorting of outliers has to be done manually.

6.3 Correlation

Correlation functions are used in statistics and signal processing to determine relation-ships between two different sets of measured data. However, some care has to be taken.Correlation estimates should be based on a physical assumption that is a known orhypothesized relation. Two time series with no physical relation will often produce acorrelation that could be used to interpret the relation. Two functions of correlation aremainly used, auto-correlation and cross-correlation. The former measure how well futurevalues can be predicted using older data. The latter is often used to reveal the similarityof two signals as a function of the time delay between them. Both signals and noise areoften analyzed with cross-correlation. Auto-correlation may be used to find specific tonesin the noise, which is relevant to the use of sonar systems [1].

The correlation is estimated as the integral of the product of the two signals. Two identicalsignals generate a value of one at zero delay, and opposite totally different signals areun-correlated and generates a value of 0 [1]. Cross correlation is further explained withan example of two identical signals where one is time delayed with 20 samples displayedin Fig. 6.1.

Figure 6.1: Example of cross-correlation between two signals of random character. Thered line indicates zero time lag and the blue line indicates the correlation at each timelag.

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The location of the peak indicates that the two signals are well correlated at a delay of20 samples. By changing the order of the signals in the cross-correlation function, thepeak would shift to the negative side of zero on the x-axis [20]. The order is importantdue to the causality of many phenomena.

6.4 Spectral analysis

For an extended analysis of noise and signals, estimation of the spectral content is re-quired. Recorded time series are presenting an amplitude, in this case the acoustic pres-sure, at every sampled time stamp. The frequency domain representation of the datais independent of time but returns the amplitude and phase for each frequency. Visualinspection of a data set in time domain tells when different pressure fluctuations appearwhile a spectral analysis returns at what frequency it does. The energy for a signal isconserved i.e. the energy is equal in both time and frequency domain in accordance withParseval’s identity [1]. Spectral analysis is a standard method to inspect both the noiseand signal contents in recorded data. For a broader understanding of signals and noiseboth temporal and spectral analysis are required. It need to be emphasized that spectralanalysis is only a preliminary data analysis tool. Spectral estimates should not be usedto answer specific questions about data such as whether a sonar pulse is present, but onlysuggest possible hypotheses. Detection is a statistical tool and should not be mixed withspectral analysis.

6.5 Fourier analysis

In spectral analysis the use of the Fourier transform is essential. The analysis is based onthat an arbitrary periodic signal could be written as a sum of sine and cosine functionsto be Fourier series. Jean- Baptiste Fourier formulated this early in the 19th century. Histheories became well used and further developed. Today non-periodic signals may alsobe expressed with a sum of sine and cosine elements by using Fourier transform [20].

6.6 Power Spectral Density

A convenient way of presenting signal and/or noise power is to estimate the power as afunction of frequency by use of Power Spectral Density (PSD) displays. The PSD displaymay look different depending on type of underlying signals in the recorded time series,i.e. short spikes are displayed as broadband components. It is established when analysingand displaying stationary continuous signals to use the PSD as the amplitude squared asa function of frequency, e.g. V 2/Hz [20].

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6.7 Bandwidth

The bandwidth of recorded data is the difference between the uppermost (highest) andlowest frequency component of a signal, i.e. if a signal consists of frequency components10 – 50 Hz the signal bandwidth is 40 Hz [25].

Octave bands are common in acoustics. The mid frequency in each octave band is thedoubling of the prior octave band. Historically, sound pressure levels are usually dividedinto 1/3 octave bands. The reason behind this is that a 1/3 octave band represents thecritical bandwidth of a human ear. The 1/3 octave bands are defined in Eq. 6.1 wherethe mid (centre) frequency fm gives the name to the 1/3 octave band [25].

fu,l = fm2±(1/6) (6.1)

The bandwidth off each 1/3 octave band increases with increased frequency. The ratiobetween the band frequency and the bandwidth is constant. Consequently, the 1/3 octaveband is suited to display in a logarithmic scale [20].

The ambient noise is in most cases described in 1 Hz bands but in some cases also in 1/3octave bands. In a technical point of view 1 Hz bands are easier in many applications tointerpret but in sonar applications 1/3 octave band is sometimes handy.

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7 Theory Part III: Passive sonar

Water is a most effective medium for transport of sound. A ship can be detected longbefore it is visually observed at the surface. Individual ships can be heard at 1000 kmdistance provided that a sound channel exists [22]. Navies utilized this fact and have beendeveloping different means to “listen” to underwater sound. The most common sensorfor both listening and transmitting sound is the sonar. This word is well known but fewknow that it is an abbreviation for Sound, Navigation and Ranging (SONAR). There aretwo types of sonars, passive and active. Passive sonars are dealt with in this study. Apassive sonar is also called listening sonar since it detects sound radiated from the target(source). Active sonars generate sound-pulses that travels through the sea hitting thetarget and returns to the sonar as an echo, cf. radar. Active sonars are used by navalwar ships to locate submarines while submarines use passive sonars to locate other ships[22].

Active underwater echo ranging was developed before the First World War to detecticebergs at far distances. At the outbreak of the First World War the interest of sonarsin military application amplified. Both active and passive sonars were developed duringthe war. A passive listening device called the Eel, consisted of twelve air tubes mountedalong a neutral buoyant line array towed by ships, was used to locate submarines [22].Using cross bearings with a group of 2-3 Eels it was possible to obtain a “fix” on a soundsource. Active sonars were employed in the hunt of German submarines but withoutsuccess. The breakthrough of active sonars had to wait till the Second World War [22].

After The First World War German papers on underwater acoustics became public andresults were presented on the behaviour of sound propagation due to salinity and tem-perature gradients. The paper was far ahead in time and was unrecognised for 60 years[22].

During the Second World War, the United States developed a simple and cheap sonarsystem that was mass-produced. The sonar system was placed on-board many surfaceships of the United States and played an important role in the victory of the AtlanticBattle [22].

The development of advanced sonar systems has been followed by more silent submarines.The development during the Cold War was no exception. The active sonars became bet-ter and cheaper and eventually they found their way to the commercial market. Activesonars became standard on merchant and fishing ships, both for depth control and fishlocation. Today it is also standard system on-board pleasure boats to measure the depth[22].

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7.1 Purpose and use of passive sonar

The purpose of using passive sonars is to locate ships without revealing the location ofthe sonar carrier. Presently, submarines are equipped with a few different passive sonarsplaced at different locations on the hull. Buoys can also be equipped with passive sonars.These can be dropped into the ocean from ships and aircrafts. Buoys have a limited ca-pacity since the battery charge is limiting the operation time. An expensive alternativeis to place fixed passive sonar systems in the oceans that constantly survey the watervolume. A surface ship towing a long passive sonar array keeps the submarine uncertainif it is hunted or not. To effectively detect submarines at low frequencies these arrays hasto be many hundred meters long [5].

7.2 Passive sonar equation

In this chapter an introduction to passive sonars is presented. The theory and resultsherein are all gathered from open sources. No classified information is presented in thisthesis. A simple passive sonar model (SOFAR) was employed and used in the estimatesof detection ranges.

The sonar equation for passive sonars is a starting point for estimating detection rangesof a ship. The sonar equation is presented in Eq. 7.1 and consists of five terms which arepresented in Table 7.1.

The sonar equation is based on the assumption that wave propagation is exponential.Thus, it is possible to relate the different terms as a sum of logarithmic values. Thisrelation is automatically fulfilled for sound pressure values that are defined as logarithmof a relative pressure (in dB relative to 1 μPa). The sonar equation is defined as follows

TL = SL−NL− (−DI)−DT (7.1)

where the variables are defined in Table 7.1.

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Table 7.1: The parameters of the passive sonar equation and brief explanations of them.All parameters are measured in dB. The table is a recreation of table 2.1 in Urick (1983)[22].

Term Equation Explanations

Transmission loss TL = 10 log10IsIt

Is = Signal intensity at 1 mIt = Signal intensity at target

Source level SL = 10 log10IkIref

Ik = Source intensity at 1 mIref = Reference signal intensity

Noise level NL = 10 log10INIref

IN = Noise intensity*Iref = Reference signal intensity

Directivity index DI = 10 log10PNekvPNS

PNekv = Power generated by ndh*PNS = Actual Power generated

Detection threshold DT = 10 log10PRPN0

PR = Signal power neededPN0 = Noise power

* Non-directional hydrophone.

TL is the difference of the source intensity and the intensity at a range r. It depends ongeometrical spreading of sound, anomalies in water and the current absorption.

SL is the intensity level 1 m from the source measured in 1 Hz band compared to thereference intensity. The reference intensity is calculated for a signal consisting of a planewave with rms 1 μPa.

NL is the unwished surrounding noise level. In this study NL is the ambient noise mea-sured in 1 Hz -bands. It changes with sea states.

To reduce the influence of noise, multiple hydrophones can be employed mounted in anarray configuration. By keeping the main axes of the array orthogonal to the target di-rection the ambient noise is reduced relative to the source level. The source to noise ratiois improved and the source can be detected at longer ranges [5]. With an array length of25 m, 5 dB directivity gain (DI) can be achieved at a frequency of 100 Hz.

Detection Threshold (DT) is the signal to noise ratio needed to detect a target with acertain confidence. It is set by the operator. With a decrease of DT an increase of falsealarms will follow and with increased value of DT an increased probability to miss thetarget is followed [5]. It is thus a trade off. Experience from operations shows that a DTof about 9 dB is a good choice. In this study broadband detection was employed sinceno specific tone was assumed for the target. If the target is producing a specific tonethat is known by the operator, it is optimal to apply a sharp filter that detects changesin frequency amplitude (narrow band detection) [5].

Both noise level (NL) and transmission loss (TL) are dependent on weather, location andhydrography, which in turn are dependent on time of year. The environmental param-eters have to be deduced by in situ measurements or calculated for each position andsituation. The transmission loss is even more difficult to determine than the ambient

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noise since it depends on spreading, sediments, anomalies and the hydrography. For ex-ample if the hydrophone is placed within a sound channel and the target is outside theTL will be higher than if the target also was located in the channel. To investigate thelocal transmission loss a numerical estimate was calculated using a LOFAR sonar. Thefrequency of the source was 100 Hz and the water depth was 70 m. The sonar was locatedat 63 m depth. The sound channel was present in the middle of the water column. Theresults are shown in Fig. 7.1. The sound is trapped in the middle of the water column.

Figure 7.1: Transmission loss in the Bothnian Sea in January. The illustration indicatesthe transmission loss for differrent source placement in the xy plane. The colour-barindicates the values of each colour. The values are in dB re 1 μPa. Result computed withsoftware SonaCalc.

This result visualizes the behaviour of sound in water in the Bothnian Sea. Accordingto this result it is most favourable for a submarine to stay in the pale blue areas, sincethat is where the transmission loss is the greatest. In this case that would be almostat the surface and at the bottom. a depth of 34 m would should be avoided since thetransmission loss is less strong at that level to a distance of about 10 km. These resultsare important for the submarine operator.

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8 Method

The aim of this study was to establish the sound levels of the ambient noise and investigatethe detection ranges for a basic sonar surveillance system. To achieve these aims a numberof data sets were used. The recorded sound data as well as wind, wave, hydrography andAIS data were used. For the estimation of detection ranges the sonar equation was usedwhere background levels were taken from the own produced results.

8.1 Data collection

The sound data used in this study was measured 2014 by the BIAS project. Meteoro-logical and ocean data were produced by SMHI and pre-processed by AquaBiota WaterResearch. The AIS data was supplied by HELCOM.

8.1.1 Noise recordings

The hydrophone used was of the type SM2M logger from Wildlife Acoustics and wasplaced at N 61.75738◦, E 19.31642◦. It was anchored at the sea bottom at 63 m depth.The deployment position was chosen to be outside the shipping lane. The sampling fre-quency was 32 kHz. The rig was deployed in November 2013. The recording startedat the 1st of January and ended 31st of December. The recording time was limited tothree months where after the memory was full the sensor had to be replaced. This pro-cedure was repeated throughout the 2014. The recording length was 23 minutes everyhour every day for a year. The main component of the rig was the autonomous recorderthat contained a hydrophone, amplifier, filter unit, A/D converter and a storing unit. Asketch of the rig is showed in Fig. 8.1 [23].

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Figure 8.1: Sketch of the BIAS standard rigs. The rig to the left uses the Loggerheadsensor and the rig to the right the Wild Life Acoustics. 1 hydrophone, 2 extra buoyancy,3 & 7 autonomous loggers, 4 acoustic releasers, 5 anchors, 6 buoys [23].

Calibration of the system was completed both before the first and after the last deploy-ment. The aim of the calibration was twofold; first to control the quality of raw data andsecond, more important, to establish the sensor sensitivity, that is the relation betweenthe pressure variations and the recorded data. The calibration gave the sensitivity inbins/μPa. The reason for this “odd” entity is that the recorded sound was stored in awav-file, which scales data in 216 bins. To convert the bins to pressure the scaling factorwas needed [23].

The hydrophone is connected to two separate channels. Thus, here was the option toamplify one of the internal amplifiers of the autonomous sensor. This has to be donewith care. A too high gain results in clipping when strong sound sources pass, which willhave an uncorrectable effect on the sound average estimate. On the contrary, if a too lowgain is set, the signal-to-noise ratio might be too low [23]. The first channel was set tozero amplification and the second to 12 dB.

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8.1.2 Meteorological data

The meteorological data was pre-processed by AquaBiota Water Research but was origi-nally produced by SMHI. The data used was significant wave height, ice thickness, salinityand temperature of the water for every sixth hour as well as wind speed data for everyhour. The meteorological data were model based estimates for a position close to theactual hydrophone position.

8.1.3 AIS data

AIS, Automatic Identification System, is a world-wide system that makes it possible toidentify and track ships from other units or land-based stations. The purpose of the sys-tem is to increase the safety at sea. The Swedish Maritime Administration is responsiblefor AIS data distributed within Sweden. HELCOM is the data holder for all AIS datato be used in BIAS. The BIAS project has allowance to use the data for 2014. The AISdata was stored in a text-file format containing date, time, speed, position, dimensionsof ship, draught, type of ship and cargo.

8.2 Signal processing

There is a number of processing steps that can be applied to a time series. The differentoptions at hand will affect the estimated properties. How to choose and implement pro-cessing methods lies in the hands of the processor. Every set of data can be measured,processed and analysed in multiple ways. There are no rules to adhere to. The methodsemployed are often based on experience from earlier studies.

8.2.1 Pre-processing

Pre-processing was performed to prepare data before estimating the statistical proper-ties. It was also done to make sure there were no artefacts affecting the estimates. Thepurpose of the recordings was to measure the ambient noise. Some signal content shouldnot be regarded as ambient noise signals [16]. Electronic spikes are an example of thatand should be removed before starting to estimate the properties of the time series. Asecond example is the ambient noise recorded during deployment of the rig, even if it canbe regarded as ambient noise it was not in this study, and it was removed.

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The processing started with grouping the data into monthly periods. In the first pre-processing test the number of files was counted to make sure no recordings were missing.In the second test the length of each file was controlled, to make sure that the sensorhad been working properly. Files with substantial difference in length were removed fromthe data set. Each noise recording was also controlled for non-numerical values such asNaNs (not-a-number) and infs (too high value for a numerical representation). All non-numerical values were excluded from the set [16]. If the sound was too loud the recordeddata became clipped when the amplitude of the signal was higher than the maximumallowed value of the Analog-to-Digital converter. Clipping was checked for both positiveand negative values.

8.2.2 Grubbs’ test

Self-noise is an unwanted product of the instrument and has to be dealt with. The firststep in this process was to optically inspect the time series for anomalies. When anoma-lies were found they were inspected both by plotting them and by listening to the sound.The different types of anomalies were identified and an algorithm was designed that au-tomatically identified and removed the anomalies. A commonly occurring anomaly werespikes. These were small groups of outliers much stronger than the surrounding signal.

The algorithm that was developed to remove spikes was based on Grubbs’ test [8]. Thisis a test which results in identification of outliers in a time series. The significance levelof 5% was used. The algorithm was built up in seven consecutive steps:

1. The data set was divided in windows of maximum 1000 samples.

2. The data within each window was sorted in ascending order; x1, x2, x3, . . . , xn.

3. The ratio of w/s was calculated, wheres = std(x1, x2, x3, . . . , xn.) andw = xn − x1.

4. The value of w/s was compared with the value 7.33 collected from table 3 in Pearson(1964) [15]. If it didn’t exceed, the process started over with a new time window instep 1, if it did, the process continued with step 5.

5. The value T1 was compared to pV, where T1 was defined according to Eq. 8.1 andpV according to Eq. 8.2. T1 was calculated as

T1 =±[xmean − xn]

s(8.1)

if the value exceeded pV, calculated as

pV = Nstdn− 1√

n

√t2

(n− 2 + t2), (8.2)

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it was classed as an outlier and the second largest value, xn−1, in the set was tested.If not, the process restarted with the next time window in step 1. pV was calculatedaccording to Dan (2013) [4], where n is the number of samples (in this case 1000),t is a constant value set to 1.645 and Nstd is the number of standard deviations[4]. A high value of Nstd results in high certainty that all outliers identified wereoutliers but with a risk that some outliers were not found. A low value results indata identified as outliers even if they were not. In this case Nstd was set to 5.

6. All xi corresponding to a too high T1 were classed as spikes and were removed fromthe data set and replaced, i.e. a piece of the data set without spikes was cut-outand used to replace spikes. Many different methods can be applied to replace data.After a few tests this method was selected due to good stationarity result.

7. The procedure was repeated with the next time window.

8.2.3 Kolmogorov-Smirnov two sample test of stationarity

In signal processing strict stationary signals are rare. The Kologorov- Smirnow two sam-ple test, also known as the KS-test, are often used to test whether a signal is stationaryor not [11].

The test is based on distribution functions of the two sets. The cumulative distributionfunction (CDF) is calculated for each set. They are compared and if there is a non-significant difference between the CDFs, the sets are said to be strict stationary with acertain confidence. This was calculated with Eq. 8.3 [3],

TKS = sup|CDF1 − CDF2|, (8.3)

where 1 and 2 denotes the different sets tested. Then a null- hypothesis as

H0 = ”the two sets are stationary”

was stated. H0 should be rejected with the significance level α, if TKS was greater than(1-α), [3] [12]. The level of significance may be decided by the user and is often set to amaximum level of 5 %, which was used in this study. A 1.5 second window was used.

8.2.4 Averaging

Noise was recorded with a sampling frequency of 32 kHz. The file corresponded to 23minutes recording. The file size was 175 MB and it was found to be difficult to handlemore than a few files simultaneously. To reduce the computational time the data wasdecimated by calculating averages over a pre-specified time length. Two different meth-ods were used for averaging; twenty seconds means (20 s means) and PSD averaging.

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A time length of 20 s reduced the data size with a factor of 640000. The length waschosen for two reasons, first these data can be published without breaching security asspecified by the Baltic Sea Navies, and secondly this time length will have a small effecton ship passage.

An alternative is to calculate the median. The advantage is that median is not affectedby spikes at the same levels as the mean. It has also been shown that median will filterout nearby events, provided that they are short, and provide an estimate on the ambientnoise, especially the sound produced by wind and waves. The problem with median isthat it requires more space and power of the computer than calculating the mean. Spikeswill influence the mean especially for sound that are spanning over a large scale but byusing Grubbs’ test and removing identified spikes improved this weakness of the mean.To verify the validity of the 20 s means, comparisons were made with 1 s means andmedian for January 2014 in Bothnian Sea.

Time averages were made over the frequency band 10 Hz – 10 kHz and for 20 secondswhich is useful for many time series based analysis. The information of the frequency con-tent disappears though, and for some analysis frequency information is required. There-fore the power spectral density, PSD, was calculated for each second in every recordingeach whole hour. These PSDs were then averaged over each frequency resulting in anaveraged PSD for each hour. By doing this, the frequency information was conserved andthe amount of data reduced.

8.3 Handling of different data sets

As was pointed out by Wenz the ambient sound is generated by a number of sources.Fortunately many of them are found in different frequency ranges. By relating meteo-rological, oceanographical and ship data to noise data it was possible to estimate theirinfluence and hence identify the source strength.

8.3.1 Combining ambient noise and meteorological data

Poikonen (2012) [18] showed that the major parameter is the wave height. Large waveswill brake and produce bubbles that produces sound. Thus, higher waves will inducemore sound. The waves are in turn dependent on wind. As a first approximation it canbe assumed that strong wind will produce large waves. Other published results in thisfield also shows that wind place a role in generating sound. Traditionally at sea, wind isgrouped in sea states, based on experiences made in the open oceans. The meteorologicaldata were produced by SMHI and based on the HIRLAM model. The wave data wascalculated based on the wind data. Clearly the different sets of data were connected.

To analyse wind dependency on the ambient noise, both time series and frequency anal-ysis methods were applied. For time series analysis five minutes represented by fifteen 20s means for every hour were averaged and combined with the meteorological data. Thefrequency analysis was performed by combining the meteorological data for every hour

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together with a five minutes PSD average for corresponding hour. The wind data wasonly given as a single value per hour and wave data even less often, the weather maydeviate within a 23 minutes time frame and therefore was only a five minutes mean used.

8.3.2 Combining ambient noise and shipping data

Since the advent of AIS it has been possible to do statistical analysis of ship traffic. Inthis study only ships in the range of 20 km were included. Ships passing outside therange are assumed not to contribute to the noise [21].

Ships continuously transmits AIS data. The time stamps of each ship transmission weregrouped together and sorted in ascending order. The data showed that the AIS datasample varied from every 30th second to every 2nd minute and in some cases even lessoften. The sorted data was also used to increase the number of positions by employinginterpolation. This scheme resulted in a position series that were synchronized with the20 s means sound data. 20 s means for 1/3 octave band were also calculated for analysingthe influence of shipping noise at different frequencies.

Results from the work by Sairanen (2014) [21] indicate that ships within 5 km radius areregarded as nearby shipping. It should be noted that ships have different source levels.A ship at 9 km may produce the same sound pressure levels as a ship at 4.5 km distanceprovided that the source level is 6 dB higher. An investigation of the most commondistance to the hydrophone for the passing ships in January showed a distance over 15km. To minimize the risk of classing a distant ship as nearby and without any risk ofclassing too many ships as nearby all ships within 10 km radius were regarded in thecalculations as nearby.

Analysing ship induced noise is complicated and to make sure noise levels and AIS datawas combined in a correct way for analyses a few steps were performed:

1. Every ship passage was combined with a 20 s mean value of noise level.

2. If two or more ships were passing the hydrophone at the same time, they were allcombined with the same noise level. If one ship passed within 10 km radius to thehydrophone and one at 20 km distance both of them got combined with the samenoise level. All distant ships passed the hydrophone simultaneously as a nearbyship, were therefore removed from data.

3. If two or more ships passed the hydrophone within the nearby shipping border (10km) all data was removed for that time period, both nearby and distant shipping.

4. All other combined noise levels and distances to ships were saved together withother ship relevant specific. These results were then saved in a table. A piece ofthe table is presented in Table 8.1.

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Table 8.1: Example of the saved AIS interpolated data.

Distance [km] SPL [dB re 1 μPa] Time [seconds] MMSI5 124 280 1115748

5.5 116 300 1115748

6 112 320 1115748

This table was then used in analyses.

8.4 Method of determining ambient noise and its

dependencies

The method of determining the different noise dependencies was initially performed formeteorological events and later for human activity, in this case shipping. The methodsare described with a starting point in recording methodology and ending with the finalanalysis of the recorded data.

8.4.1 Transformation from time to frequency plane

The raw data was divided into one second time segments, which were transformed to thefrequency domain by using the Discrete Fourier Transform, DFT. An advantage withusing 1-second time segment is that the bandwidth of the PSD, is 1 Hz. The PSD andthe amplitude will thus be identical [16]. There are many different methods to use whenanalysing noise and generating PSDs. There are no clear rules to adhere here and aftera few tests with different methods, periodogram was selected since it is trustful whenanalysing huge sets of noise. The PSD had in the end the unit (dB re 1 μPa)/ Hz. Testswere done with and without using tapering windows (Hanning and flat-top). The im-provement was found to be negligible, thus, all spectra were calculated without using anytapering.

8.4.2 Correlation of wind, waves and ambient noise

Correlation is a powerful tool in research, since it can be used to find relationships be-tween different sets of data that not obvious is related. Care has to be taken though, sincecorrelation may be found between any sets of data due to coincidence and not that theyare related. To make sure correlation can be used, a physical relationship between twoevents should be supported by a hypothesis or fact of a physical relation. In this studyrelationship between meteorological events and noise is well founded in earlier presentedpapers. Three sets of data were analysed with cross correlation:

• The noise dependent of the significant wave height.

• The noise dependent of the wind speed.

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• The wave height dependent of the wind speed.

Correlation calculations were performed with 20 s means since the frequency content wasirrelevant in this case. A five minute period, i.e. fifteen 20 s means were averaged andused in the correlation calculation.

8.4.3 Wenz curves based on wind speed

One of the goals in this study was to generate Wenz curves. This was performed in 5 steps:

1. PSDs were calculated in the band 10 Hz – 10 kHz for every noise recording inJanuary, February, March, May and June. Each hour contained 82800 PSD (1 PSDper second). These were averaged to one PSD/h. Since sea states are determinedby wind speed ranges it is assumed that the sea state is constant for 23 minutes.

2. January- March PSDs were combined together as winter and May- June PSDs com-bined together as summer. April consisted of both summer and winter hydrographiccharacteristics and not included in any. July data were biased and not used.

3. The PSD average of each hour was attributed to the actual sea state of that hour.

4. All PSDs in every sea state were averaged over each Hz.

5. The averaged PSD for each sea state was spline fitted in a logarithmic scale.

These PSD-curves as a function of sea-state constitutes the Wenz diagram. Sea statesonly based on wind speed may be misguiding. A five minutes interval containing fifteen20 s means each whole hour were averaged and combined with corresponding wind speed(m/s). By doing that it became possible to compare noise levels at different momentsfor the same wind speeds, thereby could the influence of wind direction and duration beinvestigated. Using clean wind direction and duration data was not possible.

8.4.4 Ambient noise dependency of significant wave height

The procedure of determining the ambient noise dependency of significant wave heightwas the same as for the wind speed except that the sea states were governed by thewave height. The wave height is however estimated by SMHI using the wind data. Itcannot be expected to vastly improve the estimate of the noise dependence on sea state.It is expected, at least in the large oceans, that wind direction will have an effect on thesound levels. A persistent wind will build up a sea that will propagate long distances andeventually brake and produce sound. It is also possibly to assume a sea breeze wouldbuild up the sea close to shore more effective than a land breeze. This aspect was notstudied.

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8.4.5 Ambient noise dependency of hydrography

The sound propagation is dependent on the hydrography. Under special conditions asound channel develops that will give rise to long propagation distances. In the BalticSea it is the temperature and the salinity that alters the sound speed profile. Studyingthe sound speed, based on temperature and salinity as a function of depth, reveals thatthere are three distinct periods that has to be studied separately:

• Winter season, when only a halocline is present keeping the more saline and warmerwater at the bottom. The rest of the water column has a low and constant tem-perature. Sometimes at some places a weak thermocline is built up at the top witheven colder water.

• Summer season, when a strong thermocline is developed at 15- 20 m and there areonly small variations in sound speed between mid-layer and bottom layer.

• Mixed season, when storms mix the water around and no clear clines can be iden-tified. The mixed season were not investigated within this study.

The hydrographic data given were salinity and temperature at different depths and times.To calculate the actual sound velocity the MATLAB tool-kit SEAWATER [13] was used.

8.5 Sonar range calculations

The sonar equation as presented in chapter 7.2 is the most simple variant of sonar equa-tion. It can be adjust to be used for active sonars by adding a few terms. The sonarequation can also be adjusted for different types of passive sonars by changing the pa-rameters, e.g. DI is most dependent on sonar type.

The calculations with the sonar equation were performed with the software tool SonaCalc,written by FOI. The strength of using SonaCalc is that it automatically calculates thetransmission loss due to the actual hydrographic characteristics and the effects of changingthe vertical placement of both the source and the target. A change of sonar depth canresult in significant change of transmission loss due to the hydrographic character, i.e. asound channel. The depth of the sonar was set to 63 m and the source was set at thesurface. The source levels were set according to Miasnikov (1995) [14] and Urick (1983)[22], and is presented in Table 8.2.

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Table 8.2: Source levels at different frequencies based on papers published by Urick (1983)and Miasnikov (1995).

Type of ship ModeSL at 100 HzdB re 1 μPa

SL at 1000 HzdB re 1 μPa

SubmarinePeriscope depth

Ultra quiet 100 80

SubmarinePeriscope depth

Quiet 120 100

CorvettePropeller propulsion

Cruising 157 136

By reading the generated Wenz curves at 100 and 1000 Hz for each sea state the differ-ent noise levels was determined for the sonar equation. The hydrographic characteristicstogether with the noise levels made the sonar equation unique for the actual position.

Further some assumptions of the sonar equipment were made. To keep the sonar typeas simple as possible, the sonar type LOFAR, Low Frequency Analysis Recorder, wasselected. A sonar is ineffective as a single hydrophone and therefore a 25 m long arraywas added which resulted in a directivity gain of 5 dB.

The detection threshold was set to 9 dB, which is used as a reference level in severalstudies and further corresponds to the detection threshold an experienced sonar operatorwould use.

With SonaCalc the calculated transmission loss for every range is compared to the sumof the noise level, source level, detection threshold, the directivity index and the rangewhere they are equal gives the maximum range of detection. It is explained with Eq. 7.1.

Other frequencies would in some cases be more optimal to use, but due to lack of opensources only 100 and 1000 Hz source levels were used.

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9 Results and discussion

This chapter presents the ambient noise dependency of the weather and ship inducednoise and discusses the validity of the results. Presenting signal processing results andthe interpretation of those and introducing the location meteorological character for theposition.

9.1 Signal processing results

A quick scan of data showed that it contained spikes. It was decided to evaluate the effectof their presence and to remove them if necessary. With a Grubbs’ test based algorithm,the ”spike remover”, spikes were identified and removed. One noise recording is presentedtwice in Fig. 9.1. First the original data, then the processed data with a transparency isplotted.

Figure 9.1: An arbitrary selected noise file. The original recorded data is plotted withblue and the data post ”spike remover” with orange. Both signals are displaced 0.04 indifferent directions for clarity.

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A look at the signal as well as the stationarity test shows that the two versions of thesignal are ”behaving” almost identically. In the example presented, spikes at about 20,470, 1150 and 1200 seconds (coloured blue) are not present in the orange version of thesignal, which demonstrates that the algorithm effectively identifies and removes spikes.

Most of the spikes were removed, although not all. The reason of this might be bordersset in Grubbs’ test, i.e. some spikes were smaller than 5 standard deviations and notidentified. The energy content in a non-removed spike was investigated and it containeda low amount of energy and its influence of the mean of the signal is negligible. Thosespikes are only a few samples long and didn’t affect the 20 s means with any significance.Therefore the spike remover is regarded trustworthy and was used routinely in this study.

By listening to the spikes, it could be concluded that the spikes were generated by a mo-tion inside the hydrophone pounding, or electrical noise, most likely self-noise. It couldalso occur due to a fish or bubbles hitting the hydrophone, but without doubt it was nota part of the ambient noise and was therefore removed from data.

The stationarity test of the signal was done both prior and post the use of the ”spikeremover”. The results from these two tests are presented in Fig. 9.2. The signal wastested with the Kolmogorov-Smirnov two sample test (KS-test) and each value on thex-axis represents two time windows. The values on the y-axis, α, are the probabilityof an error I or II, i.e. the probability of rejecting a true null hypothesis or acceptinga false. The red line is representing a 5% level which often is used and the green a 1% level.

Figure 9.2: KS-test for recordings in January in Bothnian Sea pre (blue) and post (orange)the ”spike remover”. The y-axis shows the probability of non-stationarity. The red linerepresentes a 5% level of significance and the green 1%.

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The KS-test show that the signal is never strict stationary at a significant level of 99%,alternatively there is never less than 1% probability of an Error I or II. Lots of timewindows are strict stationary of a significance level of 95%, both before and after the useof ”spike remover” algorithm. It should be underlined that the level of stationarity ofthe signal was improved after the use of the spike remover. Not all time windows wereimproved. A hypothesis is that segments with ship passings close to the hydrophoneresulted in a non-stationary signal. This can potentially be used to identify ship pas-sages.This has to be tested since the KS-test also identifies other events with non-shiporigin.

The results of the stationarity indicates that the majority of the signals processed arestrict stationary of a significant level of 95% which implies that the statistical estimatescan be applied. To be rigorous all time-windows with an α> 0.05 should be removedfrom the data set. This would have resulted in a totally strict stationary set of data at asignificant level of 95%. This was not done due to time restriction of the study.

The verification of the averaging methods were performed in two steps, and the first ispresented in Fig. 9.3. The figure illustrates a time series of one noise recording á 23minutes. The figure consists of the original data, the 1 s means and the 20 s means. Tocompare them the average of the 1 s means and 20 s means is presented together withthe median of the 1 s means.

Figure 9.3: One arbitrary selected noise recording. Blue illustrates the captured noise.Red is the 1 s means. Black the 20 s means and the horizontal lines are the mean andmedian of the 1 s means and the 20 s means. The original data amplitude is in volt andthe data is amplified and displaced to make the comparisons of the averages and originald

johan fridstr omkth.diva-portal.org/smash/get/diva2:893822/fulltext01.pdfpassive sonar ranges were...

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