1-4244-2547-1/08/$20.00 ©2008 IEEE

Managing Volcanic Unrest: the Mobile Volcano Fast Response System

Matthias Hort, Klemen Zakšek, and the Exupéry Team1

Institute of Geophysics Hamburg University Hamburg, Germany

matthias.hort@zmaw.de, klemen.zaksek@zmaw.de

Abstract-A mobile volcano fast response system (VFRS) that

can be deployed on a volcano in case of volcanic unrest is currently under development in Germany. The main goal of the project called Exupéry, which is funded by the BMBF (German ministry for the education and research) project Geotech-nologien, is to develop the core of a mobile volcano fast response system, i.e. providing the communication technology for stations in the field and an expert system that collects all data from various sources, assembles them in a data base, and allows different modules to assess the data and model and project the activity status of the volcano. Important novelties of the proposed mobile system are the direct inclusion of the remote sensing data into VFRS and the wireless communication mesh node based system that enables communication among field instruments and a single data base collecting data from different sources.

Keywords-Exupéry VFRS; terrestrial and satellite observations; GIS; physical model; Azores

I. INTRODUCTION Volcanic eruptions are common natural processes that have

occurred since the formation of the Earth. During most of Earth history they affected life on the Earth in several ways, but with the fast growing population and increasing globalization including fast growing communication and transport systems they are becoming more and more a real threat to our society [1]. Monitoring eruptive processes is therefore a major task especially in those countries that are most affected by volcanic activity.

The majority of the active and dangerous volcanoes are located around the pacific ring of fire with about half of them being located in third world countries. In the framework of this project we therefore develop the core of a mobile volcano fast response system (VFRS) to support those countries in case of a volcanic crisis, in case help is requested. We note that the idea of a response team for a volcanic crisis is not new. The Volcano Disaster Assistance Program (VDAP) was the first rapid response in the world [2]; its most notable success was managing the crisis associated with the catastrophic eruption of Mount Pinatubo in the Philippines in 1991 as it saved

thousands of lives.

Because some of the high risk volcanic systems around the world are already monitored in various ways (especially in the European Union), we the core of a prototype of a mobile VFRS that can be deployed on a volcano in case of unrest anywhere in the world. The system is intended to either operate standalone or to complement an existing network including some novel monitoring parameters that are regularly not observed (i.e. SO2 concentration, thermal anomalies, etc.), but which provide additional information that do allow further insight into the processes inside the volcanic edifice. The main idea behind this system is:

• that it can be installed fast due to intelligent, cable-free communication between the different stations and a data center,

• that all data are collected in a central data base including the data from an existing network (open system) if desired by local scientists and authorities,

• that the data are visualized and partially analyzed in real time,

• that models are developed to derive activity parameters out of the recorded data, and

• that objective and reliable data evaluations are carried out including recommendations for crisis management.

The project includes five main tasks, each addressing different aspects of the mobile system (Fig. 1). In order to give the system similar capabilities as systems which are permanently installed at volcano observatories we attempt to include satellite based observations that include monitoring of ground deformation, SO2 concentration, and thermal anoma-lies. Those observations are complemented by terrestrial observations of surface deformation and gas fluxes. Aside from those novel techniques (novel in terms of its implementation in the mobile system) we rely on traditional observation techniques (such as seismology) to characterize the activity status of the volcano.

1Exupéry Team: Terrestrial observations: Gerstenecker C.a, Hansteen

T.b, Läufer G.a, Becker M.a, Drescher Ra., Leinen S.a, Rödelsperger S.a; Space based observations: Eineder M.c, Valks P.c, Bamler R.cd, Cong X.Y.2, Erbertseder T.c, Hinz S.d, Loyola D.c, Märker C.c, Rix M.c, Seidenberger K.c; Data base and expert system: Stammler K.e, Wassermann J.f, Barsch R.f, Beyreuther M.f, Stittgen H.P.e, Weise K.g; Wireless communication: Garcia A.M.h; Model building: Ohrnberger M.i, Walter T.j, Dahm T.h, Hammer C.i, Krieger L.h, Shirzaei M.j, Wegler U.e

aDarmstadt University of Technology, bIFM-GEOMAR, cDLR, dTU Munich, eBGR, fLMU Munich, gJena-Optronik, hUniversity of Hamburg, iUniversity of Potsdam, jGFZ Potsdam

Figure 1. Relationship among the five work packages in the Exupéry VFRS.

All incoming data send by the wireless communications system (also developed in this project) are collected in a central database and several different routines are assessing those data to model the potential activity state of the volcano. A field test of the whole system is scheduled for mid 2009 on the Fogo volcano (Azores, Portugal). A brief description of the different efforts undertaken in developing the components of the VFRS will be given within the next chapters.

II. TERRESTRIAL OBSERVATIONS The goal is to provide novel terrestrial observation

techniques to the VFRS. Aside from classic seismic observation systems (e.g. seismological observations), we attempt to incorporate two novel (novel in terms of mobile) terrestrial observational techniques: high resolution local deformation measurements performed by a combination of terrestrial InSAR system and GPS receivers, and terrestrial gas measurements.

A. Terrestrial Ground Deformation Monitoring Two different instruments are used to monitor ground

deformations: observations of the terrestrial Synthetic Aperture Radar system IBIS-L [3] are fused with GPS observations. Up to eight single and dual frequency GPS-receivers are used to determine three-dimensional deformations on-line at particular stations. IBIS-L delivers two-dimensional areal deformations. Data from both systems are combined to a near real time hybrid deformation monitoring system that is independent of weather and daylight. The sampling rate depends on the distance to monitored area and is on average 6 min [4]. Accuracy is on the order of 1 mm. Ground deformations will used as a data input for source inversion and stress field modeling.

B. Terrestrial Gas Flux Monitoring Terrestrial gas flux monitoring enables near real-time flux

measurements of volcanic gases. Especially high sulphur dioxide (SO2) fluxes are a reliable indicator of magma presence during new episodes of volcanic unrest. Monitoring of these fluxes is done with a UV spectrometer using a miniature Scanning Dual beam (Differential Optical Absorption Spectroscopy (Mini-DOAS) [5]. The instrument measures both areal SO2 concentrations and SO2 and bromine oxide (BrO) fluxes. Mini-DOAS provides one complete measurement every

3–5 min during daylight time. As passive degassing occurs often before the eruption, the gas fluxes are important for automatic alert level estimation.

III. SPACE BASED OBSERVATIONS The goal is to include space based observations into the

VFRS because in the recent years developments in space based observations have opened up a new field in volcanology allowing the mapping of deformation, identification of broad degassing signatures, and the detection of thermal anomalies.

A. Satellite Monitoring of Ground Deformation Ground deformation is retrieved from SAR images by

using differential SAR interferometry (DInSAR) or quasi-continuous monitoring using time series of SAR images with persistent scatter ground points (PS-InSAR). One of the challenges is a fusion of multi geometry/satellite data into one model to get seedless coverage and increase measurement reliability [6]. Millimetric subsidence or uplift can be retrieved from SAR images by using interferometric techniques.

The Azores (Portugal) and Stromboli volcano (Italy) were chosen as test sites. Multi-frequency X-, C- and L-Band SAR data-sets were acquired from the satellite sensors TerraSAR-X, ASAR (aboard ENVISAT) and PALSAR (aboard ALOS) for the test areas. The volcanic movements were analyzed with the differential phases and the measurements will be compared in the near future with on-site measurements including GPS and terrestrial DInSAR measurements from the field campaign in Azores (see below section V.). The estimated ground deforma-tion will be then incorporated into geophysical stress field models in order to initialize, calibrate, and improve physical models [7].

The volcano Stromboli is currently very active and its high elevation variation poses challenges for side-looking SAR images because of its steep slopes and its unrest causing temporal decorrelation. Different TerraSAR-X acquisition geometries were used with different incidence angles, descending and ascending orbits to achieve a seamless coverage. Besides DInSAR also PS-InSAR technique was applied in order to reduce the atmospheric affects and erroneous signals from the incorrect DEM. The maximum estimated displacement equals +-30mm/year.

Figure 2. Soufriere Hills Volcano, Montserrat

quantitive physical models

terrestrial observations incl. WLAN technology

satellite based observations

database, visualization, alert levels, early warnings

overall project coordination

Fogo volcano on the Sao Miguel Island (Azores) has not shown a high volcanic activity in the recent years but the ground deformations can still be observed from the satellite images (TerraSAR-X, PALSAR, ASAR) using DInSAR [8]. The area is covered with dense vegetation that causes the surface decorrelation when using TerraSAR-X.

Radar data are not appropriate merely for ground deformation monitoring but they are also a valuable source of information in the case of cloud coverage. During the eruption of Soufriere Hills Volcano (Montserrat) an image sequence was acquired and evaluated to support DLR’s Center for Satellite Based Crisis Information" (ZKI) and the responsible National Disaster Preparedness and Response Advisory Committee (NDPRAC). The radar (amplitude) data allowed identification of the lahar flows after the eruption before the area could be accessed by other means (Fig. 2).

B. Satellite Monitoring of Volcanic SO2 Plumes Space based atmospheric sensors like GOME-2 or

SCIAMACHY allow monitoring volcanic activity and eruptions on a global scale and on a daily basis by measuring the concentration of atmospheric SO2 [9], [10]. The results are used for automatic alert level estimation. SO2 concentration is retrieved from solar backscatter measurements in the ultra-violet spectral range (315–326 nm) using DOAS.

The used DOAS method has been optimized for the GOME-2 instrument. The SO2 vertical column amounts can be retrieved for different volcanic plume heights, based on the results from the DOAS algorithm and an appropriate air mass factor calculated with a radiative transfer model. In addition to the SO2 vertical column amounts the characteristics of volcanic eruptions like plume height, eruption time and plume transport can be estimated based on the GOME-2 SO2 observations and the state-of-the-art trajectory model FLEXTRA. The new analysis technique uses probability distributions of backward trajectory ensembles.

The GOME-2 SO2 data were used for analyses of several volcanic eruptions. Recent examples are the eruption of Etna, May 2008 (Fig. 3), and the eruption of Kasatochi, August 2008, where GOME-2 was one of the first instruments to detect the eruption plume. Large amounts of SO2 were detected for the Kasatochi eruption and the plume could be traced for more than two weeks as it travelled across the northern hemisphere. The results of the Etna eruption in May 2008 have been verified with the HYSPLIT trajectory model of the National Oceanic and Atmospheric Agency (NOAA) and wind field analysis of MSG measurements by the European Centre for Medium Range Weather Forecast. The SO2 data have also been used for daily monitoring of passively degassing volcanoes, for example in Ecuador or Papua New Guinea.

The GOME-2 SO2 retrieval is carried out in near real-time, i.e. within two to three hours after the actual GOME-2 measurement [11], [12]. Daily volcanic activity maps using the GOME-2 SO2 retrievals are provided online in near real time for the selected volcanic regions all over the world – URL: http://wdc.dlr.de/data_products/SERVICES/GOME2NRT/.

Figure 3. An SO2 cloud moved to the East Mediterran a day after the powerful paroxysm at South East Crater of Etna on May 10th 2008.

C. Satellite Monitoring of Thermal Anomalies No operational satellite is devoted to thermal anomalies

monitoring (i.e. ASTER has to long revisit time, BIRD [13], [14], [15], [16] is not functional anymore), thus meteorological satellites are used for this purpose. The background of detecting and characterizing thermal anomalies using remote sensing techniques is based on the Wien’s displacement law – the peak emission wavelength gets longer by lowering the temperature. Using a dual band technique [17], even temperature and the area of the thermal anomaly can be determined. These values can be then used to estimate radiant flux and effusion rate.

The outputs of the dual band method [17] (thermal anomaly temperature and area) often contain large errors. Thus a new strategy of supplying thermal anomaly characteristics into the Exupéry GIS database is currently being developed. Radiant flux computed from temperature and area of the thermal anomaly was chosen as the best estimator of the thermal activity because of its robustness. Secondly, with the aim to relax inter-channel co-registration, the whole thermal anomaly is not characterized single pixel-wise but as a cluster. In order to reduce the noise in the measurements and to improve the temporal resolution of the data, AVHRR and MODIS data are used simultaneously. The results go directly to into the data base.

This method was tested in the case study of Etna volcano (Sicily, Italy) for the period between October 27th and November 3rd 2002 [18]. During this period the radiant flux exceeded 1 GW. The data acquired by DLR micro satellite BIRD [13], [14], [15], [16] (Fig. 4) were used to validate the results obtained by MODIS and AVHRR. During the study period the radiant flux (observed by MODIS on Aqua) drops significantly at the beginning of October 28th because Etna was acquired on two images – the sum of both values corresponds to the real value. A problem occurs also on November 2nd – the radiant flux from BIRD data is twice as large as the one from MODIS on Terra because of the large Terra zenith angle. Otherwise, the time series shows that MODIS and AVHRR provide similar values and can be used simultaneously. The accuracy of their simultaneous use will be enhanced in the future by implementing the Kalman filter.

Figure 4. BIRD image of Etna eruption (November 2nd 2002 at 10:17 UTC): plume in pink and thermal anomalies in yellow;

radinat flux 1.1 Gw, temperature 540 K, area 25 ha [13], [14], [15], [16]. The figure is courtesy of the DLR-Adlershof (D. Oertel, G. Schlotzhauer).

IV. DATA BASE AND EXPERT SYSTEM The data base is central to the whole system. It will allow

storing, browsing, accessing, and visualizing multi-component datasets (raw and/or parameterized) recorded at active volcanoes during a crisis. As the data base holds raw data, metadata, as well as results from the model building we will attempt to automatically estimated alert levels based on Bayesian Belief Networks.

A. Database and GIS Data currently used in state of the art networks at volcano

observatories are inherently diverse and complex spanning from continuous 1D time series all the way to discontinuous 2D or 3D datasets. This results in a high-dimensional, complicated data stream, which needs a flexible database and analysis system to handle them. Unfortunately most volcanology adapted databases and processing tools are developed along the needs of seismological observations and they are often limited to classic three-component seismic recordings and are unable to easily handle collocated multi-component datasets (rotational motions, 2D InSAR, etc.). In order to make the data stream of the VFRS available for real time processing we adopt a new database called SeisHub [19] – a web service and database for archiving, processing and distributing of seismological data over the network. In order to assess the current activity status of the volcano at unrest and in order to provide decision makers with the information available the data must be visualized. The visualization will be implemented using license free GIS software. The current development includes the direct data access via possible links to data centers (i.e. DLR) and/or import and queries of the Exupéry data base via web services and XML protocols.

B. Automatic Alert Level Estimation Alert levels (AL) are color or number codes which are used

to characterize the activity status of a volcano. They are usually determined by a group of scientist and decision makers that are involved in managing the volcanic crisis. In order to assist this group of people the system will determine alert levels automatically using so called Basian Belief Networks (BBN) [20]. The automatic AL determination will especially help local decision makers to identify the particular parameter that is

mainly responsible for a possible high, automatically estimated AL including its confidence measure. A high, automatically estimated AL with either high or low confidence can certainly lead to different decisions by the human interpreter. Importantly, the fully probabilistic architecture of BBN is of advantage when dealing with uncertainties. Their ability to represent the conditional probability distributions as graphical models provides a transparent way for estimating the AL.

V. WP4: WIRELESS COMMUNICATION AND SYSTEM TEST In order to provide the data collected in the field in real

time to the data base a reliable wireless communication including sufficient bandwidth is necessary.

A. Wireless Network Based on standard industry components a so called wireless

communication node (WCN) was developed to provide MESH networking telemetry [21]. Main requirements are low power consumption and accessibility via different ports (serial, USB, Ethernet). Also the node should provide state of health information including instrument status and battery voltage. Each WCN includes an energy support system and a WLAN radio. The MESH networking capabilities guaranties communication between the monitoring station and the main data base even in the case of a node loss through rerouting the traffic via another line. The energy support system includes two solar panels and two batteries. The WLAN radio is build according to industry standard electronic boards and runs open software operating systems developed for wireless networks. It can operate as an access point, repeater, slave or any combination thereof, using one or two MiniPCI WLAN (200 mW, 802.11b/g) cards.

B. Prototype Instalation The whole system will be tested on the Azores in mid 2009.

This test includes the wireless communication network, 30 seismic stations from DEPAS pool [22], terrestrial measurements of deformation and gas flux. In addition we will also use the data that are currently recorded by the existing seismic network on the Azores. The land network will be complemented by ocean bottom seismometers and tiltmeter, whose data will be added to the database after the seagoing experiment. All the data collected during the test experiment will be used to further improve the algorithms that are developed in order to assess the activity state of the volcano.

VI. WP5: QUANTITATIVE PHYSICAL MODEL BUILDING The key to better characterize the activity status of a

volcano is the ability to identify changes in stress distribution. However, none of the methods available at hand provides a direct assessment of the stress or even better changes in the stress distribution. It is therefore necessary to further process the incoming data, if possible in real time, to better characterize stress changes.

A. Event Detection and Waveform Classification Classical trigger algorithms like STA/LTA are used to

enable a robust detection of transient signals in the presence of

noise. The window lengths in the STA/LTA algorithm [23] are varied adaptively using instantaneous frequency estimates. Incorporating dynamic thresholds allows the detection of signals with very low signal-to-noise ratios.

Once an event is detected it needs to be classified. Therefore continuous activity parameters are computed from the raw data stream. The real-time seismic amplitude measurements as well as the seismic spectral amplitude measurement are used to characterize the volcanic activity. State of the art speech recognition methods based on Hidden Markov Models (HMM) are adapted to the needs of VFRS [24], [25]. For the HMM-based classification module short time features are extracted in windows from the continuous data stream. Among those we compute polarization, spectral as well as time-domain attributes. The spectral characteristics show the best discrimination between different event classes. The main goal is to provide a robust event classification based on a minimum number of reference waveforms allowing for the fast build-up of a volcanic signal classification scheme for the volcanic task force action.

B. Source Inversion and Stress Field Modelling Ground deformation measurements harbor a wealth of

information on stresses and stress changes in volcanic systems. [26]. In order to improve existing inversion techniques, a new tool being gradient free, fast, reliable, and flexible enough to include auxiliary information from other disciplines and overcoming previous difficulties has been developed using two Random Search approaches: Simulated Annealing (SA) and a Genetic Algorithm (GA). This approach helps from getting trapped in local minima while searching for the best fit model and it also increases redundancy for exploring the search space.

C. (Near-)Real-Time Centroid Moment Tensor Inversion The implementation of the time-domain full waveform

inversion of transient seismic signals follows the concept of [27], [28], [29] and [30]. An algebraic routine works on given network data-windows and continuously inverts for source centroid, moment and radiation pattern based on a database of Green's functions. We focus on long-period-events (LP-event) with a frequency below 0.5 Hz, since these events have been associated with magma and volcanic fluid movement and are considered as an important class of events to characterize a specific state of unrest of a volcano. Additionally, working with relatively low frequencies avoids a possibly strong influence from the unknown small-scale structure of the volcano complex.

VII. CONCLUSION We have just completed the first year of the Exupéry

project. Most of the project tasks are still establishing the methods and tools for the VFRS including first applications to test cases. The data base is under development and will be tested during the field experiment in 2009 on the Azores at Fogo volcano. This will be the first full test of the Exupéry VFRS and will hopefully show all system limitations that should be removed before the project ends in 2010.

ACKNOWLEDGMENT This project is funded by the German Ministry for

Education and Research (BMBF) project Geotechnologien. We acknowledge the usage of land and OBS station from the DEPAS Pool (Germany) for the envisaged experiment at the Azores. We thank the DLR in Berlin Adlershof (D. Oertel, G. Schlotzhauer) for providing us with the BIRD data.

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