MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

Local event detection with neural networks(application to Webnet data)

J. Doubravová, J. Horálek1 and J. Wiszniowski2

1Department of Seismology

Institute of Geophysics, Czech Academy of Sciences

2Department of Seismology

Institute of Geophysics, Polish Academy of Sciences

Workshop CzechGeo/EPOS, 16.11.2016

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

Motivation

seismic networks produce huge volumes of continuous data

good seismic event detection

for manual processing to reduce false alarms to be inspected(speci�c)for automatic processing detect all the weak events (sensitive)

in arti�cial neural networks the useful information is extracted(even if we are not able to describe the algorithm)

in arti�cial neural networks the forward problem is solved fast

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

Biology facts

neural network - interface between organism and environment

information from receptors spread through neurons to e�ectors

in cerebral cortex 15-33 billion neurons

dendrites (inputs), synaptic terminals (output, connected to upto 5000 other neurons dendrites)

synaptic permeability between dendrite and axon is adjusted

if the activity of dendrites is above threshold - neurongenerates an electric impulse

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

Formal neuron

n real inputs x = dendrites, bias x0 = 1

weights w = synaptic permeability,w0 =−h threshold

activation potential ξ =n

∑i=0

wixi

activation function y = σ(ξ )

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

Arti�cial neural network

to solve more complex problems - neural networks

typical applications: classi�cation, pattern recognition,regression

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

WEBNET dataSingle Layer Recurrent Neural Network con�gurationSLRNN training

WEBNET data

22 stations in total

13 stations online

250Hz, threecomponentground-velocityrecords

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

WEBNET dataSingle Layer Recurrent Neural Network con�gurationSLRNN training

SLRNN - network architecture

outputs of neurons are fed back as inputs = recurrence,memory

delays of more samples D1..Dd (1, 2, 4, and 8 samples)

8 neurons, 18 inputs, 3 outputs (event, P, S)

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

WEBNET dataSingle Layer Recurrent Neural Network con�gurationSLRNN training

SLRNN - input data processing

STA window 2x shortest period, LTAwindow 10x longest period

decimation to 0.2 s time steps -compromise between the acceptablecomputational load and goodseparation of individual waves

100

101

102

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Frequency [Hz]

Fre

quency r

esponse [−

]

vertical 0.6-1 Hz

vertical 25-40 HzZ

N

E

√N2 + E2

Filter

bank

9 x

half-octave

IIR filters

horizontal 0.6-1 Hz

horizontal 25-40 Hz

STA/LTA

ratios

for eachfreq. band

STA/LTA vertical

0.6-1 Hz

STA/LTA horizontal

25-40 Hz

downsample

50 x

18x ANNinputs

Z 0.6-1 Hz

Z 25-40 Hz

E 0.6-1 Hz

E 25-40 Hz

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

WEBNET dataSingle Layer Recurrent Neural Network con�gurationSLRNN training

Training

supervised learning: �nd wij to get the best �t of the actualand required outputs

100 events for each station (di�erent magnitudes, locations orfocal mechanisms)

100 examples of disturbances and non-local events (quarryblasts, regional or teleseismic events, disturbances by wind orstorms...)

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

WEBNET dataSingle Layer Recurrent Neural Network con�gurationSLRNN training

Training - example

Learning Importance Weight of Events(LIWE) - sensitivity of eventoutput to S wave onset = very important parameter

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

TestsRelation to SNRUndetected events

Cost function and ROC for various LIWE

sensitivity = detected events / all events

speci�city = rejected disturbances / all disturbances

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

TestsRelation to SNRUndetected events

ROC for joint and individual training

in case that the training P- and S-onset picks are not evenlypresent on all stations, it is helpful to use joint training

if the picks are complete for the station and all events,individual training performs better

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

TestsRelation to SNRUndetected events

Signal-to-noise ratio

the worst trained stations (KAC and ZHC) also those withhigher noisethe best trained stations (LBC, POC, VAC) with lower noise(higher SNR)

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

TestsRelation to SNRUndetected events

Undetected events - examplesEvents ML = 2.3 and ML = 2.2 masked by a coda of ML = 3.8 earthquake

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

TestsRelation to SNRUndetected events

Undetected events - examplesdetection of ML =−0.3 event failed on station KAC due to higher noise level

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)

MotivationArti�cial neural networks - introduction

Method and dataTests and results

Summary

Summary

SLRNN proved to be an appropriate tool for local eventdetection

LIWE - very important, a�ects the number of successful trials

in case the training P- and S-onset picks are not evenlypresent on all stations => joint training helps

if the picks are complete for the station and all events =>individual training performs better

Outlook

use the network = coincidence - rejection of false alarms +con�rmation of eventstry to teach similar network for Reykjanet data

J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to Webnet data)