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

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Max Blomqvist
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1 Local event detection with neural networks (application to Webnet data) J. Doubravová, J. Horálek 1 and J. Wiszniowski 2 1 Department of Seismology Institute of Geophysics, Czech Academy of Sciences 2 Department of Seismology Institute of Geophysics, Polish Academy of Sciences Workshop CzechGeo/EPOS,

2 seismic networks produce huge volumes of continuous data good seismic event detection for manual processing to reduce false alarms to be inspected (specic) for automatic processing detect all the weak events (sensitive) in articial neural networks the useful information is extracted (even if we are not able to describe the algorithm) in articial neural networks the forward problem is solved fast

3 Biology facts neural network - interface between organism and environment information from receptors spread through neurons to eectors in cerebral cortex billion neurons dendrites (inputs), synaptic terminals (output, connected to up to 5000 other neurons dendrites) synaptic permeability between dendrite and axon is adjusted if the activity of dendrites is above threshold - neuron generates an electric impulse

4 Formal neuron n real inputs x = dendrites, bias x 0 = 1 weights w = synaptic permeability, w 0 = h threshold activation potential ξ = n w i x i i=0 activation function y = σ(ξ )

5 Articial neural network to solve more complex problems - neural networks typical applications: classication, pattern recognition, regression

stations online 250Hz, three component ground-velocity records J. Doubravová, J.

6 Arti cial neural networks - introduction WEBNET data Single Layer Recurrent Neural Network con guration SLRNN training WEBNET data 22 stations in total 13 stations online 250Hz, three component ground-velocity records J. Doubravová, J. Horálek and J. Wiszniowski Local event detection with neural networks (application to

7 SLRNN - network architecture WEBNET data Single Layer Recurrent Neural Network conguration SLRNN training outputs of neurons are fed back as inputs = recurrence, memory delays of more samples D 1..D d (1, 2, 4, and 8 samples) 8 neurons, 18 inputs, 3 outputs (event, P, S)

8 SLRNN - input data processing WEBNET data Single Layer Recurrent Neural Network conguration SLRNN training STA window 2x shortest period, LTA window 10x longest period decimation to 0.2 s time steps - compromise between the acceptable computational load and good separation of individual waves Frequency response [ ] Frequency [Hz] Z N E Filter Z Hz bank Z Hz 9 x half-octave E Hz IIR filters E Hz N 2 + E 2 vertical Hz vertical Hz horizontal Hz horizontal Hz STA/LTA vertical STA/LTA Hz ratios for each freq. band STA/LTA horizontal Hz downsample 50 x 18x ANN inputs

9 Training WEBNET data Single Layer Recurrent Neural Network conguration SLRNN training supervised learning: nd w ij to get the best t of the actual and required outputs 100 events for each station (dierent magnitudes, locations or focal mechanisms) 100 examples of disturbances and non-local events (quarry blasts, regional or teleseismic events, disturbances by wind or storms...)

10 Training - example WEBNET data Single Layer Recurrent Neural Network conguration SLRNN training Learning Importance Weight of Events(LIWE) - sensitivity of event output to S wave onset = very important parameter

11 Tests Relation to SNR Undetected events Cost function and ROC for various LIWE sensitivity = detected events / all events specicity = rejected disturbances / all disturbances

12 Tests Relation to SNR Undetected events ROC for joint and individual training in case that the training P- and S-onset picks are not evenly present 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

13 Signal-to-noise ratio Tests Relation to SNR Undetected events the worst trained stations (KAC and ZHC) also those with higher noise the best trained stations (LBC, POC, VAC) with lower noise (higher SNR)

14 Tests Relation to SNR Undetected events Undetected events - examples Events M L = 2.3 and M L = 2.2 masked by a coda of M L = 3.8 earthquake

15 Tests Relation to SNR Undetected events Undetected events - examples detection of M L = 0.3 event failed on station KAC due to higher noise level

16 SLRNN proved to be an appropriate tool for local event detection LIWE - very important, aects the number of successful trials in case the training P- and S-onset picks are not evenly present 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 + conrmation of events try to teach similar network for Reykjanet data

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