Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data

SUMMARY What makes a seismogram look like a seismogram? Seismic data sets generally contain waveforms sharing some set of visual characteristics and features—indeed, seismologists routinely exploit this when performing quality control ‘by hand’. Understanding and harnessing these characteristics off...

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Published in:Geophysical journal international Vol. 189; no. 2; pp. 1183 - 1202
Main Authors: Valentine, Andrew P., Trampert, Jeannot
Format: Journal Article
Language:English
Published: Oxford, UK Blackwell Publishing Ltd 01.05.2012
Subjects:
Encoding
Networks
Neural networks
Neural networks, fuzzy logic
Searching
Seismic engineering
Seismic phenomena
Seismograms
Self‐organization
Statistical seismology
Time‐series analysis
Wave propagation
Waveforms
ISSN:0956-540X, 1365-246X
Online Access:Get full text
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Summary:SUMMARY What makes a seismogram look like a seismogram? Seismic data sets generally contain waveforms sharing some set of visual characteristics and features—indeed, seismologists routinely exploit this when performing quality control ‘by hand’. Understanding and harnessing these characteristics offers the prospect of a deeper understanding of seismic waveforms, and opens up many potential new techniques for processing and working with data. In addition, the fact that certain features are shared between waveforms suggests that it may be possible to transform the data away from the time domain, and represent the same information using fewer parameters. If so, this would be a significant step towards making fully non‐linear tomographic inversions computationally tractable. Hinton & Salakhutdinov showed that a particular class of neural network, termed ‘autoencoder networks’, may be used to find lower‐dimensional encodings of complex binary data sets. Here, we adapt their work to the continuous case to allow the use of autoencoders for seismic waveforms, and offer a demonstration in which we compress 512‐point waveforms to 32‐element encodings. We also demonstrate that the mapping from data to encoding space, and its inverse, are well behaved, as required for many applications. Finally, we sketch a number of potential applications of the technique, which we hope will be of practical interest across all seismological disciplines, and beyond.
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ISSN:0956-540X
1365-246X
DOI:10.1111/j.1365-246X.2012.05429.x