Deep learning for seismic template recognition

Dawei Mu, En Jui Lee, Pietro Cicotti, Florin James Langer, Yifeng Cui, Junyi Qiu, Haemin Jenny Lee, Cody Morrin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Detecting earthquakes is one of the most fundamental tasks in seismology. As continuous seismic recordings grow and become readily available, they offer opportunities for identifying weak seismic signals (e.g., microseismicity, nonvolcanic tremor). Mining this source of data recordings is now considered a substantial method to study various geophysical phenomena. Widely used methods, such as the template matching algorithm (TMA), use waveforms of confirmed seismic signal to slide through corresponding continuous recordings searching similarities. These methods present several weaknesses, including sensitivity to the choice of thresholds, signal-to-noise ratios (SNRs), and bias. In this work, we present a solution for seismic signal recognition using emerging deep learning techniques. We have designed a Convolutional Neural Network (CNN) that recognizes seismic signals using features extracted by multiple filter kernels. This software is designed for training with data from multiple stations and the SNRs of template waveforms. We demonstrate the accuracy of our approach using seismic waveform recordings from multiple stations during the 2016 ML 6.6 Meinong earthquake sequence in Taiwan.

Original languageEnglish
Title of host publicationPractice and Experience in Advanced Research Computing 2018
Subtitle of host publicationSeamless Creativity, PEARC 2018
PublisherAssociation for Computing Machinery
ISBN (Print)9781450364461
Publication statusPublished - 2018 Jul 22
Event2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018 - Pittsburgh, United States
Duration: 2017 Jul 222017 Jul 26

Publication series

NameACM International Conference Proceeding Series


Other2018 Practice and Experience in Advanced Research Computing Conference: Seamless Creativity, PEARC 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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