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A Story In Three Parts: Earthquakes, Microseismic, And Tectonic Tremors


Table of contents

  • Chapter 1: Introduction and background
  • Chapter 2: Deploying machine learning workflows at scale
  • Chapter 3: Detecting earthquakes in urban areas
  • Chapter 4: Detecting microseismic events on fiber-optic data
  • Chapter 5: Detecting low-frequency earthquakes
  • Chapter 6: Conclusion
  • Bibliography


As new seismic acquisition methods arise, growing data volumes call for automated processing methods to extract full value out of the recorded data. Herein, we develop an end-to-end machine learning framework for seismic event detection and identification on continuous data. We illustrate our methodology through three field-data use cases. Firstly, we perform earthquake detection using fiber-optic cables in the telecommunication con-duits under the Stanford University campus. We identify new uncataloged small-magnitude local earthquakes by analyzing more than three years of continuous recordings. We demonstrate that fiber-optic cables can complement sparse seismometer networks. We then tackle microseismic event detection in fiber-optic data acquired inside an unconventional reservoir. Our methodology identifies more than 100,000 events over ten hydraulic stimulation stages, allowing the reconstruction of the spatio-temporal fracture development far more accurately and efficiently than would have been feasible by traditional methods. Finally, we explore tectonic tremor identification using a catalog of more than 1 million events detected along the central San Andreas Fault over a period of 15 years. Tectonic tremors are composed of hundreds of repeating low-frequency earthquakes (LFEs). These LFEs are near the noise level and are thus usually found via a multichannel matched-filter search using carefully curated waveform templates. We demonstrate that our methodology can successfully detect new LFEs with low signal amplitude without a prior template.

Reproducibility and source codes

This thesis has been tested for reproducibility. The source codes are made available for download (password required).


Defense presentation

Fantine Huot
Publication Date
December 8, 2021