Machine learning for seismic event detection : a story in three parts: earthquakes, microseismic events, and tectonic tremors
Abstract/Contents
- Abstract
- 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 conduits 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.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Huot, Fantine Eri |
|---|---|
| Degree supervisor | Biondi, Biondo, 1959- |
| Thesis advisor | Biondi, Biondo, 1959- |
| Thesis advisor | Beroza, Gregory C. (Gregory Christian) |
| Thesis advisor | Clapp, Robert G. (Robert Graham) |
| Degree committee member | Beroza, Gregory C. (Gregory Christian) |
| Degree committee member | Clapp, Robert G. (Robert Graham) |
| Associated with | Stanford University, Department of Geophysics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Fantine Eri Huot. |
|---|---|
| Note | Submitted to the Department of Geophysics. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/hw584wy6079 |
Access conditions
- Copyright
- © 2022 by Fantine Eri Huot
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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