A FAST data mining approach for similar earthquake detection
Abstract/Contents
- Abstract
- Seismology, a data-driven science where breakthroughs often come from improved observations, is experiencing rapid growth in the quantity of data. Many recent advances in computing technologies enable the automatic detection of hidden patterns in massive data volumes: increased computing power, parallel processing, more memory, vast disk storage, and the development of new scalable data mining and machine learning algorithms. Seismologists can leverage these technological advances to extract more useful information from these data sets. This thesis employs advances in data-driven technology to improve a fundamental operation for observational seismology: earthquake detection. It introduces a new algorithm called Fingerprint And Similarity Thresholding (FAST), adapted from a data mining technique originally developed for audio recognition, to detect small earthquake signals within continuous time series seismic data. FAST efficiently performs a comprehensive blind search for similar earthquake signals at all possible times in the continuous data, without requiring previous knowledge about the earthquake waveform. FAST is scalable to continuous data sets up to a decade long, and can detect earthquake signals over a network of multiple seismic stations. FAST can identify new earthquake sources that produce only small, previously undetected earthquakes; it is especially useful for detecting new earthquakes in sparse seismic networks, but can also find previously unknown earthquakes even with relatively complete catalogs. Three separate investigations in this thesis reveal that earthquakes newly detected with FAST, followed by their relative location, magnitude estimation, and source characterization, provide a more complete understanding of the underlying earthquake process: 1) The 1999 magnitude 7.1 Hector Mine, California, earthquake was triggered by a cascade of stress transfer from its foreshocks; 2) Microseismicity near the Diablo Canyon nuclear power plant in California from 2007-2017 confirmed that nearby faults are active; 3) Many microearthquakes in the initial stages of the 2010 Guy-Greenbrier, Arkansas, earthquake sequence were induced by hydraulic fracturing. The open-source FAST software, developed in an interdisciplinary collaborative effort, successfully detects earthquakes in a diverse range of seismic data sets, and it can be a useful tool to enable other seismologists to discover small earthquake signals of interest.
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 | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Yoon, Clara Elizabeth |
|---|---|
| Degree supervisor | Beroza, Gregory C. (Gregory Christian) |
| Thesis advisor | Beroza, Gregory C. (Gregory Christian) |
| Thesis advisor | Dunham, Eric |
| Thesis advisor | Ellsworth, William L |
| Degree committee member | Dunham, Eric |
| Degree committee member | Ellsworth, William L |
| Associated with | Stanford University, Department of Geophysics. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Clara Elizabeth Yoon. |
|---|---|
| Note | Submitted to the Department of Geophysics. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Clara Elizabeth Yoon
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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