Machine Learning Detection of P-Waves in Laboratory Acoustic Emission Events to Understand Deep-Focus Earthquakes

Simple item page
dc.contributor.advisorNiu, Fenglinen_US
dc.creatorSheehan, Jacken_US
dc.date.accessioned2022-05-05T18:29:04Zen_US
dc.date.available2022-05-05T18:29:04Zen_US
dc.date.issued2022en_US
dc.descriptionSenior Honors Thesis for Earth, Environmental, and Planetary Science Department, Rice Universityen_US
dc.description.abstractThe mechanisms of deep-focus earthquakes (DFEQs)—those between 350 and 700 km depth—remain poorly understood due to our inability to directly measure their fault properties in situ. One potential explanation for DFEQ nucleation is the transformational faulting hypothesis, which theorizes mineral transformations initiate the faults. To analyze these structures in more detail, deformation events were conducted in a controlled laboratory environment on 2.1x3.0mm Mg¬2GeO4 samples, the leading mineralogical candidate of the transformational faulting hypothesis. Six orthogonally oriented sensors recorded Acoustic Emission (AE) events to detect P-wave arrival times, returning event-trigger waveform data at 50 MHz and continuous data for 42.4 minutes at 10 MHz. The experiment returned 3,901 event-trigger SAC files and 19,280 continuous SAC files, totaling just over one billion data points. To analyze this large quantity of raw waveforms, this study introduces machine learning as a tool to automate the detection process. A deep-learning-based detector called EqTransformer (EqT; Mousavi et al., 2020) was trained on global seismic data from the Stanford Earthquake Dataset (Mousavi et al., 2019) and applied to the experimental data to perform P-wave detection and arrival time picking. The short-term goal of this project is to determine the robustness of EqT on microseismic data in both event-trigger and continuous forms. Preliminary results indicate the application of EqT on the event-trigger data was successful. EqT detected 93.4% of the events identified manually, as well as 57.3% additional events missed by the human analysts. The long-term goal is to create a definitive catalog of the AE events that occurred in this experiment, using EqT on the continuous dataset. This could potentially offer key insights into the scaling properties of seismic experiments.en_US
dc.format.extent36 ppen_US
dc.identifier.citationSheehan, Jack. "Machine Learning Detection of P-Waves in Laboratory Acoustic Emission Events to Understand Deep-Focus Earthquakes." Undergraduate thesis, Rice University, 2022. https://doi.org/10.25611/MF2H-9609.en_US
dc.identifier.doihttps://doi.org/10.25611/MF2H-9609en_US
dc.identifier.urihttps://hdl.handle.net/1911/112388en_US
dc.language.isoengen_US
dc.publisherRice Universityen_US
dc.rightsThis item is shared under a Creative Commons License- Attribution (CC BY)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/us/en_US
dc.subjectearthquakesen_US
dc.subjectmachine-learningen_US
dc.subjectacoustic emissionsen_US
dc.titleMachine Learning Detection of P-Waves in Laboratory Acoustic Emission Events to Understand Deep-Focus Earthquakesen_US
dc.type.dcmiTexten_US
dc.type.genreThesisen_US
thesis.degree.departmentEarth, Environmental, and Planetary Scienceen_US
thesis.degree.disciplineNatural Sciencesen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelUndergraduateen_US
thesis.degree.nameHonor Thesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
sheehan_honors_thesis.pdf
Size:
3.97 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Rice Undergraduate Theses