\"During the past decades, applications of Machine Learning have been explosively developed to solve various academic and industrial problems, and over-human performance has been shown in diverse areas. In geophysical research, Machine Learning, especially Convolutional Neural Network (CNN), has been applied in numerous studies and demonstrated considerable potential. In this study, we applied CNN to solve two geophysical problems, ranking teleseismic shear splitting (SWS) measurements and classifying different types of earthquakes.
For ranking teleseismic SWS measurements, we utilized a CNN-based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data recorded in south-central Alaska reveals that CNN classifies 98.1% of human-selected measurements as acceptable and revealed ~30% additional measurements.
For classifying different types of earthquakes, we utilized a CNN to classify natural earthquakes, mine collapses, and explosions using seismic waveforms recorded by 287 stations in Shandong Province, China. Cross-validation is employed to scan the whole dataset, and the measurements with different labels between human and the CNN are manually assessed and kept, corrected, or abandoned in the dataset. Testing with the corrected dataset, the classification accuracies of the three types of events increase from 97.3% to 99.2% for earthquakes, from 84.9% to 95.8% for mine collapses, and from 93.6% to 98.1% for explosions\"--Abstract, p. iv
","abstract_html":"<p>"During the past decades, applications of Machine Learning have been explosively developed to solve various academic and industrial problems, and over-human performance has been shown in diverse areas. In geophysical research, Machine Learning, especially Convolutional Neural Network (CNN), has been applied in numerous studies and demonstrated considerable potential. In this study, we applied CNN to solve two geophysical problems, ranking teleseismic shear splitting (SWS) measurements and classifying different types of earthquakes.</p> <p>For ranking teleseismic SWS measurements, we utilized a CNN-based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data recorded in south-central Alaska reveals that CNN classifies 98.1% of human-selected measurements as acceptable and revealed ~30% additional measurements.</p> <p>For classifying different types of earthquakes, we utilized a CNN to classify natural earthquakes, mine collapses, and explosions using seismic waveforms recorded by 287 stations in Shandong Province, China. Cross-validation is employed to scan the whole dataset, and the measurements with different labels between human and the CNN are manually assessed and kept, corrected, or abandoned in the dataset. Testing with the corrected dataset, the classification accuracies of the three types of events increase from 97.3% to 99.2% for earthquakes, from 84.9% to 95.8% for mine collapses, and from 93.6% to 98.1% for explosions"--Abstract, p. iv</p>","abstract_has_math":false,"creators":["Zhang, Yanwei"],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Geology and Geophysics","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T03:18:18Z","subjects":["Convolutional Neural Network","Earthquake Classification","Machine Learning","Seismology","Shear Wave Splitting","Earth Sciences","Geology","Geophysics and Seismology","Physical Sciences and Mathematics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/3245","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Zhang, Yanwei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Geology and Geophysics"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Missouri University of Science and Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Convolutional Neural Network","Earthquake Classification","Machine Learning","Seismology","Shear Wave Splitting","Earth Sciences","Geology","Geophysics and Seismology","Physical Sciences and Mathematics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/3245"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"During the past decades, applications of Machine Learning have been explosively developed to solve various academic and industrial problems, and over-human performance has been shown in diverse areas. In geophysical research, Machine Learning, especially Convolutional Neural Network (CNN), has been applied in numerous studies and demonstrated considerable potential. In this study, we applied CNN to solve two geophysical problems, ranking teleseismic shear splitting (SWS) measurements and classifying different types of earthquakes.
For ranking teleseismic SWS measurements, we utilized a CNN-based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data recorded in south-central Alaska reveals that CNN classifies 98.1% of human-selected measurements as acceptable and revealed ~30% additional measurements.
For classifying different types of earthquakes, we utilized a CNN to classify natural earthquakes, mine collapses, and explosions using seismic waveforms recorded by 287 stations in Shandong Province, China. Cross-validation is employed to scan the whole dataset, and the measurements with different labels between human and the CNN are manually assessed and kept, corrected, or abandoned in the dataset. Testing with the corrected dataset, the classification accuracies of the three types of events increase from 97.3% to 99.2% for earthquakes, from 84.9% to 95.8% for mine collapses, and from 93.6% to 98.1% for explosions\"--Abstract, p. iv
"]},{"key":"dc:title","label":"Title","values":["APPLICATION OF MACHINE LEARNING IN GEOPHYSICS: RANKING TELESEISMIC SHEAR WAVE SPLITTING MEASUREMENTS AND CLASSIFYING DIFFERENT TYPES OF EARTHQUAKES"]}]}],"canonical_facts":{"dc:creator":["Zhang, Yanwei"],"dc:description.abstract":["\"During the past decades, applications of Machine Learning have been explosively developed to solve various academic and industrial problems, and over-human performance has been shown in diverse areas. In geophysical research, Machine Learning, especially Convolutional Neural Network (CNN), has been applied in numerous studies and demonstrated considerable potential. In this study, we applied CNN to solve two geophysical problems, ranking teleseismic shear splitting (SWS) measurements and classifying different types of earthquakes.
For ranking teleseismic SWS measurements, we utilized a CNN-based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data recorded in south-central Alaska reveals that CNN classifies 98.1% of human-selected measurements as acceptable and revealed ~30% additional measurements.
For classifying different types of earthquakes, we utilized a CNN to classify natural earthquakes, mine collapses, and explosions using seismic waveforms recorded by 287 stations in Shandong Province, China. Cross-validation is employed to scan the whole dataset, and the measurements with different labels between human and the CNN are manually assessed and kept, corrected, or abandoned in the dataset. Testing with the corrected dataset, the classification accuracies of the three types of events increase from 97.3% to 99.2% for earthquakes, from 84.9% to 95.8% for mine collapses, and from 93.6% to 98.1% for explosions\"--Abstract, p. iv
"],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/3245"],"dc:subject":["Convolutional Neural Network","Earthquake Classification","Machine Learning","Seismology","Shear Wave Splitting","Earth Sciences","Geology","Geophysics and Seismology","Physical Sciences and Mathematics"],"dc:title":["APPLICATION OF MACHINE LEARNING IN GEOPHYSICS: RANKING TELESEISMIC SHEAR WAVE SPLITTING MEASUREMENTS AND CLASSIFYING DIFFERENT TYPES OF EARTHQUAKES"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. D. in Geology and Geophysics"],"thesis:institution_name":["Missouri University of Science and Technology"]},"updated_at":"2026-07-24T03:18:18Z"}