{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/103864"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/103864","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Efficient Algorithms for Data Analytics in Geophysical Imaging","abstract":"Modern sensing systems such as distributed acoustic sensing (DAS) can produce massive quantities of geophysical data, often in remote locations. This presents significant challenges with regards to data storage and performing efficient analysis. To address this, we have designed and implemented efficient algorithms for two commonly utilized techniques in geophysical imaging: cross-correlations, and multichannel analysis of surface waves (MASW). Our cross-correlation algorithms operate directly in the wavelet domain on compressed data without requiring a reconstruction of the original signal, reducing memory costs and improving scalabiliy. Meanwhile, our MASW implementations make use of MPI parallelism and GPUs, and present a novel problem for the GPU.","abstract_html":"Modern sensing systems such as distributed acoustic sensing (DAS) can produce massive quantities of geophysical data, often in remote locations. This presents significant challenges with regards to data storage and performing efficient analysis. To address this, we have designed and implemented efficient algorithms for two commonly utilized techniques in geophysical imaging: cross-correlations, and multichannel analysis of surface waves (MASW). Our cross-correlation algorithms operate directly in the wavelet domain on compressed data without requiring a reconstruction of the original signal, reducing memory costs and improving scalabiliy. Meanwhile, our MASW implementations make use of MPI parallelism and GPUs, and present a novel problem for the GPU.","abstract_has_math":false,"creators":["Kump, Joseph Lee"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mathematics","degree_department":"Mathematics","school":null,"contributors":[],"advisors":[],"committee_chairs":["Martin, Eileen R."],"committee_members":["Embree, Mark P.","Hewett, Russell Joseph"],"year":2021,"date_issued":"2021-06-14","date_published":"2021-06-14","updated_at":"2026-07-22T22:20:07Z","subjects":["Algorithms","Wavelets","Cross-correlations","MASW","GPU"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:31151"],"render_values":[{"text":"vt_gsexam:31151","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/103864","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Martin, Eileen R."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Embree, Mark P.","Hewett, Russell Joseph"]},{"key":"dc:contributor.department","label":"Department","values":["Mathematics"]},{"key":"dc:creator","label":"Author","values":["Kump, Joseph Lee"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-06-15T08:01:16Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-06-15T08:01:16Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-06-14"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Algorithms","Wavelets","Cross-correlations","MASW","GPU"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:31151"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/103864"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Modern sensing systems such as distributed acoustic sensing (DAS) can produce massive quantities of geophysical data, often in remote locations. This presents significant challenges with regards to data storage and performing efficient analysis. To address this, we have designed and implemented efficient algorithms for two commonly utilized techniques in geophysical imaging: cross-correlations, and multichannel analysis of surface waves (MASW). Our cross-correlation algorithms operate directly in the wavelet domain on compressed data without requiring a reconstruction of the original signal, reducing memory costs and improving scalabiliy. Meanwhile, our MASW implementations make use of MPI parallelism and GPUs, and present a novel problem for the GPU."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Modern sensor designs make it easier to collect large quantities of seismic vibration data. While this data can provide valuable insight, it is difficult to effectively store and perform analysis on such a high data volume. We propose a few new, general-purpose algorithms that enable speedy use of two common methods in geophysical modeling and data analytics: crosscorrelation, which provides a measure of similarity between signals; and multichannel analysis of surface waves, which is a seismic imaging technique. Our algorithms take advantage of hardware and software typically available on modern computers, and the mathematical properties of these two methods."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Efficient Algorithms for Data Analytics in Geophysical Imaging"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Martin, Eileen R."],"dc:contributor.committeemember":["Embree, Mark P.","Hewett, Russell Joseph"],"dc:contributor.department":["Mathematics"],"dc:creator":["Kump, Joseph Lee"],"dc:date.accessioned":["2021-06-15T08:01:16Z"],"dc:date.available":["2021-06-15T08:01:16Z"],"dc:date.issued":["2021-06-14"],"dc:description.abstract":["Modern sensing systems such as distributed acoustic sensing (DAS) can produce massive quantities of geophysical data, often in remote locations. This presents significant challenges with regards to data storage and performing efficient analysis. To address this, we have designed and implemented efficient algorithms for two commonly utilized techniques in geophysical imaging: cross-correlations, and multichannel analysis of surface waves (MASW). Our cross-correlation algorithms operate directly in the wavelet domain on compressed data without requiring a reconstruction of the original signal, reducing memory costs and improving scalabiliy. Meanwhile, our MASW implementations make use of MPI parallelism and GPUs, and present a novel problem for the GPU."],"dc:description.abstractgeneral":["Modern sensor designs make it easier to collect large quantities of seismic vibration data. While this data can provide valuable insight, it is difficult to effectively store and perform analysis on such a high data volume. We propose a few new, general-purpose algorithms that enable speedy use of two common methods in geophysical modeling and data analytics: crosscorrelation, which provides a measure of similarity between signals; and multichannel analysis of surface waves, which is a seismic imaging technique. 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