{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/2184"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/2184","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Automatic First Break Detection by Spectral Decomposition Using Minimum Uncertainty Wavelets","abstract":"Seismic Signal Processing can be effectively utilized to determine micro- seismic events. With the advances in hydraulic fracturing techniques, first break detection has become really important in locating micro-seismic events. The measured data collected gathers far more information than can be extracted by human operators and whose interpretation can consume a lot of time. The transforma- tion in the computational efficiency suggests the involvement of computers in interpreting the measured data. We suggest a new method of first break detec- tion that is based on time-frequency spectral decomposition method and utilizes the Cn Transform and the Super-Gaussian μ wavelets. We tested our method on lab data with various signals and first arrival time was determined. The results were compared to the manual detection and our method had an accuracy of 0.6 μ seconds. The results indicate that our method is robust and is successful in detecting the first arrival time automatically.","abstract_html":"Seismic Signal Processing can be effectively utilized to determine micro- seismic events. With the advances in hydraulic fracturing techniques, first break detection has become really important in locating micro-seismic events. The measured data collected gathers far more information than can be extracted by human operators and whose interpretation can consume a lot of time. The transforma- tion in the computational efficiency suggests the involvement of computers in interpreting the measured data. We suggest a new method of first break detec- tion that is based on time-frequency spectral decomposition method and utilizes the Cn Transform and the Super-Gaussian μ wavelets. We tested our method on lab data with various signals and first arrival time was determined. The results were compared to the manual detection and our method had an accuracy of 0.6 μ seconds. The results indicate that our method is robust and is successful in detecting the first arrival time automatically.","abstract_has_math":false,"creators":["Kapur, Sunil 1988-"],"institution":"University of Houston","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Kouri, Donald J."],"committee_chairs":[],"committee_members":["Rao, Jagannatha R.","Ardebili, Haleh"],"year":2012,"date_issued":"2012-12","date_published":"2012-12","updated_at":"2026-07-24T02:31:47Z","subjects":["Super Gaussian Mu-Wavelets","First Break Detection","Spectral decomposition","Heisenberg Uncertainty Principle","Singular Value Decomposition Method","Cn transform"],"languages":["eng"],"rights":["The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s)."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10657/2184","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kouri, Donald J."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Rao, Jagannatha R.","Ardebili, Haleh"]},{"key":"dc:creator","label":"Author","values":["Kapur, Sunil 1988-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-02-15T20:06:37Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-02-15T20:06:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2012-12"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"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":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Super Gaussian Mu-Wavelets","First Break Detection","Spectral decomposition","Heisenberg Uncertainty Principle","Singular Value Decomposition Method","Cn transform"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s)."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10657/2184"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Seismic Signal Processing can be effectively utilized to determine micro- seismic events. With the advances in hydraulic fracturing techniques, first break detection has become really important in locating micro-seismic events. The measured data collected gathers far more information than can be extracted by human operators and whose interpretation can consume a lot of time. The transforma- tion in the computational efficiency suggests the involvement of computers in interpreting the measured data. We suggest a new method of first break detec- tion that is based on time-frequency spectral decomposition method and utilizes the Cn Transform and the Super-Gaussian μ wavelets. We tested our method on lab data with various signals and first arrival time was determined. The results were compared to the manual detection and our method had an accuracy of 0.6 μ seconds. The results indicate that our method is robust and is successful in detecting the first arrival time automatically."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Automatic First Break Detection by Spectral Decomposition Using Minimum Uncertainty Wavelets"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kouri, Donald J."],"dc:contributor.committeemember":["Rao, Jagannatha R.","Ardebili, Haleh"],"dc:creator":["Kapur, Sunil 1988-"],"dc:date.accessioned":["2018-02-15T20:06:37Z"],"dc:date.available":["2018-02-15T20:06:37Z"],"dc:date.issued":["2012-12"],"dc:description.abstract":["Seismic Signal Processing can be effectively utilized to determine micro- seismic events. With the advances in hydraulic fracturing techniques, first break detection has become really important in locating micro-seismic events. The measured data collected gathers far more information than can be extracted by human operators and whose interpretation can consume a lot of time. The transforma- tion in the computational efficiency suggests the involvement of computers in interpreting the measured data. We suggest a new method of first break detec- tion that is based on time-frequency spectral decomposition method and utilizes the Cn Transform and the Super-Gaussian μ wavelets. We tested our method on lab data with various signals and first arrival time was determined. The results were compared to the manual detection and our method had an accuracy of 0.6 μ seconds. The results indicate that our method is robust and is successful in detecting the first arrival time automatically."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/10657/2184"],"dc:language.iso":["eng"],"dc:rights":["The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s)."],"dc:subject":["Super Gaussian Mu-Wavelets","First Break Detection","Spectral decomposition","Heisenberg Uncertainty Principle","Singular Value Decomposition Method","Cn transform"],"dc:title":["Automatic First Break Detection by Spectral Decomposition Using Minimum Uncertainty Wavelets"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:31:47Z"}