{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/8849"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/8849","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"An Auto-Picking Algorithm For The Detection of Clay Seams In Potash Mines Using GPR Data","abstract":"Potash mines can be in operation for up to one hundred (100) years. Maintaining a stable mine roof is critical for the safety of current and future underground mine operations. Visual clues on side walls, combined with the historical knowledge of the ore formation in a particular region, provide a primary but inconclusive identification of the presence and distance of clay seams from the mining zone. Clay seams are a weak point of possible failure and as such, a buffer distance needs to be maintained between the clay seam and the mining roof. In addition to safety, knowledge of the position of clay seams helps in the efficient allocation of operational resources and limits down time, which is crucial in maintaining an efficient operation. This thesis is focused on developing an auto-picking algorithm that tracks the distance of the 414-clay-seam from an underground potash mine roof. The developed algorithm is implemented on Ground Penetrating Radar (GPR) data. Three main processes are required: clustering, ratio analysis and derivative (CRD). These processes translate the knowledge of a trained geophysicist in determining the position of a clay seam using GPR data into an algorithm. The developed CRD algorithm is compared with an interpretation of an experienced geophysicist. The test results have ninety percent (90%) of the data having at least 91.5% accuracy with a standard deviation of 5.5%. Typically, a geophysicist would need about three hours to generate a result however, the CRD algorithm provides near real-time results. The CRD algorithm was tested on data from different mines and achieved similar accuracy. In addition, it also offers tunable features to increase the sensitivity as desired by the user.","abstract_html":"Potash mines can be in operation for up to one hundred (100) years. Maintaining a stable mine roof is critical for the safety of current and future underground mine operations. Visual clues on side walls, combined with the historical knowledge of the ore formation in a particular region, provide a primary but inconclusive identification of the presence and distance of clay seams from the mining zone. Clay seams are a weak point of possible failure and as such, a buffer distance needs to be maintained between the clay seam and the mining roof. In addition to safety, knowledge of the position of clay seams helps in the efficient allocation of operational resources and limits down time, which is crucial in maintaining an efficient operation. This thesis is focused on developing an auto-picking algorithm that tracks the distance of the 414-clay-seam from an underground potash mine roof. The developed algorithm is implemented on Ground Penetrating Radar (GPR) data. Three main processes are required: clustering, ratio analysis and derivative (CRD). These processes translate the knowledge of a trained geophysicist in determining the position of a clay seam using GPR data into an algorithm. The developed CRD algorithm is compared with an interpretation of an experienced geophysicist. The test results have ninety percent (90%) of the data having at least 91.5% accuracy with a standard deviation of 5.5%. Typically, a geophysicist would need about three hours to generate a result however, the CRD algorithm provides near real-time results. The CRD algorithm was tested on data from different mines and achieved similar accuracy. In addition, it also offers tunable features to increase the sensitivity as desired by the user.","abstract_has_math":false,"creators":["Briggs, Tokini Kiki"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Applied Science (MASc)","degree_level":"Master's","degree_discipline":"Engineering - Electronic Systems","degree_department":null,"school":null,"contributors":[],"advisors":["Paranjape, Raman"],"committee_chairs":[],"committee_members":["Bais, Abdul","Wang, Zhanle"],"year":2019,"date_issued":"2019-03","date_published":"2019-03","updated_at":"2026-07-24T04:03:38Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4390"],"render_values":[{"text":"https://doi.org/10.82465/4390","href":"https://doi.org/10.82465/4390","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/8849","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Paranjape, Raman"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Bais, Abdul","Wang, Zhanle"]},{"key":"dc:creator","label":"Author","values":["Briggs, Tokini Kiki"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-06-21T19:16:46Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-06-21T19:16:46Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-03"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering - Electronic Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/4390"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/8849"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Electronic Systems Engineering, University of Regina. ix, 95 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["Potash mines can be in operation for up to one hundred (100) years. Maintaining a stable mine roof is critical for the safety of current and future underground mine operations. Visual clues on side walls, combined with the historical knowledge of the ore formation in a particular region, provide a primary but inconclusive identification of the presence and distance of clay seams from the mining zone. Clay seams are a weak point of possible failure and as such, a buffer distance needs to be maintained between the clay seam and the mining roof. In addition to safety, knowledge of the position of clay seams helps in the efficient allocation of operational resources and limits down time, which is crucial in maintaining an efficient operation. This thesis is focused on developing an auto-picking algorithm that tracks the distance of the 414-clay-seam from an underground potash mine roof. The developed algorithm is implemented on Ground Penetrating Radar (GPR) data. Three main processes are required: clustering, ratio analysis and derivative (CRD). These processes translate the knowledge of a trained geophysicist in determining the position of a clay seam using GPR data into an algorithm. The developed CRD algorithm is compared with an interpretation of an experienced geophysicist. The test results have ninety percent (90%) of the data having at least 91.5% accuracy with a standard deviation of 5.5%. Typically, a geophysicist would need about three hours to generate a result however, the CRD algorithm provides near real-time results. The CRD algorithm was tested on data from different mines and achieved similar accuracy. In addition, it also offers tunable features to increase the sensitivity as desired by the user."]},{"key":"dc:title","label":"Title","values":["An Auto-Picking Algorithm For The Detection of Clay Seams In Potash Mines Using GPR Data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Paranjape, Raman"],"dc:contributor.committeemember":["Bais, Abdul","Wang, Zhanle"],"dc:creator":["Briggs, Tokini Kiki"],"dc:date.accessioned":["2019-06-21T19:16:46Z"],"dc:date.available":["2019-06-21T19:16:46Z"],"dc:date.issued":["2019-03"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Electronic Systems Engineering, University of Regina. ix, 95 p."],"dc:description.abstract":["Potash mines can be in operation for up to one hundred (100) years. Maintaining a stable mine roof is critical for the safety of current and future underground mine operations. Visual clues on side walls, combined with the historical knowledge of the ore formation in a particular region, provide a primary but inconclusive identification of the presence and distance of clay seams from the mining zone. Clay seams are a weak point of possible failure and as such, a buffer distance needs to be maintained between the clay seam and the mining roof. In addition to safety, knowledge of the position of clay seams helps in the efficient allocation of operational resources and limits down time, which is crucial in maintaining an efficient operation. This thesis is focused on developing an auto-picking algorithm that tracks the distance of the 414-clay-seam from an underground potash mine roof. The developed algorithm is implemented on Ground Penetrating Radar (GPR) data. Three main processes are required: clustering, ratio analysis and derivative (CRD). These processes translate the knowledge of a trained geophysicist in determining the position of a clay seam using GPR data into an algorithm. The developed CRD algorithm is compared with an interpretation of an experienced geophysicist. The test results have ninety percent (90%) of the data having at least 91.5% accuracy with a standard deviation of 5.5%. Typically, a geophysicist would need about three hours to generate a result however, the CRD algorithm provides near real-time results. The CRD algorithm was tested on data from different mines and achieved similar accuracy. In addition, it also offers tunable features to increase the sensitivity as desired by the user."],"dc:identifier.doi":["https://doi.org/10.82465/4390"],"dc:identifier.uri":["https://hdl.handle.net/10294/8849"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["An Auto-Picking Algorithm For The Detection of Clay Seams In Potash Mines Using GPR Data"],"dc:type":["master thesis"],"thesis:degree_discipline":["Engineering - Electronic Systems"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:38Z"}