{"id":"https://openalex.org/W2970524616","doi":"https://doi.org/10.3997/2214-4609.201901972","title":"Machine Learning For DAS Microseismic Event Detection","display_name":"Machine Learning For DAS Microseismic Event Detection","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2970524616","doi":"https://doi.org/10.3997/2214-4609.201901972","mag":"2970524616"},"language":"en","primary_location":{"id":"doi:10.3997/2214-4609.201901972","doi":"https://doi.org/10.3997/2214-4609.201901972","is_oa":false,"landing_page_url":"https://doi.org/10.3997/2214-4609.201901972","pdf_url":null,"source":{"id":"https://openalex.org/S4306499727","display_name":"EAGE Conference and Exhibition - Workshops and Fieldtrips","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"81st EAGE Conference and Exhibition 2019 Workshop Programme","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007015129","display_name":"Steve A. Horne","orcid":"https://orcid.org/0000-0003-3925-7519","observed_orcids":["https://orcid.org/0000-0003-3925-7519"]},"institutions":[{"id":"https://openalex.org/I4210092304","display_name":"Chevron (United Kingdom)","ror":"https://ror.org/00pvegc36","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210092304","https://openalex.org/I4210118014"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"S. Horne","raw_affiliation_strings":["Chevron (UK) Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chevron (UK) Ltd","institution_ids":["https://openalex.org/I4210092304"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021215088","display_name":"Alan F. Baird","orcid":"https://orcid.org/0000-0002-8740-9516","observed_orcids":["https://orcid.org/0000-0002-8740-9516"]},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"A. Baird","raw_affiliation_strings":["University of Bristol"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Bristol","institution_ids":["https://openalex.org/I36234482"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090468758","display_name":"Anna L. Stork","orcid":"https://orcid.org/0000-0001-5989-3788","observed_orcids":["https://orcid.org/0000-0001-5989-3788"]},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"A. Stork","raw_affiliation_strings":["University of Bristol"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Bristol","institution_ids":["https://openalex.org/I36234482"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002218937","display_name":"Garth Naldrett","orcid":"https://orcid.org/0009-0002-9905-7017","observed_orcids":["https://orcid.org/0009-0002-9905-7017"]},"institutions":[{"id":"https://openalex.org/I4210127206","display_name":"Silixa (United Kingdom)","ror":"https://ror.org/031jzas57","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210127206"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"G. Naldrett","raw_affiliation_strings":["Silixa Ltd"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Silixa Ltd","institution_ids":["https://openalex.org/I4210127206"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2671,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.71412301,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10635","display_name":"Hydraulic Fracturing and Reservoir Analysis","score":0.38260000944137573,"subfield":{"id":"https://openalex.org/subfields/2103","display_name":"Fuel Technology"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10635","display_name":"Hydraulic Fracturing and Reservoir Analysis","score":0.38260000944137573,"subfield":{"id":"https://openalex.org/subfields/2103","display_name":"Fuel Technology"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.35910001397132874,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11757","display_name":"Seismic Waves and Analysis","score":0.053700000047683716,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/distributed-acoustic-sensing","display_name":"distributed acoustic sensing","score":0.9319999814033508},{"id":"https://openalex.org/keywords/microseismic-event-detection","display_name":"microseismic event detection","score":0.9990000128746033},{"id":"https://openalex.org/keywords/fiber-optic-sensing","display_name":"fiber optic sensing","score":0.628000020980835},{"id":"https://openalex.org/keywords/big-data","display_name":"big data","score":0.7139999866485596},{"id":"https://openalex.org/keywords/apache-spark","display_name":"Apache Spark","score":0.8579999804496765},{"id":"https://openalex.org/keywords/hadoop","display_name":"Hadoop","score":0.9800000190734863},{"id":"https://openalex.org/keywords/haar-cascade","display_name":"Haar cascade","score":0.718999981880188},{"id":"https://openalex.org/keywords/computer-vision","display_name":"computer vision","score":0.8960000276565552},{"id":"https://openalex.org/keywords/seismic-data-processing","display_name":"seismic data processing","score":0.3330000042915344}],"concepts":[{"id":"https://openalex.org/C7266685","wikidata":"https://www.wikidata.org/wiki/Q1303250","display_name":"Microseism","level":2,"score":0.7874900698661804},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7047712802886963},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5091482996940613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43612948060035706},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12801507115364075},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.10540974140167236},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0712009072303772},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3997/2214-4609.201901972","doi":"https://doi.org/10.3997/2214-4609.201901972","is_oa":false,"landing_page_url":"https://doi.org/10.3997/2214-4609.201901972","pdf_url":null,"source":{"id":"https://openalex.org/S4306499727","display_name":"EAGE Conference and Exhibition - Workshops and Fieldtrips","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"81st EAGE Conference and Exhibition 2019 Workshop Programme","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G5310577178","display_name":"Fibre-optic distributed Acoustic Sensor Technology for seismic Monitoring During shale gas Extraction (FAST-MoDE)","funder_award_id":"NE/R014531/1","funder_id":"https://openalex.org/F4320334631","funder_display_name":"Natural Environment Research Council"}],"funders":[{"id":"https://openalex.org/F4320334631","display_name":"Natural Environment Research Council","ror":"https://ror.org/02b5d8509"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3081196182","https://openalex.org/W3034950392","https://openalex.org/W4319348185","https://openalex.org/W4401067636","https://openalex.org/W4386495304","https://openalex.org/W7123510283","https://openalex.org/W3196507497","https://openalex.org/W4226044217","https://openalex.org/W7208739093","https://openalex.org/W4390489423"],"abstract_inverted_index":{"Summary":[0],"Emerging":[1],"acquisition":[2],"systems":[3],"based":[4,127],"on":[5,128,165],"fiber":[6],"optic":[7],"technologies":[8,84,138],"such":[9,85,139],"as":[10,52,86,140,156],"Distributed":[11],"Acoustic":[12],"Sensing":[13],"are":[14,177],"enabling":[15],"dense":[16],"spatial":[17],"and":[18,39,64,89,168,176],"temporal":[19],"sampling":[20],"of":[21,37,55,59,101,135,188],"strain":[22],"fields.":[23],"This":[24],"has":[25],"resulted":[26],"in":[27,31,34],"a":[28,148,157],"large":[29],"increase":[30],"the":[32,35,40,56,98,136,186],"volume":[33,36],"data":[38,44,71,83,95,119,167],"rate":[41],"at":[42],"which":[43],"is":[45],"generated.":[46],"These":[47],"increases":[48],"can":[49,173],"be":[50,111],"interpreted":[51],"satisfying":[53],"two":[54],"3":[57],"\u2018V's":[58],"\u2018Big":[60],"Data\u2019":[61],"i.e.":[62],"Volume":[63],"Velocity":[65],"(the":[66],"third":[67],"V":[68],"refers":[69],"to":[70,92,110,113,116,123,146,179],"Variety).":[72],"In":[73],"this":[74,163,171],"presentation":[75],"we":[76,79,121,144],"show":[77],"how":[78],"have":[80,161],"used":[81,151],"big":[82],"Apache":[87,90],"Hadoop":[88],"Spark":[91],"tackle":[93],"these":[94,117],"issues":[96],"for":[97,152],"specific":[99],"problem":[100],"microseismic":[102,191],"event":[103,192],"detection.":[104,193],"Furthermore,":[105],"traditional":[106],"approaches":[107,126],"were":[108],"thought":[109],"unlikely":[112],"efficiently":[114],"scale":[115],"new":[118],"so":[120],"turned":[122],"machine":[124,182],"learning":[125,183],"computer":[129],"vision.":[130],"Rather":[131],"than":[132],"trying":[133],"state":[134],"art":[137],"Convolutional":[141],"Neural":[142],"Nets":[143],"decided":[145],"try":[147,180],"mature":[149],"technology":[150],"face":[153],"detection":[154],"known":[155],"Haar":[158],"Cascade.":[159],"We":[160],"tested":[162],"approach":[164,172],"field":[166],"found":[169],"that":[170],"work":[174],"well":[175],"motivated":[178],"newer":[181],"techniques":[184],"with":[185],"expectation":[187],"moving":[189],"beyond":[190]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2019-09-05T00:00:00"}
