{"id":"https://openalex.org/W2746491916","doi":"https://doi.org/10.1109/aipr.2016.8010599","title":"Seismic signal analysis using multi-scale/multi-resolution transformations","display_name":"Seismic signal analysis using multi-scale/multi-resolution transformations","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2746491916","doi":"https://doi.org/10.1109/aipr.2016.8010599","mag":"2746491916"},"language":"en","primary_location":{"id":"doi:10.1109/aipr.2016.8010599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2016.8010599","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5027350885","display_name":"Millicent Thomas","orcid":null},"institutions":[{"id":"https://openalex.org/I36819085","display_name":"Northwest University","ror":"https://ror.org/00y7snj24","country_code":"US","type":"education","lineage":["https://openalex.org/I36819085"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Millicent Thomas","raw_affiliation_strings":["Northwest University Kirkland, WA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwest University Kirkland, WA","institution_ids":["https://openalex.org/I36819085"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088304347","display_name":"Joseph Raquepas","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135209","display_name":"Centerforce","ror":"https://ror.org/02tcncc65","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210135209"]},{"id":"https://openalex.org/I4388482708","display_name":"U.S. Air Force Research Laboratory Materials and Manufacturing Directorate","ror":"https://ror.org/0584m4844","country_code":null,"type":"funder","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4388482708","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph Raquepas","raw_affiliation_strings":["Airforce Research Lab Rome, NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Airforce Research Lab Rome, NY","institution_ids":["https://openalex.org/I4210135209","https://openalex.org/I4388482708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082241487","display_name":"Adam Lutz","orcid":null},"institutions":[{"id":"https://openalex.org/I200885203","display_name":"Indiana University of Pennsylvania","ror":"https://ror.org/0511cmw96","country_code":"US","type":"education","lineage":["https://openalex.org/I200885203"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adam Lutz","raw_affiliation_strings":["Indiana University of Pennsylvania Indiana, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indiana University of Pennsylvania Indiana, PA","institution_ids":["https://openalex.org/I200885203"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088839879","display_name":"Soundararajan Ezekiel","orcid":null},"institutions":[{"id":"https://openalex.org/I200885203","display_name":"Indiana University of Pennsylvania","ror":"https://ror.org/0511cmw96","country_code":"US","type":"education","lineage":["https://openalex.org/I200885203"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Soundararajan Ezekiel","raw_affiliation_strings":["Indiana University of Pennsylvania, Indiana, PA, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indiana University of Pennsylvania, Indiana, PA, US","institution_ids":["https://openalex.org/I200885203"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.2515194,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"51","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9945999979972839,"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/wavelet","display_name":"Wavelet","score":0.8237952589988708},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.6968384385108948},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.6608233451843262},{"id":"https://openalex.org/keywords/second-generation-wavelet-transform","display_name":"Second-generation wavelet transform","score":0.6576884984970093},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6281111836433411},{"id":"https://openalex.org/keywords/harmonic-wavelet-transform","display_name":"Harmonic wavelet transform","score":0.61054527759552},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.6102733612060547},{"id":"https://openalex.org/keywords/wavelet-packet-decomposition","display_name":"Wavelet packet decomposition","score":0.5710762143135071},{"id":"https://openalex.org/keywords/curvelet","display_name":"Curvelet","score":0.5674794912338257},{"id":"https://openalex.org/keywords/stationary-wavelet-transform","display_name":"Stationary wavelet transform","score":0.5336279273033142},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4963269829750061},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.4570666551589966},{"id":"https://openalex.org/keywords/lifting-scheme","display_name":"Lifting scheme","score":0.4502132534980774},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44439616799354553},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.4334453344345093},{"id":"https://openalex.org/keywords/contourlet","display_name":"Contourlet","score":0.4255527853965759},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4238310754299164},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31385746598243713},{"id":"https://openalex.org/keywords/digital-signal-processing","display_name":"Digital signal processing","score":0.23170402646064758}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.8237952589988708},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.6968384385108948},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.6608233451843262},{"id":"https://openalex.org/C111350171","wikidata":"https://www.wikidata.org/wiki/Q7443700","display_name":"Second-generation wavelet transform","level":5,"score":0.6576884984970093},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6281111836433411},{"id":"https://openalex.org/C1109138","wikidata":"https://www.wikidata.org/wiki/Q3280930","display_name":"Harmonic wavelet transform","level":5,"score":0.61054527759552},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.6102733612060547},{"id":"https://openalex.org/C155777637","wikidata":"https://www.wikidata.org/wiki/Q2736187","display_name":"Wavelet packet decomposition","level":4,"score":0.5710762143135071},{"id":"https://openalex.org/C131720326","wikidata":"https://www.wikidata.org/wiki/Q5196075","display_name":"Curvelet","level":4,"score":0.5674794912338257},{"id":"https://openalex.org/C73339587","wikidata":"https://www.wikidata.org/wiki/Q1375942","display_name":"Stationary wavelet transform","level":5,"score":0.5336279273033142},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4963269829750061},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.4570666551589966},{"id":"https://openalex.org/C199550912","wikidata":"https://www.wikidata.org/wiki/Q3238415","display_name":"Lifting scheme","level":5,"score":0.4502132534980774},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44439616799354553},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.4334453344345093},{"id":"https://openalex.org/C20479862","wikidata":"https://www.wikidata.org/wiki/Q5165589","display_name":"Contourlet","level":4,"score":0.4255527853965759},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4238310754299164},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31385746598243713},{"id":"https://openalex.org/C84462506","wikidata":"https://www.wikidata.org/wiki/Q173142","display_name":"Digital signal processing","level":2,"score":0.23170402646064758},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr.2016.8010599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2016.8010599","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322439","display_name":"Northwest University","ror":"https://ror.org/00z3td547"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1501570648","https://openalex.org/W1658679052","https://openalex.org/W1663998522","https://openalex.org/W1971103086","https://openalex.org/W1986830331","https://openalex.org/W2037198541","https://openalex.org/W2060422862","https://openalex.org/W2066462711","https://openalex.org/W2096684483","https://openalex.org/W2115755118","https://openalex.org/W2117853853","https://openalex.org/W2131408363","https://openalex.org/W2132984323","https://openalex.org/W2206436926","https://openalex.org/W2325630260","https://openalex.org/W2550523977","https://openalex.org/W3214074647","https://openalex.org/W4300017134","https://openalex.org/W6803975782"],"related_works":["https://openalex.org/W1588899229","https://openalex.org/W1976022598","https://openalex.org/W2386482837","https://openalex.org/W2355889335","https://openalex.org/W2023142747","https://openalex.org/W2019515987","https://openalex.org/W2463313577","https://openalex.org/W2025989299","https://openalex.org/W2085792030","https://openalex.org/W2363631399"],"abstract_inverted_index":{"Fast":[0,198],"Fourier":[1,76,199],"Transforms":[2],"have":[3,59,82,101,132,147],"been":[4,23,102,149],"used":[5,25],"since":[6],"the":[7,17,34,47,75,93,105,118,124,138,163,179,192,210,217,245],"early":[8],"1960s":[9],"as":[10,26,54,63,92,117,150,206,208,229],"a":[11,44,155,188,241],"method":[12,27,157,177],"of":[13,43,145,158],"processing":[14],"signals.":[15],"Since":[16],"1990s":[18],"wavelet":[19,121,128,139,166],"transformation":[20,122,238],"has":[21],"also":[22],"routinely":[24],"for":[28,137,212,224],"signal":[29,160,204],"processing.":[30],"Their":[31],"limitations":[32],"include":[33],"inability":[35],"to":[36,65,86,104,108,168,191],"detect":[37],"contours,":[38],"curves":[39],"and":[40,56,70,78,97,123,134,170,197,247],"directional":[41],"information":[42,67],"signal.":[45],"In":[46],"past":[48],"few":[49],"years,":[50],"new":[51],"approaches":[52],"such":[53,91,116,228],"multi-scale":[55,140],"multi-resolution":[57],"transformations":[58,81,146,167],"become":[60],"more":[61,88,164],"prevalent":[62],"methods":[64,90,115,239],"extrapolate":[66,171],"from":[68],"signals":[69],"images.":[71],"With":[72],"roots":[73],"in":[74,114,226],"transform":[77],"wavelet,":[79],"multi-scale/multi-resolution":[80],"recently":[83],"given":[84],"rise":[85],"even":[87],"advanced":[89,165],"bandelet,":[94],"contourlet,":[95],"ridgelet,":[96],"curvelet.[24]":[98],"Additionally,":[99],"advancements":[100],"made":[103],"standard":[106],"wavelets":[107],"improve":[109],"their":[110],"analytical":[111],"capabilities,":[112],"resulting":[113],"dual-tree":[119],"discrete":[120,127],"dual":[125],"density":[126],"transformation.":[129],"Many":[130],"studies":[131],"developed":[133],"validate":[135],"algorithms":[136],"analysis":[141,161,176],"but":[142],"other":[143,213,237],"types":[144],"not":[148],"thoroughly":[151],"studied.":[152],"We":[153],"propose":[154],"novel":[156],"seismic":[159,172,203],"using":[162],"identify":[169],"information.":[173],"The":[174,232],"proposed":[175],"makes":[178],"Double":[180],"Density":[181],"Dual":[182],"Tree":[183],"Discrete":[184,193],"Wavelet":[185],"Transform":[186,195,200],"(D3WT)":[187],"viable":[189],"alternative":[190],"Wavelength":[194],"(DWT)":[196],"(FFT)":[201],"during":[202],"processing,":[205],"well":[207],"paving":[209],"way":[211],"multiscale":[214],"transforms.[26]":[215],"Further,":[216],"outcome":[218],"can":[219],"then":[220],"be":[221],"statistically":[222],"analyzed":[223],"use":[225],"applications":[227],"earthquake":[230],"prediction.":[231],"initial":[233],"results":[234],"indicate":[235],"that":[236],"yield":[240],"better":[242],"prediction":[243],"than":[244],"DWT":[246],"FFT.":[248]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
