Modeling Crustal Structure in the Permian Basin by Waveform‐Matching P Receiver Functions and Autocorrelograms With Particle Swarm Optimization
We model basin and Moho structure in the Permian Basin region of west Texas and southeastern New Mexico using a method for waveform matching via global optimization of P‐to‐S receiver functions, vertical autocorrelograms, and horizontal autocorrelograms. The algorithm is driven by Particle Swarm Opt...
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description | We model basin and Moho structure in the Permian Basin region of west Texas and southeastern New Mexico using a method for waveform matching via global optimization of P‐to‐S receiver functions, vertical autocorrelograms, and horizontal autocorrelograms. The algorithm is driven by Particle Swarm Optimization, whose search history can be used to assess the strength of data constraints on model parameters. A common drawback in receiver function modeling is the need to assume a value for Vp before Vs and layer thickness can be estimated. But constraints on Vp can be provided by vertical autocorrelograms of teleseismic arrivals, which detect reverberating P waves, because the phase delay times depend only on Vp and interface depth. P‐to‐S receiver functions and vertical and radial autocorrelograms are computed for M > 5.5 events at 30°–100° epicentral distance, then edited and binned by ray parameter. Synthetic seismograms are computed for layered models using a 1D reflectivity method. The free parameters in the algorithm include basin depth, basin Vp, basin Vp/Vs, and thickness and Vp/Vs of the crystalline crust. Where vertical autocorrelograms prove insufficient for determining basin Vp, Vp is assumed from nearby stations that were modeled successfully. We find an average basin Vp of 4.57 km/s and basin depths to ∼8 km. Moho depths are generally 40–48 km. A basin depth of at least 3.5 km is typically needed to produce sufficient phase separation to allow an average Vp to be determined with confidence. Modeling autocorrelograms jointly with receiver functions therefore improves constraints on deep basin structure.
Plain Language Summary
The Permian Basin is a sedimentary basin in west Texas and southeastern New Mexico that harbors a large reservoir of oil and gas. In this study, distant earthquakes recorded by seismometers in the Permian Basin region are used as sources for imaging the structure of both the Permian Basin and the Earth's crust beneath the Basin. Processing techniques, including receiver function and autocorrelogram computation, are used to strip the individual earthquake signature from the recorded earthquakes, leaving only the signal due to the geologic structure beneath the seismic stations. Structure beneath each of the stations is then modeled by producing receiver functions and autocorrelograms that would be expected from different geologic structure to best match the real data. We find an average basin P wave velocity of 4.57 km/s and ba |
doi_str_mv | 10.1029/2023JB026730 |
format | Article |
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Plain Language Summary
The Permian Basin is a sedimentary basin in west Texas and southeastern New Mexico that harbors a large reservoir of oil and gas. In this study, distant earthquakes recorded by seismometers in the Permian Basin region are used as sources for imaging the structure of both the Permian Basin and the Earth's crust beneath the Basin. Processing techniques, including receiver function and autocorrelogram computation, are used to strip the individual earthquake signature from the recorded earthquakes, leaving only the signal due to the geologic structure beneath the seismic stations. Structure beneath each of the stations is then modeled by producing receiver functions and autocorrelograms that would be expected from different geologic structure to best match the real data. We find an average basin P wave velocity of 4.57 km/s and basin depths to ∼8 km. Crustal depths are generally 40–48 km. A basin depth of at least 3.5 km is typically needed for basin P wave velocities to be reasonably modeled.
Key Points
Waveform modeling receiver functions and autocorrelograms allows for determination of Vp, Vp/Vs, and contrast depth
Waveform modeling is applied to the Permian Basin to model basin and crustal structure
This study found an average basin P wave velocity of 4.57 km/s, basin depths to ∼8 km, and Moho depths 40–48 km</description><identifier>ISSN: 2169-9313</identifier><identifier>EISSN: 2169-9356</identifier><identifier>DOI: 10.1029/2023JB026730</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Algorithms ; autocorrelations ; Computation ; Constraint modelling ; Crustal structure ; Delay time ; Depth ; Depth perception ; Earth crust ; Earthquakes ; Elastic waves ; Geological structures ; Geology ; Geophysics ; Global optimization ; Matching ; Model basins ; Modelling ; Moho ; Optimization ; P waves ; Parameters ; Particle swarm optimization ; Permian ; Phase separation ; receiver functions ; Reflectance ; Sedimentary basins ; Seismic activity ; Seismic velocities ; Seismic wave velocities ; Seismograms ; Seismometers ; Thickness ; Wave velocity ; waveform modeling ; Waveforms</subject><ispartof>Journal of geophysical research. Solid earth, 2023-06, Vol.128 (6), p.n/a</ispartof><rights>2023. The Authors.</rights><rights>2023. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3021-9d2efeec4171c913f08e823ad6bd67dc261f1820b1e9b2355d1247229b5295c3</cites><orcidid>0000-0001-7661-359X ; 0000-0002-6244-2141</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2023JB026730$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2023JB026730$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Sadler, B.</creatorcontrib><creatorcontrib>Pulliam, J.</creatorcontrib><title>Modeling Crustal Structure in the Permian Basin by Waveform‐Matching P Receiver Functions and Autocorrelograms With Particle Swarm Optimization</title><title>Journal of geophysical research. Solid earth</title><description>We model basin and Moho structure in the Permian Basin region of west Texas and southeastern New Mexico using a method for waveform matching via global optimization of P‐to‐S receiver functions, vertical autocorrelograms, and horizontal autocorrelograms. The algorithm is driven by Particle Swarm Optimization, whose search history can be used to assess the strength of data constraints on model parameters. A common drawback in receiver function modeling is the need to assume a value for Vp before Vs and layer thickness can be estimated. But constraints on Vp can be provided by vertical autocorrelograms of teleseismic arrivals, which detect reverberating P waves, because the phase delay times depend only on Vp and interface depth. P‐to‐S receiver functions and vertical and radial autocorrelograms are computed for M > 5.5 events at 30°–100° epicentral distance, then edited and binned by ray parameter. Synthetic seismograms are computed for layered models using a 1D reflectivity method. The free parameters in the algorithm include basin depth, basin Vp, basin Vp/Vs, and thickness and Vp/Vs of the crystalline crust. Where vertical autocorrelograms prove insufficient for determining basin Vp, Vp is assumed from nearby stations that were modeled successfully. We find an average basin Vp of 4.57 km/s and basin depths to ∼8 km. Moho depths are generally 40–48 km. A basin depth of at least 3.5 km is typically needed to produce sufficient phase separation to allow an average Vp to be determined with confidence. Modeling autocorrelograms jointly with receiver functions therefore improves constraints on deep basin structure.
Plain Language Summary
The Permian Basin is a sedimentary basin in west Texas and southeastern New Mexico that harbors a large reservoir of oil and gas. In this study, distant earthquakes recorded by seismometers in the Permian Basin region are used as sources for imaging the structure of both the Permian Basin and the Earth's crust beneath the Basin. Processing techniques, including receiver function and autocorrelogram computation, are used to strip the individual earthquake signature from the recorded earthquakes, leaving only the signal due to the geologic structure beneath the seismic stations. Structure beneath each of the stations is then modeled by producing receiver functions and autocorrelograms that would be expected from different geologic structure to best match the real data. We find an average basin P wave velocity of 4.57 km/s and basin depths to ∼8 km. Crustal depths are generally 40–48 km. A basin depth of at least 3.5 km is typically needed for basin P wave velocities to be reasonably modeled.
Key Points
Waveform modeling receiver functions and autocorrelograms allows for determination of Vp, Vp/Vs, and contrast depth
Waveform modeling is applied to the Permian Basin to model basin and crustal structure
This study found an average basin P wave velocity of 4.57 km/s, basin depths to ∼8 km, and Moho depths 40–48 km</description><subject>Algorithms</subject><subject>autocorrelations</subject><subject>Computation</subject><subject>Constraint modelling</subject><subject>Crustal structure</subject><subject>Delay time</subject><subject>Depth</subject><subject>Depth perception</subject><subject>Earth crust</subject><subject>Earthquakes</subject><subject>Elastic waves</subject><subject>Geological structures</subject><subject>Geology</subject><subject>Geophysics</subject><subject>Global optimization</subject><subject>Matching</subject><subject>Model basins</subject><subject>Modelling</subject><subject>Moho</subject><subject>Optimization</subject><subject>P waves</subject><subject>Parameters</subject><subject>Particle swarm optimization</subject><subject>Permian</subject><subject>Phase separation</subject><subject>receiver functions</subject><subject>Reflectance</subject><subject>Sedimentary basins</subject><subject>Seismic activity</subject><subject>Seismic velocities</subject><subject>Seismic wave velocities</subject><subject>Seismograms</subject><subject>Seismometers</subject><subject>Thickness</subject><subject>Wave velocity</subject><subject>waveform modeling</subject><subject>Waveforms</subject><issn>2169-9313</issn><issn>2169-9356</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp9kE1KA0EQhQdRUDQ7D9Dg1mh3deanlyb4i8Gggsuhp6fGtMxMx-oeJa48gl7RkzghIq6sTRWP79WDF0X7gh8JDuoYOMirMYcklXwj2gGRqKGScbL5ewu5HQ28f-L9ZL0kRjvR59SVWNv2kU2o80HX7C5QZ0JHyGzLwhzZDKmxumVj7XulWLIH_YKVo-br_WOqg5mv3DN2iwbtCxI761oTrGs9023JTrrgjCPC2j2Sbjx7sGHOZpqCNTWyu1dNDbtZBNvYN72y7UVbla49Dn72bnR_dno_uRhe35xfTk6uh0ZyEENVAlaIZiRSYZSQFc8wA6nLpCiTtDSQiEpkwAuBqgAZx6WAUQqgihhUbORudLB-uyD33KEP-ZPrqO0Tc8hAqSwRKe-pwzVlyHlPWOULso2mZS54vqo9_1t7j8s1_mprXP7L5lfnt-M4kSDkN3O-hfI</recordid><startdate>202306</startdate><enddate>202306</enddate><creator>Sadler, B.</creator><creator>Pulliam, J.</creator><general>Blackwell Publishing Ltd</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-7661-359X</orcidid><orcidid>https://orcid.org/0000-0002-6244-2141</orcidid></search><sort><creationdate>202306</creationdate><title>Modeling Crustal Structure in the Permian Basin by Waveform‐Matching P Receiver Functions and Autocorrelograms With Particle Swarm Optimization</title><author>Sadler, B. ; Pulliam, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3021-9d2efeec4171c913f08e823ad6bd67dc261f1820b1e9b2355d1247229b5295c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>autocorrelations</topic><topic>Computation</topic><topic>Constraint modelling</topic><topic>Crustal structure</topic><topic>Delay time</topic><topic>Depth</topic><topic>Depth perception</topic><topic>Earth crust</topic><topic>Earthquakes</topic><topic>Elastic waves</topic><topic>Geological structures</topic><topic>Geology</topic><topic>Geophysics</topic><topic>Global optimization</topic><topic>Matching</topic><topic>Model basins</topic><topic>Modelling</topic><topic>Moho</topic><topic>Optimization</topic><topic>P waves</topic><topic>Parameters</topic><topic>Particle swarm optimization</topic><topic>Permian</topic><topic>Phase separation</topic><topic>receiver functions</topic><topic>Reflectance</topic><topic>Sedimentary basins</topic><topic>Seismic activity</topic><topic>Seismic velocities</topic><topic>Seismic wave velocities</topic><topic>Seismograms</topic><topic>Seismometers</topic><topic>Thickness</topic><topic>Wave velocity</topic><topic>waveform modeling</topic><topic>Waveforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sadler, B.</creatorcontrib><creatorcontrib>Pulliam, J.</creatorcontrib><collection>Wiley Online Library (Open Access Collection)</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Journal of geophysical research. Solid earth</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sadler, B.</au><au>Pulliam, J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling Crustal Structure in the Permian Basin by Waveform‐Matching P Receiver Functions and Autocorrelograms With Particle Swarm Optimization</atitle><jtitle>Journal of geophysical research. Solid earth</jtitle><date>2023-06</date><risdate>2023</risdate><volume>128</volume><issue>6</issue><epage>n/a</epage><issn>2169-9313</issn><eissn>2169-9356</eissn><abstract>We model basin and Moho structure in the Permian Basin region of west Texas and southeastern New Mexico using a method for waveform matching via global optimization of P‐to‐S receiver functions, vertical autocorrelograms, and horizontal autocorrelograms. The algorithm is driven by Particle Swarm Optimization, whose search history can be used to assess the strength of data constraints on model parameters. A common drawback in receiver function modeling is the need to assume a value for Vp before Vs and layer thickness can be estimated. But constraints on Vp can be provided by vertical autocorrelograms of teleseismic arrivals, which detect reverberating P waves, because the phase delay times depend only on Vp and interface depth. P‐to‐S receiver functions and vertical and radial autocorrelograms are computed for M > 5.5 events at 30°–100° epicentral distance, then edited and binned by ray parameter. Synthetic seismograms are computed for layered models using a 1D reflectivity method. The free parameters in the algorithm include basin depth, basin Vp, basin Vp/Vs, and thickness and Vp/Vs of the crystalline crust. Where vertical autocorrelograms prove insufficient for determining basin Vp, Vp is assumed from nearby stations that were modeled successfully. We find an average basin Vp of 4.57 km/s and basin depths to ∼8 km. Moho depths are generally 40–48 km. A basin depth of at least 3.5 km is typically needed to produce sufficient phase separation to allow an average Vp to be determined with confidence. Modeling autocorrelograms jointly with receiver functions therefore improves constraints on deep basin structure.
Plain Language Summary
The Permian Basin is a sedimentary basin in west Texas and southeastern New Mexico that harbors a large reservoir of oil and gas. In this study, distant earthquakes recorded by seismometers in the Permian Basin region are used as sources for imaging the structure of both the Permian Basin and the Earth's crust beneath the Basin. Processing techniques, including receiver function and autocorrelogram computation, are used to strip the individual earthquake signature from the recorded earthquakes, leaving only the signal due to the geologic structure beneath the seismic stations. Structure beneath each of the stations is then modeled by producing receiver functions and autocorrelograms that would be expected from different geologic structure to best match the real data. We find an average basin P wave velocity of 4.57 km/s and basin depths to ∼8 km. Crustal depths are generally 40–48 km. A basin depth of at least 3.5 km is typically needed for basin P wave velocities to be reasonably modeled.
Key Points
Waveform modeling receiver functions and autocorrelograms allows for determination of Vp, Vp/Vs, and contrast depth
Waveform modeling is applied to the Permian Basin to model basin and crustal structure
This study found an average basin P wave velocity of 4.57 km/s, basin depths to ∼8 km, and Moho depths 40–48 km</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1029/2023JB026730</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-7661-359X</orcidid><orcidid>https://orcid.org/0000-0002-6244-2141</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms autocorrelations Computation Constraint modelling Crustal structure Delay time Depth Depth perception Earth crust Earthquakes Elastic waves Geological structures Geology Geophysics Global optimization Matching Model basins Modelling Moho Optimization P waves Parameters Particle swarm optimization Permian Phase separation receiver functions Reflectance Sedimentary basins Seismic activity Seismic velocities Seismic wave velocities Seismograms Seismometers Thickness Wave velocity waveform modeling Waveforms |
title | Modeling Crustal Structure in the Permian Basin by Waveform‐Matching P Receiver Functions and Autocorrelograms With Particle Swarm Optimization |
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