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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Veröffentlicht in:Journal of geophysical research. Solid earth 2023-06, Vol.128 (6), p.n/a
Hauptverfasser: Sadler, B., Pulliam, J.
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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
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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 &gt; 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><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. 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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 &gt; 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. ; 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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 &gt; 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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