Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Ernesto Sandoval-Curiel"'
Publikováno v:
GEOPHYSICS. 88:K51-K68
We develop a novel physics-adaptive machine-learning (ML) inversion scheme showing optimal generalization capabilities for field data applications. We apply the physics-driven deep-learning inversion to a massive helicopter-borne transient electromag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c22cafe166d6279a679944b2fd3c8b51
https://doi.org/10.1190/geo2022-0407.1
https://doi.org/10.1190/geo2022-0407.1
Publikováno v:
Inverse Problems.
Accurate near-surface characterization and velocity model building are important for a number of geotechnical and geophysical applications. 3D full waveform inversion can be used to generate a detailed velocity model, but must be provided with a good
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82c5ebfbde2580029684f511bd483f7a
https://doi.org/10.1088/1361-6420/acda59
https://doi.org/10.1088/1361-6420/acda59
Publikováno v:
The Leading Edge. 41:313-321
Sand-covered areas, such as desert environments, pose challenges to seismic imaging for resource exploration and monitoring. Aeolian sand dunes provide extreme variations of elastic parameters causing, among other effects, nonlinear velocity gradient
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b3da0953007c544396d57e74de9b3173
https://doi.org/10.1190/tle41050313.1
https://doi.org/10.1190/tle41050313.1
Publikováno v:
Second International Meeting for Applied Geoscience & Energy.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4708bbcb62bb56a260b0f87752872a3
https://doi.org/10.1190/image2022-3746223.1
https://doi.org/10.1190/image2022-3746223.1
Publikováno v:
Second International Meeting for Applied Geoscience & Energy.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c797bcf758c215012d25a24a496cb8a9
https://doi.org/10.1190/image2022-3730253.1
https://doi.org/10.1190/image2022-3730253.1
Publikováno v:
Second International Meeting for Applied Geoscience & Energy.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a31fc5227a2aa3e4203eb9cc2f1269f7
https://doi.org/10.1190/image2022-3745792.1
https://doi.org/10.1190/image2022-3745792.1
Publikováno v:
GEOPHYSICS. 86:E209-E224
Machine learning, and specifically deep-learning (DL) techniques applied to geophysical inverse problems, is an attractive subject, which has promising potential and, at the same time, presents some challenges in practical implementation. Some obstac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0fbcdb3f7e5c757393237264d08f5a69
https://doi.org/10.1190/geo2020-0760.1
https://doi.org/10.1190/geo2020-0760.1
Publikováno v:
GEOPHYSICS. 86:U15-U29
Land seismic velocity modeling is a difficult task largely related to the description of the near-surface complexities. Full-waveform inversion (FWI) is the method of choice for achieving high-resolution velocity mapping, but its application to land
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a1392be75ea17e52a31d18f134478759
https://doi.org/10.1190/geo2020-0474.1
https://doi.org/10.1190/geo2020-0474.1
Publikováno v:
GEOPHYSICS. 85:V169-V181
We have developed a new framework for performing surface-consistent amplitude balancing and deconvolution of the near-surface attenuation response. Both approaches rely on the early arrival waveform of a seismic recording, which corresponds to the re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::459e346f0c7104619f52e6a5dfc4f538
https://doi.org/10.1190/geo2019-0443.1
https://doi.org/10.1190/geo2019-0443.1
Publikováno v:
Sixth International Conference on Engineering Geophysics, Virtual, 25–28 October 2021.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::951f4b55fb0ae518055ecd58779cea62
https://doi.org/10.1190/iceg2021-022.1
https://doi.org/10.1190/iceg2021-022.1