Výsledky vyhledávání - "George A. McMechan"

Akademický článek

A Two-Dimensional Post-Stack Seismic Inversion for Acoustic Impedance of Gas and Hydrate Bearing Deep-Water Sediments Within the Continental Slope of the Ulleung Basin, East Sea, Korea

Autor: Keumsuk Lee, Dong-Geun Yoo, George A. McMechan, Namsoon Hwang, Gwang H. Lee

Publikováno v: Terrestrial, Atmospheric and Oceanic Sciences, Vol 24, Iss 3, p 295 (2013)

A post-stack inversion of 2D seismic data was conducted to estimate the spatial distribution of acoustic impedance associated with gas and hydrates in the Ulleung Basin, East Sea, Korea constrained by logs from three boreholes drilled on its continen

Externí odkaz: https://doaj.org/article/1fb9ec3fdbc64431be56d48c63f7cd31

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Re-weighted variational full waveform inversions

Autor: Wenlong Wang, George A. McMechan, Jianwei Ma

Publikováno v: GEOPHYSICS. :1-61

Starting from an initial model and pre-defined priors, a variational full waveform in- version (VFWI) seeks posterior distributions of model parameters via optimization using Bayesian theorem. VFWI is thus useful in estimating uncertainties in full-w

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2cbe4f135a2aad011d89fc5e5e7b27aa
https://doi.org/10.1190/geo2021-0766.1

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Automated dispersion curve picking using multi-attribute convolutional-neural-network based machine learning

Autor: Li Ren, Fuchun Gao, Yulang Wu, Paul Williamson, George A McMechan, Wenlong Wang

Publikováno v: Geophysical Journal International. 232:1173-1208

SUMMARY Surface wave dispersion curves are useful to characterize shallow subsurface structures while accurately picking them is typically laborious. To make these approaches more efficient and practical, it is important to automate the picking proce

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8710a082d6b2494ff35d26fec5d9893b
https://doi.org/10.1093/gji/ggac383

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Approximating the Gauss–Newton Hessian Using a Space-Wavenumber Filter and its Applications in Least-Squares Seismic Imaging

Autor: Jianping Huang, George A. McMechan, Zhenchun Li, Hejun Zhu, Xin Luo, Jidong Yang

Publikováno v: IEEE Transactions on Geoscience and Remote Sensing. 60:1-13

The acquisition footprint, finite-frequency source, and unbalanced subsurface illumination make it difficult for traditional adjoint-based migration to produce a high-quality image for complex structures. By fitting reflection events with linearized

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bae5a62b56017d8a858cdecd1f7d9cf9
https://doi.org/10.1109/tgrs.2021.3064852

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Mitigating Velocity Errors in Least-Squares Imaging Using Angle-Dependent Forward and Adjoint Gaussian Beam Operators

Autor: George A. McMechan, Jianping Huang, Hejun Zhu, James Zhang, Zhenchun Li, Yang Zhao, Jidong Yang, Chaoshun Hu

Publikováno v: Surveys in Geophysics. 42:1305-1346

Compared with traditional adjoint-based migration, least-squares migration (LSM) can reduce finite-frequency effects, remove acquisition footprints and improve spatial resolution by solving a linear inverse problem for subsurface reflectivity. One im

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cbde59a2b29976f22f6f1c66c2a2daa2
https://doi.org/10.1007/s10712-021-09676-y

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Self-supervised deep learning to adaptively predict geophysical multi-parameter models

Autor: Yulang Wu, Wenlong Wang, Yanfei Wang, George A. McMechan

Multi-parameter models (MPMs) imaging is essential to understanding and interpreting geological structures. Conventional geophysical imaging methods matching simulated seismic data to the observed seismic data cannot obtain accurate MPMs, due to impl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::696bda3a45fbb48a5bc13615c7f5b760

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Elastic isotropic and anisotropic full-waveform inversions using automatic differentiation for gradient calculations in a framework of recurrent neural networks

Autor: Jianwei Ma, George A. McMechan, Wenlong Wang

Publikováno v: GEOPHYSICS. 86:R795-R810

We have implemented multiparameter full-waveform inversions (FWIs) in the framework of recurrent neural networks in elastic isotropic and transversely isotropic media. A staggered-grid velocity-stress scheme is used to solve the first-order elastodyn

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f6b6df2381f21bbae9915f4c4a9c8423
https://doi.org/10.1190/geo2020-0542.1

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Automatic velocity picking from semblances with a new deep-learning regression strategy: Comparison with a classification approach

Autor: Fei Xie, Wenlong Wang, George A. McMechan, Jianwei Ma

Publikováno v: GEOPHYSICS. 86:U1-U13

The physical basis, parameterization, and assumptions involved in root-mean-square (rms) velocity estimation have not significantly changed since they were first developed. However, these three aspects are all good targets for novel application of th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2a303f38d3aa734a62c9e33f91f25acc
https://doi.org/10.1190/geo2020-0423.1

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Source-domain full-waveform inversions

Autor: Yulang Wu, George A. McMechan

Publikováno v: GEOPHYSICS. 86:R147-R159

Conventional full-waveform inversion (FWI) updates a velocity model by minimizing the data residuals between the predicted and observed data at the receiver positions. We have developed a new FWI to update the velocity model by minimizing virtual sou

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::11862d6493c51fca8bca496a828de399
https://doi.org/10.1190/geo2020-0047.1

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