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Subsalt imaging in the presence of salt-body uncertainty. / Protasov, Maxim; Kolyukhin, Dmitriy; Rostomyan, Semen и др.

в: Leading Edge, Том 36, № 2, 01.02.2017, стр. 146-150.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Protasov, M, Kolyukhin, D, Rostomyan, S & Landa, E 2017, 'Subsalt imaging in the presence of salt-body uncertainty', Leading Edge, Том. 36, № 2, стр. 146-150. https://doi.org/10.1190/tle36020146.1

APA

Protasov, M., Kolyukhin, D., Rostomyan, S., & Landa, E. (2017). Subsalt imaging in the presence of salt-body uncertainty. Leading Edge, 36(2), 146-150. https://doi.org/10.1190/tle36020146.1

Vancouver

Protasov M, Kolyukhin D, Rostomyan S, Landa E. Subsalt imaging in the presence of salt-body uncertainty. Leading Edge. 2017 февр. 1;36(2):146-150. doi: 10.1190/tle36020146.1

Author

Protasov, Maxim ; Kolyukhin, Dmitriy ; Rostomyan, Semen и др. / Subsalt imaging in the presence of salt-body uncertainty. в: Leading Edge. 2017 ; Том 36, № 2. стр. 146-150.

BibTeX

@article{4e7380afc7824c19b67a0c731d581abc,
title = "Subsalt imaging in the presence of salt-body uncertainty",
abstract = "The problem of subsalt imaging in the presence of salt-boundary uncertainty is considered. In this case, the salt-body model should be considered by a probability density function rather than by a unique deterministic function. We discuss a way to look at seismic imaging using the path-integral concept. The method computes the image by summing the contributions of individual images computed for different statistical realizations of the salt-body boundaries. It samples different realizations instead of relying on only one model derived from manual or automatic picking. The focusing mechanism is achieved by a weighting function (probability amplitude), which is designed to emphasize contributions from models close to the stationary one and to suppress contributions from unlikely models. The presented examples demonstrate principles and feasibility of the new concept.",
keywords = "Imaging, Salt, Statistical, Subsalt",
author = "Maxim Protasov and Dmitriy Kolyukhin and Semen Rostomyan and Evgeny Landa",
year = "2017",
month = feb,
day = "1",
doi = "10.1190/tle36020146.1",
language = "English",
volume = "36",
pages = "146--150",
journal = "Leading Edge",
issn = "1070-485X",
publisher = "Society of Exploration Geophysicists",
number = "2",

}

RIS

TY - JOUR

T1 - Subsalt imaging in the presence of salt-body uncertainty

AU - Protasov, Maxim

AU - Kolyukhin, Dmitriy

AU - Rostomyan, Semen

AU - Landa, Evgeny

PY - 2017/2/1

Y1 - 2017/2/1

N2 - The problem of subsalt imaging in the presence of salt-boundary uncertainty is considered. In this case, the salt-body model should be considered by a probability density function rather than by a unique deterministic function. We discuss a way to look at seismic imaging using the path-integral concept. The method computes the image by summing the contributions of individual images computed for different statistical realizations of the salt-body boundaries. It samples different realizations instead of relying on only one model derived from manual or automatic picking. The focusing mechanism is achieved by a weighting function (probability amplitude), which is designed to emphasize contributions from models close to the stationary one and to suppress contributions from unlikely models. The presented examples demonstrate principles and feasibility of the new concept.

AB - The problem of subsalt imaging in the presence of salt-boundary uncertainty is considered. In this case, the salt-body model should be considered by a probability density function rather than by a unique deterministic function. We discuss a way to look at seismic imaging using the path-integral concept. The method computes the image by summing the contributions of individual images computed for different statistical realizations of the salt-body boundaries. It samples different realizations instead of relying on only one model derived from manual or automatic picking. The focusing mechanism is achieved by a weighting function (probability amplitude), which is designed to emphasize contributions from models close to the stationary one and to suppress contributions from unlikely models. The presented examples demonstrate principles and feasibility of the new concept.

KW - Imaging

KW - Salt

KW - Statistical

KW - Subsalt

UR - http://www.scopus.com/inward/record.url?scp=85013869276&partnerID=8YFLogxK

U2 - 10.1190/tle36020146.1

DO - 10.1190/tle36020146.1

M3 - Article

AN - SCOPUS:85013869276

VL - 36

SP - 146

EP - 150

JO - Leading Edge

JF - Leading Edge

SN - 1070-485X

IS - 2

ER -

ID: 10279058