Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Traveltime-table compression using artificial neural networks for Kirchhoff-migration processing of microseismic data. / Grubas, Serafim I.; Loginov, Georgy N.; Duchkov, Anton A.
в: Geophysics, Том 85, № 5, 01.09.2020, стр. U121-U128.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
}
TY - JOUR
T1 - Traveltime-table compression using artificial neural networks for Kirchhoff-migration processing of microseismic data
AU - Grubas, Serafim I.
AU - Loginov, Georgy N.
AU - Duchkov, Anton A.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Massive computation of seismic traveltimes is widely used in seismic processing, for example, for the Kirchhoff migration of seismic and microseismic data. Implementation of the Kirchhoff migration operators uses large precomputed traveltime tables (for all sources, receivers, and densely sampled imaging points). We have tested the idea of using artificial neural networks for approximating these traveltime tables. The neural network has to be trained for each velocity model, but then the whole traveltime table can be compressed by several orders of magnitude (up to six orders) to the size of less than 1 MB. This makes it convenient to store, share, and use such approximations for processing large data volumes. We evaluate some aspects of choosing neural-network architecture, training procedure, and optimal hyperparameters. On synthetic tests, we find a reasonably accurate approximation of traveltimes by neural networks for various velocity models. A final synthetic test shows that using the neural-network traveltime approximation results in good accuracy of microseismic event localization (within the grid step) in the 3D case.
AB - Massive computation of seismic traveltimes is widely used in seismic processing, for example, for the Kirchhoff migration of seismic and microseismic data. Implementation of the Kirchhoff migration operators uses large precomputed traveltime tables (for all sources, receivers, and densely sampled imaging points). We have tested the idea of using artificial neural networks for approximating these traveltime tables. The neural network has to be trained for each velocity model, but then the whole traveltime table can be compressed by several orders of magnitude (up to six orders) to the size of less than 1 MB. This makes it convenient to store, share, and use such approximations for processing large data volumes. We evaluate some aspects of choosing neural-network architecture, training procedure, and optimal hyperparameters. On synthetic tests, we find a reasonably accurate approximation of traveltimes by neural networks for various velocity models. A final synthetic test shows that using the neural-network traveltime approximation results in good accuracy of microseismic event localization (within the grid step) in the 3D case.
KW - interpolation
KW - microseismic
KW - migration
KW - neural networks
KW - traveltime
UR - http://www.scopus.com/inward/record.url?scp=85095565826&partnerID=8YFLogxK
U2 - 10.1190/geo2019-0427.1
DO - 10.1190/geo2019-0427.1
M3 - Article
AN - SCOPUS:85095565826
VL - 85
SP - U121-U128
JO - Geophysics
JF - Geophysics
SN - 0016-8033
IS - 5
ER -
ID: 25863554