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 -