Composite loss function for 3-D poststack seismic data compression

Kevyn Swhants dos Santos Ribeiro

doi:10.1016/j.cageo.2024.105620

Composite loss function for 3-D poststack seismic data compression

2024-07-01

0

PRE

AI

K

Kevyn Swhants dos Santos Ribeiro

M

Marcelo Bernardes Vieira

S

Saulo Moraes Villela

M

Marcelo Caniato Renhe *

DOI：10.1016/j.cageo.2024.105620

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En 中文

This work introduces composite functions to compute distortion in volumetric seismic data. Several loss functions, such as those based on L p -functions, ignore the structure of 3-D seismic data, treating it as a unidimensional vector. Alternatively, applying distinct functions in each axis, properly designed for dimension reduction, can evaluate seismic data error according to its unity and magnitude. We thus propose a novel multidimensional composite loss function to evaluate through dimension reduction, suitable for seismic data compression within a method named 3DSC-GAN. It replaces the usual peak signal-to-noise ratio (PSNR) metric as the distortion function. An extensive study is conducted to analyze potential combinations of functions for the 3-D poststack seismic data compression problem. Results indicate that the new function contributes to improve the neural network learning step. Our method provides superior reconstructions, both quantitatively and qualitatively, when compared to the PSNR metric.

Keyword:

3-D poststack data

Seismic data compression

Deep learning

Composite loss function

Generative adversarial networks

Composite loss function for 3-D poststack seismic data compression

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