Physics-Driven Self-Supervised Deep Learning for Free-Surface Multiple Elimination

TL;DR

This paper introduces a physics-driven self-supervised deep learning method for free-surface multiple elimination in seismic data, which learns from the full wavefield without ground truth labels, outperforming traditional benchmarks in accuracy.

Contribution

The proposed method incorporates physical principles into self-supervised learning for seismic multiple elimination, eliminating the need for labeled training data and improving estimation accuracy.

Findings

Outperforms traditional SRME benchmarks in accuracy.

Achieves more complete primary estimation with less multiple leakage.

Demonstrates effectiveness on both synthetic and field data.

Abstract

In recent years, deep learning (DL) has emerged as a promising alternative approach for various seismic processing tasks, including primary estimation (or multiple elimination), a crucial step for accurate subsurface imaging. In geophysics, DL methods are commonly based on supervised learning from large amounts of high-quality labelled data. Instead of relying on traditional supervised learning, in the context of free-surface multiple elimination, we propose a method in which the DL model learns to effectively parameterize the free-surface multiple-free wavefield from the full wavefield by incorporating the underlying physics into the loss computation. This, in turn, yields high-quality estimates without ever being shown any ground truth data. Currently, the network reparameterization is performed independently for each dataset. We demonstrate its effectiveness through tests on both synthetic and field data. We employ industry-standard Surface-Related Multiple Elimination (SRME) using, respectively, global least-squares adaptive subtraction and local least-squares adaptive subtraction as benchmarks. The comparison shows that the proposed method outperforms the benchmarks in estimation accuracy, achieving the most complete primary estimation and the least multiple energy leakage, but at the cost of a higher computational burden.