Efficient Upside-Down Rayleigh-Marchenko Imaging through Self-Supervised Focusing Function Estimation

TL;DR

This paper introduces a self-supervised learning method using a U-Net to rapidly estimate focusing functions in the Upside-Down Rayleigh-Marchenko seismic imaging, significantly reducing computational costs while maintaining high image quality.

Contribution

It presents a novel self-supervised deep learning approach to accelerate focusing function estimation in UD-RM seismic imaging, enabling efficient large-scale applications.

Findings

The method achieves high-quality imaging with fewer training points.

It significantly reduces computational costs compared to traditional iterative methods.

The approach is validated on synthetic and real field data, demonstrating robustness.

Abstract

The Upside-Down Rayleigh-Marchenko (UD-RM) method has recently emerged as a powerful tool for retrieving subsurface wavefields and images free from artifacts caused by both internal and surface-related multiples. Its ability to handle acquisition setups with large cable spacing or sparse node geometries makes it particularly suitable for ocean-bottom seismic data processing. However, the widespread application of the method is limited by the high computational cost required to estimate the focusing functions, especially when dealing with large imaging domains. To address this limitation, a self-supervised learning approach is proposed to accelerate the estimation of the focusing functions. Specifically, a U-Net network is trained on a small subset of image points from within the target area of interest, whose focusing functions are pre-computed using the conventional iterative scheme. The network is tasked to predict both the up- and down-going focusing functions from an initial estimate of the subsurface wavefields. Once trained, the network generalizes to remaining unseen imaging locations, enabling direct prediction of the focusing functions. Validation on a synthetic dataset with both dense and sparse receiver sampling using progressively fewer training points demonstrates the method's effectiveness. In both cases, the resulting images closely match those obtained from the UD-RM method with focusing functions retrieved by the conventional iterative approach at a much lower cost and significantly outperform mirror migration (when the same input dataset is used). Finally, an application to the Volve field data confirms the method's robustness in practical scenarios. The proposed approach enables seismic imaging at a fraction of the computational cost of the conventional UD-RM approach while maintaining imaging quality, underscoring its potential for large-scale seismic applications.