Physics‐Guided Deep Learning for Adaptive Surface‐Related Multiple Subtraction

ABSTRACT Surface‐related multiple elimination is a fundamental step in seismic data processing, typically relying on a two‐stage procedure: multiple prediction followed by adaptive subtraction. While the prediction step is physically robust, the adaptive subtraction stage often struggles to resolve complex non‐stationary discrepancies and overlapping primary‐multiple events using conventional energy minimization criteria. In this paper, we propose a physics‐guided deep learning (PGDL) framework to address these limitations by treating adaptive subtraction as a non‐linear, physics‐constrained mapping task. We utilize a U‐Net architecture with a specialized dual‐channel input: the original recorded full wavefield and the globally estimated multiples derived from the wave equation–based multi‐dimensional convolution. By explicitly incorporating the multiple models, we inject robust kinematic constraints (i.e., physics) into the network, allowing the learning process to focus on the non‐linear residual mapping required to correct amplitude and phase errors rather than learning wave propagation from scratch. We validate the proposed framework through three comprehensive scenarios: (1) synthetic‐to‐synthetic generalization, (2) field‐to‐field application using pseudo‐labels and (3) a cross‐data‐distribution test training on synthetic data and applying it to field data. Our results demonstrate that the PGDL framework effectively suppresses surface‐related multiples while preserving weak primary energy that is often damaged by traditional methods. Furthermore, we show that a transfer learning strategy using minimal field data effectively bridges the data distribution gap between synthetic training sets and real‐world field acquisition, offering a scalable and computationally efficient way for industrial deployment.