Horizontal-Component Prior-based Framework for Adaptive Shear-wave Leakage Suppression in OBC Data

TL;DR

This paper introduces HPAS, a novel adaptive framework for suppressing shear-wave leakage in OBC seismic data using horizontal-component priors and an additive-subtractive noise strategy, avoiding the need for clean P-wave labels.

Contribution

The proposed HPAS framework generates input-label pairs directly from raw data, enabling adaptive shear-wave leakage suppression without supervised labels.

Findings

Effectively suppresses shear-wave leakage in both synthetic and field data.

Preserves P-wave amplitude while reducing S-wave noise.

Demonstrates strong generalization across different datasets.

Abstract

Shear-wave leakage in the vertical (Z) component of ocean-bottom cable (OBC) seismic data commonly results from the receiver tilt and poor seafloor coupling, introducing unwanted coherent noise that impacts the subsequent data processing and imaging. Traditional denoising methods are limited by manual parameter tuning and idealized model assumptions, while deep-learning (DL) approaches have shown significant potential in suppressing shear-wave leakage. However, supervised learning requires clean primary waves (P waves) as the label, which is generally impractical to obtain for field data. To address these challenges, we propose a framework based on horizontal-component priors for adaptive shear-wave leakage suppression (HPAS). Instead of relying on clean primary-wave (P-wave) data, HPAS generates input-label pairs directly from raw multi-component field data using an additive-subtractive noise strategy. Specifically, we extract shear-wave (S-wave) noise from the horizontal components and apply a linear transformation to match its first and second order moments with the S-wave leakage in the Z-component, and the statistically matched noise is then added to and subtracted from the original Z-component to create the input and label pairs. By allowing the denoising model to learn the S-wave features present in the differences between the input and the label, the adaptive denoising process approximates supervised learning. Evaluations on both synthetic and field data demonstrate that the proposed HPAS framework effectively and adaptively suppresses S-wave leakage while preserving the amplitude of the P-wave signals in the Z-component, offering a robust solution with strong generalization capabilities.