Noise Suppression for CRP Gathers Based on Self2Self with Dropout

TL;DR

This paper introduces a self-supervised deep learning method called SSDCN for noise suppression in seismic CRP gathers, leveraging internal data structure and geological knowledge to improve signal clarity without needing clean labels.

Contribution

The paper proposes a novel self-supervised learning approach based on Self2Self with Dropout for seismic noise suppression, integrating geological insights to enhance performance.

Findings

Effective noise suppression demonstrated on synthetic data

High-fidelity results on real CRP gathers

Outperforms traditional methods in preserving useful signals

Abstract

Noise suppression in seismic data processing is a crucial research focus for enhancing subsequent imaging and reservoir prediction. Deep learning has shown promise in computer vision and holds significant potential for seismic data processing. However, supervised learning, which relies on clean labels to train network prediction models, faces challenges due to the unavailability of clean labels for seismic exploration data. In contrast, self-supervised learning substitutes traditional supervised learning with surrogate tasks by different auxiliary means, exploiting internal input data information. Inspired by Self2Self with Dropout, this paper presents a self-supervised learning-based noise suppression method called Self-Supervised Deep Convolutional Networks (SSDCN), specifically designed for Common Reflection Point (CRP) gathers. We utilize pairs of Bernoulli-sampled instances of the input noisy image as surrogate tasks to leverage its inherent structure. Furthermore, SSDCN incorporates geological knowledge through the normal moveout correction technique, which capitalizes on the approximately horizontal behavior and strong self-similarity observed in useful signal events within CRP gathers. By exploiting the discrepancy in self-similarity between the useful signals and noise in CRP gathers, SSDCN effectively extracts self-similarity features during training iterations, prioritizing the extraction of useful signals to achieve noise suppression. Experimental results on synthetic and actual CRP gathers demonstrate that SSDCN achieves high-fidelity noise suppression.