发表机构
Jilin University; Northeast Electric Power University（吉林大学; 东北电力大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该综述探讨地震数据去噪，提出从任务特定深度学习模型转向基础模型范式，并验证了SeisDeFM模型在多样噪声条件下实现优越去噪性能和跨噪声泛化能力。
AI 中文摘要
去噪是地震数据处理中长期存在且广受关注的话题，因为它能显著提高地震数据的信噪比。众多深度学习（DL）方法已展现出有前景的去噪性能，但其中大多数是针对特定任务且聚焦于某一类型的地震背景噪声。地震数据集常被多种噪声污染的真实情况，促使我们探索一种泛化良好且多功能的深度学习模型用于地震数据去噪。近期，在计算机视觉和自然语言处理领域，在海量数据集上预训练的基础模型（FMs）展现出跨多种下游任务的卓越适应性和泛化性。这一范式为解决特定任务深度学习去噪模型所面临的挑战（如泛化能力差、针对不同噪声需从头重新训练以及缺乏标注数据）提供了有前景的路径。我们首先简要回顾传统地震去噪方法，随后按噪声类型对现有基于深度学习的去噪方法进行全面综述。此外，我们对一个专门的地震去噪基础模型（称为SeisDeFM）进行了案例研究。这是地球物理研究中首个在具有多样噪声条件的前叠加道集上开发和验证地震去噪基础模型的研究。实验结果表明，与特定任务的深度学习基线相比，SeisDeFM凭借充分预训练和适当下游适应的优势，实现了更优的去噪性能和跨噪声泛化能力，并在抑制前叠加地震数据中复杂噪声的同时有效保留了弱反射事件。
英文摘要
Denoising is a long-standing and widely-concerned topic in seismic data processing, for it can significantly increase the signal-to-noise ratio of seismic data. Numerous deep learning (DL) methods have shown promising denoising performance, but most of them are task-specific and focus on a certain type of seismic background noise. The real condition that seismic datasets are often contaminated by various types of noises motivates us to explore a well-generalized and versatile DL model for seismic data denoising. Recently, in the fields of computer vision and natural language processing, foundation models (FMs) pre-trained on vast datasets demonstrate outstanding adaptability and generality across diverse downstream tasks. This paradigm offers a promising path to address the challenges faced by task-specific DL denoising models, such as poor generalization, retraining from scratch for different noise, and the lack of labeled data. We first provide a brief review of traditional seismic denoising methods, followed by a comprehensive review of existing DL-based denoising methods categorized by noise type. Furthermore, we conduct a case study on a dedicated seismic denoising foundation model termed SeisDeFM. This is the first study in geophysical research to develop and validate a seismic denoising foundation model on pre-stack gathers with diverse noise conditions. Experimental results demonstrate that, compared with task-specific DL baselines, SeisDeFM achieves superior denoising performance and cross-noise generalization by the advantages of sufficient pre-training and appropriate downstream adaptation, and it effectively preserves weak reflection events while suppressing complex noise in pre-stack seismic data.
