Mutual-guided scale-aggregation denoising network for seismic noise attenuation

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Geosciences Pub Date : 2024-07-30 DOI:10.1016/j.cageo.2024.105682
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Abstract

The background noise contained in seismic records contaminate the effective reflection waves and impact the subsequent processes, such as inversion and migration. The properties of seismic noises, such as non-Gaussianity and non-linearity, will be even more complex in challenging exploration environments. Deep-learning techniques are effective in suppressing complex seismic noises and outperform conventional denoising algorithms. Nonetheless, most deep learning networks are designed to extract the features of input data in single-scale only, which leads to inadequate performance when dealing with complicated seismic data. To enhance the denoising capability for seismic noises of deep learning, a novel mutual-guided scale-aggregation denoising network (MSD-Net) is designed to suppress seismic noises by utilizing the multi-scale features of input data. Specifically, the MSD-Net achieves functions including multi-scale feature extraction, fusion, and guidance through information interaction between different scales. Spatial aggregation attention is used in MSD-Net to enhance relevant features, which improves the separation of effective reflection waves and noises further. Additionally, a model-based training data generation strategy is devised to ensure the efficiency of learning and the denoising capability of MSD-Net. Compared to conventional denoising algorithms and typical deep learning networks, MSD-Net shows powerful result in suppressing complex seismic noises and generalization.

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用于地震噪声衰减的互导尺度聚合去噪网络
地震记录中的背景噪声会污染有效反射波,影响反演和迁移等后续过程。在充满挑战的勘探环境中,地震噪声的特性(如非高斯性和非线性)将更加复杂。深度学习技术可有效抑制复杂的地震噪声,其性能优于传统的去噪算法。然而,大多数深度学习网络的设计只能提取单尺度输入数据的特征,这导致其在处理复杂地震数据时性能不足。为了增强深度学习对地震噪声的去噪能力,我们设计了一种新型互导尺度聚合去噪网络(MSD-Net),利用输入数据的多尺度特征来抑制地震噪声。具体来说,MSD-Net 通过不同尺度之间的信息交互实现多尺度特征提取、融合和引导等功能。MSD-Net 利用空间聚合注意力来增强相关特征,从而进一步提高有效反射波与噪声的分离度。此外,还设计了基于模型的训练数据生成策略,以确保 MSD-Net 的学习效率和去噪能力。与传统的去噪算法和典型的深度学习网络相比,MSD-Net 在抑制复杂地震噪声和泛化方面显示出强大的效果。
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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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