基于物理约束深度神经网络的近地表非平稳相干噪声抑制

IF 8.6 1区 地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Jiwei Liu;Tianyue Hu;Chunming Wang;Xixi Li;Qingcai Zeng;Lichao Liu
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引用次数: 0

摘要

地表波通常被称为瑞利波,也被称为地滚波,它是一种强烈的相干噪声,会干扰地震勘探记录数据在地下成像的准确性。在崎岖地形和变化的近地表速度带来的进一步挑战的情况下,现场数据中的表面波和其他与近地表相关的噪声表现出非平稳特征,其振幅、频率和速度随记录时间而变化。目前,抑制这类表面波噪声的传统方法是基于稳态假设。本文提出了一种基于物理信息约束的深度神经网络的非平稳表面波抑制方法。该方法利用表面波的物理特性和数据驱动标签共同约束神经网络的训练。它可以在小样本条件下进行有效的训练,并有效地抑制收集到的每个叠前地震数据中的非平稳表面波。它还能有效地降低与近地表相关的非平稳干扰,如单频噪声和线性噪声。数值和现场数据的实例表明,这种近地表噪声抑制技术可以精确地消除表面波等非平稳干扰信号,重构有效波,具有高效率、鲁棒性和泛化能力。将现场数据处理结果与一些典型商业软件的处理结果进行比较,证实了本文提出的深度神经网络方法超越了传统标签约束网络训练的局限性,以更低的成本高效、稳定地抑制了现场数据中的表面波、单频噪声和线性干扰。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Near-Surface-Related Nonstationary Coherent Noise Suppression Using a Physically Constrained Deep Neural Network
Surface waves, usually referred to as Rayleigh waves and also called ground roll, generated near the surface are a kind of strong coherent noise to disturbs the accuracy of subsurface images with the recorded data from a seismic survey. In the case of the further challenges posed by rugged topography and variable near-surface velocities, the surface waves and other near-surface-related noises in the field data exhibit nonstationary characteristics, and their amplitude, frequency, and velocity vary with recorded time. Currently, the conventional methods to suppress this type of surface wave noise are based on steady-state assumptions. This article develops a nonstationary surface wave suppression method utilizing a deep neural network constrained by physical information. This method leverages the physical characteristics of surface waves and data-driven labels to constrain neural network training jointly. It enables effective training under small-sample conditions and effectively suppresses nonstationary surface waves in each prestack seismic data gathered. It also effectively decreases near-surface-related nonstationary interferences such as single-frequency noise and linear noise. Examples for both numerical and field datasets demonstrate that this near-surface-related noise suppression technology can precisely eliminate nonstationary interference signals such as surface waves, reconstructing effective waves with high efficiency, robustness, and generalization capability. Comparisons between the processing results of field data and those obtained using some typical commercial software confirm that the proposed deep neural network method surpasses traditional label-constrained network training limitations, offering superior suppression of surface waves, single-frequency noise, and linear interference in field data efficiently and stably with much less cost.
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来源期刊
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing 工程技术-地球化学与地球物理
CiteScore
11.50
自引率
28.00%
发文量
1912
审稿时长
4.0 months
期刊介绍: IEEE Transactions on Geoscience and Remote Sensing (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.
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