摘要：Seismic high-resolution processing is crucial to enhance the accuracy and reliability of seismic data, particularly in the exploration and development of complex hydrocarbon reservoirs. Conventional high-resolution processing methods, exemplified by sparse spike deconvolution (SSD), typically rely on the assumption of sparse reflectivity. Recent advancements in deep learning have introduced a deep convolutional neural network (DCNN) into high-resolution processing and relaxed it from rigorous physical assumptions. However, DCNN high-resolution processing often suffers from limited generalization capabilities, primarily due to the scarcity of labeled data and variations in data quality. In this study, we identify a frequency learning bias in DCNN high-resolution processing, in which the network initially prioritizes dominant-frequency components before gradually addressing lower and higher frequencies. This bias results in inadequate learning of high-frequency components. We develop a frequency-dependent multiscale network (FMN) informed by the frequency multiscale transformation theory. The FMN aims to improve the frequency learning direction of neural networks, enabling them to simultaneously and efficiently learn information across low-, dominant-, and high-frequency components. Our synthetic and 3D field data experiments demonstrate that our FMN outperforms SSD and DCNN methods, providing superior generalization and higher fidelity in producing high-resolution results. ? 2025 Society of Exploration Geophysicists. All rights reserved.

ISSN号：0016-8033

卷、期、页：卷90期4：V297-V312

发表日期：2025-07-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：Geophysics

参与作者：Zeng, Huahui,Yuan, Junliang

通讯作者：许言午,于越,李明轩

第一作者：袁三一,王尚旭

论文类型：期刊论文

论文概要：许言午,袁三一,Zeng, Huahui,Yuan, Junliang,于越,李明轩,王尚旭，Frequency-dependent multiscale network for seismic high-resolution processing，Geophysics，2025，卷90期4：V297-V312

论文题目：Frequency-dependent multiscale network for seismic high-resolution processing