摘要：The high resolution Radon transform is one of the commonly used methods in seismic data processing. Its inversion usually involves matrix inversion, multiple iterations, hyperparameter selection et al. These factors lead to problems such as a large amount of calculation and a slow convergence rate in the inversion of the Radon transform inversion. Based on the analysis of the low resolution of Radon transform, we propose a high resolution Radon transform inversion method based on one-dimensional Convolutional Neural Network (1-D CNN). This method realizes the mapping from conjugate Radon solution to high resolution by the nonlinear representation ability of CNN. And the principle of improving resolution of series mapping model based on deconvolution principle and parallel mapping model based on residual learning are analyzed. The specific frequency Radon parameter obtained by the above CNN network is restricted to the inversion of other frequency parameters, which avoids the drawbacks of frequency division training. Multiple suppression experiments on synthetic and field data show that the proposed high-resolution Radon transform based on 1-D CNN can suppress multiples with high computational efficiency.

关键字：Radon transform; Convolutional Neural Network (CNN); Inversion method; Frequency constraint; Multiple suppression

ISSN号：0001-5733

卷、期、页：卷: 65期: 9页: 3610-3622

发表日期：2022-09-01

影响因子：0.847000

期刊分区（SCI为中科院分区）：三区

收录情况：SCIE（科学引文索引网络版）,EI（工程索引）

发表期刊名称：CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION

通讯作者：郭蒙军,冯璐瑜

第一作者：薛亚茹,马继涛,陈小宏

论文类型：期刊论文

论文概要：薛亚茹,郭蒙军,冯璐瑜,马继涛,陈小宏，High resolution Radon transform inversion based on one dimensional convolutional neural network，CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION，2022，卷: 65期: 9页: 3610-3622

论文题目：High resolution Radon transform inversion based on one dimensional convolutional neural network