基于一维卷积神经网络的高分辨率Radon变换反演方法研究

高分辨率Radon变换是地震资料处理常用的方法之一，其反演通常涉及矩阵求逆、多次迭代等环节，这些因素导致Radon变换反演计算量大，收敛速度慢等问题.本文在分析Radon变换分辨率降低原因基础上，提出基于一维卷积神经网络（Convolutional Neural Network，CNN）的高分辨率Radon变换反演方法.该方法通过卷积神经网络的非线性表征能力实现低分辨率Radon参数到高分辨率Radon参数的映射，分析了基于反褶积原理的串联映射模型和基于残差学习的并联映射模型提高分辨率的原理.
将上述CNN网络得到的特定频率Radon参数约束其他频率参数的反演，避免了分频训练的弊端.模拟数据和实际数据的多次波压制实验表明，本文提出的基于一维卷积神经网络的高分辨率Radon变换可以较好地压制多次波，且计算效率高.

High resolution Radon transform inversion based on one dimensional convolutional neural network

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.