深度学习地震数据重建方法研究综述

Review of deep learning seismic data reconstruction methods

由于自然条件限制和人为因素的影响,实际采集得到的地震数据往往会出现地震道数据缺失的情况,会对后续的地震数据处理和解释制造困难,需要对地震数据进行重建.而传统地震数据重建方法通常存在着重建效果受先验条件约束、超参数选择需要人工干预、自动化程度低等问题.于是人们将目光投向发展迅速的深度学习领域,截至今日已经有不少深度学习方法应用于地震数据重建领域以解决上述地震数据重建过程中的问题.本文将着重分析具有代表性的深度学习地震数据重建方法,分别基于卷积神经网络、循环神经网络、卷积自编码器、生成对抗性网络.通过重建结果残差对比图,重建结果信噪比分析等方法对深度学习地震数据重建方法的优势和不足进行深入探讨.并进一步阐述深度学习地震数据重建方法的研究现状、方法优势、存在的问题以及未来发展趋势,对现今的深度学习重建方法进行总结和展望.

Due to the influence of natural and human factors,the actually collected seismic data often have the situation that the seismic trace data is missing,which will affect the subsequent seismic data processing and interpretation.Data processing and interpretation.However,traditional seismic data reconstruction methods usually have the problems that the seismic data reconstruction effect is constrained by prior conditions,the selection of hyperparameters requires manual intervention,and the degree of automation is low.This is an unavoidable drawback of current geophysical methods.Since its inception,the deep learning method has the advantages of high degree of automation,completed training,fast processing of target tasks,wide application fields,and flexibility.Therefore,people turn their attention to the rapidly developing field of deep learning.As of today,it has been There are many deep learning methods applied to the field of seismic data reconstruction to solve the above-mentioned problems in the seismic data reconstruction process.This paper will focus on the analysis of representative deep learning seismic data reconstruction methods,which are based on Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),convolutional autoencoder,and generative adversarial network.The above three methods are selected from the following considerations.The CNN is the representative of the classic visual neural network and super-resolution image reconstruction,and it is very representative;the RNN is a significant attempt based on the characteristics of the seismic data sequence,because it gets rid of the long-term perception of deep learning methods.The thinking inertia of processing through the visual neural network;the generative adversarial network uses the network game theory,and the deep learning network is constantly adjusted in the continuous learning process.In this paper,the advantages and disadvantagesof the deep learning seismic data reconstruction method are discussed in depth through the reconstruction results residual comparison chart and the reconstruction result signal-to-noise ratio analysis.And further expound the research status,method advantages,existing problems and future development trends of deep learning seismic data reconstruction methods,and summarize and prospect the current deep learning reconstruction methods.