石油科学通报2024，Vol.9Issue(1)：35-49,15.DOI:10.3969/j.issn.2096-1693.2024.01.003
基于注意力机制的无监督学习地震数据随机和不规则噪声衰减方法
Attention mechanism-based unsupervised learning seismic data random and erratic noise attention framework
摘要
Abstract
Random noise and coherent noise interfere with seismic data collected in the field,resulting in the reduction of the signal-to-noise ratio,which affects the subsequent processing of seismic data,such as seismic migration and imaging.There-fore,it is necessary to develop an efficient and adaptive method to attenuate random and coherent noise in real seismic data.Conventional supervised learning algorithms need to manually generate a large number of labels to train the network,which is very difficult in the field of seismic exploration where the data volume is small.In addition,supervised learning-based noise attenuation methods are expensive in terms of computation and labor costs.To solve this problem,this paper constructs an adaptive deep learning framework based on unsupervised learning strategies to attenuate random and irregular(erratic)noise in multi-dimensional seismic data.This method uses the corresponding structure of encoding and decoding to compress and reconstruct data features.In order to improve the network's attention to important waveform features,this paper uses a soft attention mechanism to assign more weight to important waveform features in a weighted way.In this paper,the multi-di-mensional noisy data is segmented into a large number of one-dimensional noisy signals,which are input into the network for iteration,so as to adaptively attenuate random and erratic noise in seismic data.This small-scale signal denoising method can effectively improve the noise attenuation performance of the network and help to avoid artifacts.In this paper,we use a more robust Huber loss function to attenuate random and erratic noise,which combines the root-mean-square error with l2 norm and the average absolute error loss with l1 norm.In addition,a Total Variation(TV)regularization term is added to the constructed network to capture the local smooth structure of the seismic data.By adjusting the weight of Huber loss function and TV regularization term,the network can obtain the best denoising performance.The method constructed in this paper can be directly used for attenuation of random and erratic noise of multi-dimensional seismic data,and ensure transverse continuity of seismic signals after reconstruction.We compare the proposed framework with classical seismic data denoising methods and noise attenuation methods based on unsupervised learning to analyze the advantages and disadvantages of each method.The test results of 2D and 3D synthetic data and actual seismic data show that the proposed method has better noise attenuation and useful signal protection capabilities.关键词
深度学习/无监督学习/注意力机制/随机噪声/相干噪声Key words
deep learning/unsupervised learning/attention mechanism/random noise/coherent noise引用本文复制引用
杨柳青,王守东,杜宝强..基于注意力机制的无监督学习地震数据随机和不规则噪声衰减方法[J].石油科学通报,2024,9(1):35-49,15.基金项目
国家重点研发计划(2019YFC0312003),中国石油天然气集团有限公司-中国石油大学(北京)战略合作科技专项(ZLZX2020-03)和中国石油天然气集团有限公司科技管理部(物探应用基础实验和前沿理论方法研究2022DQ0604-04)联合资助 （2019YFC0312003）
