Reconstruction of randomly missing seismic data using XGBoost algorithm
With the increasing complexity of the structure and the surface geological conditions of the explora-tion target,the problems of irregular and incomplete data often occur in the process of seismic data acquisition,which brings serious difficulties to the follow-up data processing.To solve this problem,this paper proposes a seismic data reconstruction method based on the XGBoost algorithm.From the perspective of local learning,this method selects a certain number of adjacent seismic traces around the randomly missing seismic traces as a reference.By constructing the regression model between the trace numbers,sampling point numbers and their values of the reference seismic traces,the missing seismic trace data can be accurately learned and recon-structed.In order to fully evaluate the performance of the proposed method,the experiments are performed on simulated data with different missing seismic traces,and the reconstruction methods such as U-net convolutional neural network and Curvelet algorithm based on projection onto convex sets are compared.The experimental re-sults show that the reconstruction method based on the XGBoost algorithm presents high accuracy in the recon-struction of randomly missing seismic data.The actual data processing results show that this method can pro-vide high-precision regular shot gather for the follow-up seismic data processing.
seismic data reconstructionXGBoost algorithmprojection onto convex setsmachine learningU-net
万方数据
维普
