Automatic fault recognition in seismic data based on AHRFaultSegNet deep learning network
Fault recognition is an essential step in seismic data interpretation.The development of deep learning has effectively improved the efficiency and accuracy of automatic fault recognition.However,in automatic fault recognition,it is still challenging to accurately capture subtle structures of faults and effectively resist noise in-terference.Thus,in this study,we propose a high-resolution fault recognition network model,AHRFaultSeg-Net,based on the HRNet network and decoupled self-attention mechanisms.The decoupling of self-attention mechanisms combines spatial attention and channel attention,replacing parallel convolution layers in HRNet.This reduces the computational amount of traditional self-attention mechanisms while allowing the model to cal-culate the relevance of input feature on a global scale,thus more accurately modeling non-local features.In de-coupled self-attention,the residual connection is employed to preserve the original feature,speeding up model training and better maintaining detailed information.Experimental results demonstrate that the proposed net-work model outperforms other common automatic fault recognition network models in performance evaluation indexes such as Dice,Fmeasure,IoU,Precision,and Recall.Through fault recognition experiments on syn-thetic seismic data and actual seismic data,this method is proven to be effective in subtle fault structure identifi-cation and robust in noise resistance.
fault detection and recognitiondeep learningdecoupled self-attention mechanismresidual con-nection
万方数据
维普
