Low-order fault identification method based on SA-VNet convolutional neural network
Fault identification is a crucial step in seismic structure interpretation,serving as the basis for locating promising areas for oil and gas exploration.In strike-slip fault regions,low-order faults can develop,which are difficult to identify using conventional fault identification methods and traditional convolutional neural networks,posing significant challenges for oil and gas exploration and development in complex areas.In order to overcome this issue and improve training efficiency,we propose a novel fault identification method,SA-VNet,which incorporates the Spatial Attention mechanism into the V-Net network.The proposed method utilizes semantic segmentation technology to classify each data point of the input data body as either fault or non-fault,generating a probability body of the fault distribution.We introduce dynamic learning rate adjustment to improve the training efficiency and achieve superior training outcomes with fewer rounds.The feasibility of the proposed method is verified through theoretical modeling,and we further demonstrate its effectiveness in identifying low order faults in actual working areas.
Fault identificationLow order faultConvolutional neural networkSA-VNet
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