High-resolution seismic fault detection with adversarial neural networks and regularization

A method for high-resolution seismic fault detection with an adversarial neural network, wherein the method comprises following steps of:

• training a target adversarial neural network based on a preset training sample set to obtain a trained target adversarial neural network, wherein the preset training sample set comprises seismic data and fault labels, the target adversarial neural network comprises:
• a segmentation module, a feature fusion module, and a discriminator module, the segmentation module is a module configured for obtaining a fault feature based on the preset training sample set, and the feature fusion module is a module configured for fusing the fault feature and the seismic data into a global feature map;
• and
• performing seismic fault detection on a target seismic image based on the trained target adversarial neural network;
• wherein the step of training a target adversarial neural network based on a preset training set comprises:
• a first training step A:
• training the segmentation module by utilizing the preset training sample set based on a balanced cross entropy loss function, so as to obtain a trained segmentation module;
• a predicting step B:
• substituting the preset training sample set into the trained segmentation module to obtain a predicted fault feature;
• a fusing step C:
• fusing the seismic data and the predicted fault feature into a global feature map based on the feature fusion module;
• a second training step D:
• training the discriminator module by utilizing the global feature map based on a categorical cross entropy loss function, so as to obtain a trained discriminator module;
• a discriminating step E:
• substituting the global feature map into the trained discriminator module to obtain a discriminative difference value;
• and
• an updating step F:
• updating the balanced cross entropy loss function based on the discriminative difference value and a regularization loss function, and
• repeating the steps A through F from the first training step to the updating step, and finishing the training if the discriminative difference value is less than a preset threshold value;
• and
• wherein the step of fusing the seismic data and the predicted fault feature into a global feature map based on the feature fusion module comprises:
• performing local feature inversion on the predicted fault feature to obtain a degree of attention of the predicted fault feature;
• and
• calculating the dot product of the degree of attention and the seismic data, and performing normalization processing of local contrast, so as to obtain the global feature map.