Artificial Intelligence-Based Rock Fracture Prediction Method for Multi-Physics Monitoring

An artificial intelligence-based multi-physics monitoring method for rock fracture prediction, comprising conducting laboratory rock physical hydraulic fracturing experiments and performing multi-physics monitoring as follows:

• constructing a training dataset, by performing the aforementioned experiments on different rock samples to obtain data, forming a total dataset, wherein the data includes:
• CT structural images with piezoelectric ceramic sensor metal artifacts removed, rock fusion fracture images, acoustic emission location distribution maps, ultrasonic inversion velocity maps, b-values calculated based on acoustic emission events divided into time segments, axial pressure curves, confining pressure curves, injection pressure curves, injection rate curves, and injection volume curves;
• constructing a fracture prediction module, which is built based on a multi-layer ConvLSTM network;
• training the detection network using the training dataset;
• preprocessing all actually monitored data using the same steps, inputting observation data before time t, and obtaining the predicted fracture result at time t+1;
• updating the dataset and the fracture prediction module.