Intelligent Recognition Method of Geological Fault Based on Deep Learning of Convolutional Neural Network
In order to solve the problem that traditional machine learning methods have poor ability to identify fault structures,a UNet++network structure model based on convolutional neural network is pro-posed to identify geological faults.Different attention mechanisms and loss functions are introduced in the es-tablishment of the model,which can better realize semantic deep learning and feature fusion.Correlation in-dex analysis and image analysis are also carried out.The results show that the prediction graph correspond-ing to WCE loss function has the clearest output effect,and ECA+UN++model has the best training effect using WCE loss function,and the recognition accuracy is higher.ECA+UN++model with WCE loss func-tion is applied in the fault area of Guanduhe Coal Mine,which can identify the fault location intelligently and deal with the noise reduction of underground noise well.It shows that the UNet++network structure mod-el with ECA attention mechanism can effectively improve the efficiency and accuracy of fault recognition.
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