Research on the identification of electrical anomalies of dam leakage based on end-to-end deep learning
[Objective]The traditional method for identifying electrical anomalies in electrical prospecting are computationally complex,inefficient,and highly reliant on the initial model for result interpretation,which makes it challenging to meet the ap-plication demands of rapid processing of massive detection data.[Methods]In this regard,combined with the idea of deep learn-ing,an end-to-end apparent resistivity identification network(Apparent Resistivity Network,ARNet)model for dams was pro-posed,converting the traditional leakage identification method into a problem of nonlinear mapping from the input apparent resis-tivity data to the output distribution of anomalous bodies.The pyGIMLi finite element tool was used to establish 2.2×104 dam leakage geoelectric models with different shapes,locations and resistivity values,allowing for the computation of the distribution of apparent resistivity forms.A network training dataset was constructed by performing batch extraction and interpolation grid pro-cessing on simulated data.The network model construction was completed using network front-end,back-end,and post-process-ing techniques.The loss value and intersection over union were employed as model performance indicators,and iterative optimi-zation of model weight parameters was carried out using a stochastic gradient descent algorithm.[Results]The result showed that after 1000 epochs of training,the ARNet model achieved a loss value reduction to 0.068 and an intersection over union of 94.60％.During comparative testing involving single and double anomalous bodies with varying resistivity,positions,and shapes,the model demonstrated a matching accuracy of 97.66％and an error below 0.257 m.[Conclusion]Combining experi-mental data from reservoir dam measurements,the ARNet model demonstrates excellent generalization performance.In compari-son to traditional inversion method,it achieves highly accurate intelligent identification of anomalies.The research outcomes ex-pand the application of deep learning technology in dam safety.
dam leakageapparent resistivitynetwork modeldeep learningnumerical simulation
万方数据
维普
