Controllable source electromagnetic inversion utilizes artificial signals to obtain underground structural information,witch can provide accurate data support for geological exploration and resource development. However,traditional electromagnetic inversion methods face the challenge of low resolution,mainly due to the limitations of simplified processing and observation data. As a result,traditional model smoothness and loss of details have weakened the accuracy of inversion and affected the efficiency of electromagnetic exploration. To address this issue,this work proposed to use both traditional inversion results and response data as input data for deep network inversion,providing prior physical information for deep network inversion. Combined with deep learning algorithms,the computational efficiency of controllable source electromagnetic inversion was improved. Through model experiments,traditional inversion,intelligent inversion,and intelligent inversion integrating prior physical information were performed on the synthesized resistivity model. The results indicate that intelligent inversion based on prior physical information can better characterize the structural characteristics of anomalous bodies,effectively improve inversion efficiency,and obtain resistivity parameters that are more in line with reality. Finally,the inversion technique was applied to the controllable source data inversion interpretation of the Jinchuan copper-nickel deposit,and achieved relatively reliable application results.
维普
万方数据