High Density Electrical Apparent Resistivity Anomaly Intelligent Interpretation Method Based on SSD Target Detection Algorithm
The high-density electrical method has been widely used in detecting underground karst cave disease bodies in limestone areas,but the inversion results of the high-density electrical method rely heavily on the initial model and have multiple solutions.Geological in-terpretation is easily affected by subjective factors of professionals.Therefore,this article studies the intelligent interpretation method technology of apparent resistivity anomalies based on the deep learning SSD(single shot multi-box detector)object detection algorithm,starting from unique apparent resistivity data.In response to karst geological disasters,elec-trical anomaly models of karst caves with different filling types,shapes,scales and quantities were designed.Res2dmod software was used for forward calculation of apparent resistivity,and a high-density electrical method-based intelligent interpretation and learning sample li-brary(samples and labels)containing 1 400 samples was constructed.Based on the Tensor-Flow framework and deep learning SSD algorithm,a high-density electrical resistivity anom-aly intelligent interpretation technology was established,where the SSD algorithm weights were trained using a learning sample library,and intelligent interpretation numerical experi-ments were conducted on the apparent resistivity anomaly of the high-density electrical resis-tivity Wenner device.The learning sample library was trained and tested,with less than 1 second intelligent interpretation time for a single apparent resistivity profile anomaly,and the average accuracy of various targets(filled and unfilled karst caves)at 90.68％,and the error in interpreting the scale and location of karst caves at centimeter level.The results show that the intelligent interpretation technology of high-density electrical resistivity anom-alies based on SSD algorithm significantly improves the efficiency of high-density electrical resistivity interpretation,while avoiding the interference of subjective factors by profession-als on the interpretation results.The category,location and scale of anomalies in the appar-ent resistivity cross-section can be accurately interpreted,with an average accuracy rate of 90.68％for various targets.The SSD algorithm based on deep learning can be used for in-telligent interpretation of high-density electrical resistivity anomalies.
high density electrical methodWenner arrayapparent resistivitySSD object detection algorithmartificial intelligent interpretation
NETL
NSTL
万方数据
维普
