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| A site pollution nonlinear inversion method based on deep convolutional neural network |
| NAI Chang-xin1,2, SUN Xiao-chen3, XU Ya2, LIU Jia-lin1, DONG Lu2, LIU Yu-qiang2 |
1. School of Information and Electronic Engineering, Shandong Technology and Business University, Shandong 264005, China;
2. State Key Laboratory of Environmental Benchmarks and Risk Assessment, Research Institute of Solid Waste Management, Chinese Research Academy of Environment Science, Beijing 100012, China;
3. School of Computer Science and Technology, Shandong Technology and Business University, Shandong 264005, China |
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Abstract A novel nonlinear method named E-ConvNet was proposed for ERT inversion of contaminated sites, which combined Sobel edge detection operator with deep convolutional neural network algorithm (CNN). The edge features of apparent resistivity data in contaminated sites were extracted by the Sobel operator as the prior information feed into CNN, which improved computational efficiency and identification accuracy of E-ConvNet. The performance of E-ConvNet algorithm were testedon five theoretical model data (single anomaly, double anomalies, and layered structures with double anomalies) and field data, and then compared with results from the traditional Least Squares (LS) algorithm. The results showed that E-ConvNet can accurately identify the area, location and resistance of pollution, and its accuracy and computing efficiency were better than those of LS. The single anomaly recognition accuracy of E-ConvNet and LS were 81.8%~84.9% and 9.6%~36.2%, respectively; the multiple anomalies recognition accuracy of E-ConvNet and LS were 68.6%~84.4% and 2.8%~27.6%, respectively; the computing time of E-ConvNetis about 112~190ms,and the computing time of LS was 6000~7000ms. Therefore, E-ConvNet proposed in this study can accurately and efficiently inversed the polluted areas in the investigation of contaminated sites and provide technical support for the follow-up assessment/restoration work.
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Received: 14 May 2019
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