dc.description.abstract | Today’s resistivity imaging processes not only take a long time to get one result, but also sensitive to human manipulation, making it difficult to apply to an automatic system. Some studies using neural networks have attempted to solve this problem, but neural networks in these studies have less inference power than modern deep neural networks. Besides, these studies haven’t released a toolbox for reference, so those who study the same research field need to reproduce the corresponding workflow. In the light of this, the goal of my study is to develop a resistivity imaging toolbox based on deep learning. The forward simulation part of this toolbox is a potential simulation program developed by Pidlisecky & Knight (2008); the neural network part is built upon Keras with Tensorflow backbone.
We used synthetic data and field data to test the electrical resistivity imaging process established in this study. In the synthetic data section, we designed five configurations of training data for training neural networks. Then, we used layered, double-block and multi-block resistivity models to evaluate the predictive ability of neural networks. From the result of resistivity profiles, we can observe the effects of different operations on training data. In addition, we compared the resistivity profiles produced by EarthImager 2D and neural networks to observe the differences between these profiles. In the field data section, we used the data measured by the north-south array at the Yongkang site from October 10 to October 21, 2015. The remediation reagent injection was implemented at the Yongkang site from October 13 to October 18, 2015. Whether the change in resistivity caused by the injection can be observed in the resistivity profile can be used as an indicator for evaluating the imaging procedure of this study. For field data testing, we designed two training dataset, one based on common geological materials and the other based on core data. The final result shows that the neural network trained based on prior information has a good predictive power for YongKang site. However, the quality of the field data is not good, making it difficult to obtain a well resistivity profile even with conventional inversion. Therefore, it is difficult to observe the influence of the remediation reagent.
The toolbox constructed in this study is easily embedded in the present data processing system. The trained model can greatly reduce the actual imaging time, and it may be applied to the same array with similar site conditions. More importantly, the workflow of this toolbox can add different observations data and can be used as a reference for different inversion problems. However, this toolbox has both advantages and disadvantages. The training phase requires a lot of computational resource, the predicted resistivity profile is difficult to see the fine structure, and the data quality and missing values affect the prediction results. Thus, we will continuously improve this toolbox in the future.
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