The codes are used in paper Deep learning for locating contaminated sites from self-potential signals: a laboratory perspective.
SPNet is an useful tool to predict locations of SP sources, which helps to monitor and locate contaminated sites and address environmental problems.
- Download the python package described in requirement.txt.
- Windows 10
- Matlab 2020a
- SPinversion.py: The main program to train the SPNet with normalized SP signal with regarding sources.
- testdatasets.py: Codes for evaluating the SPNet with test datasets
- plotdata.py: Example of 3D plot in this paper. In this code, the lab test dataset is used.
- Code for SP: 3D Forward modeling codes for SP signal generation by finite-infinite element coupling method.
You can find the dataset used in this research at https://doi.org/10.5281/zenodo.6781446
You can contact author Hang Chen (hangchen@u.boisestate.edu)
I want to thank https://github.com/LeeJunHyun/Image_Segmentation#r2u-net for providing U-Net codes. This study modifies the codes to fit our problems.