Model implementation, models with trained weights, and sample inversion results of "Implicit Neural Representation to Improve the Resolution of 3D Electrical Resistivity"

Detailed Description of Each File1. INR.zipContents: Raw data used in the Implicit Neural Representation method (e.g., input coordinates, observed data) and the corresponding model outputs after training (e.g., reconstructed resistivity fields, predicted values). Format: ZIP archive; after extraction, typical files include .npy, .mat, .txt, .csv, or custom formats. Usage: Reproduce INR model predictions or serve as input for further analysis. 2. Numerical.zipContents: Raw data and model outputs from the Res2Dinv and Res3Dinv methods (e.g., synthetic/field ERT data and inverted resistivity distributions). Format: ZIP archive; internal file structure follows the standard input/output format of the respective methods. Usage: Compare with INR results to validate the proposed method. 3. Implicit Neural Representation.ipynbType: Jupyter Notebook Function: Implements the complete training pipeline for the Implicit Neural Representation method, including data loading, network definition, loss function, training loop, and model saving. Outputs: Training logs, loss curves, intermediate results, and final model weights (likely saved inside INR.zip or a separate folder). Dependencies: See "Environment Requirements" below. 4. Forward Modeling ERT (update).ipynbType: Jupyter Notebook Function: Performs ERT forward modeling to compute potential/apparent resistivity responses given a resistivity model. Includes updated parameter settings, solver configurations, and result visualization. Outputs: Forward response data, mesh information, synthetic datasets (which may be used as inputs in INR.zip or Numerical.zip). Applicability: Serves as a common data generation source for both INR and traditional inversion methods. 5. Figures and Analysis of ERT-3D-INR paper.ipynbType: Jupyter Notebook Function: Generates all figures in the main text of the *ERT-3D-INR* paper (e.g., comparison plots, error maps, cross-sections, convergence curves) and performs statistical analysis of the results. Inputs: Reads output files from INR.zip, Numerical.zip, and the forward modeling notebook. Outputs: Exportable .png, .pdf, or vector graphics files identical to those shown in the paper. Usage InstructionsDownload and extractUnzip INR.zip and Numerical.zip into the current directory (or keep them zipped if the scripts support auto-reading). Set up the environment (recommended: conda or pip)Main dependencies may include: Python 3.8+ Jupyter Notebook / Lab NumPy, SciPy, Matplotlib PyTorch / TensorFlow (depending on the INR implementation) ERT forward modeling tools (e.g., pyGIMLi, Res2Dinv call interfaces)(Please add a complete list based on actual requirements) Suggested execution order To regenerate forward data: run Forward Modeling ERT (update).ipynb To train the INR model: run Implicit Neural Representation.ipynb For traditional methods: extract Numerical.zip and ensure output file paths are correct Final plotting: run Figures and Analysis of ERT-3D-INR paper.ipynb Important NotesThis repository does not include raw measured data (if applicable). All synthetic data are generated via forward modeling or predefined models. The .ipynb files depend on specific file paths; please keep the default directory structure. To reproduce all computations, run the notebooks in the suggested order and ensure intermediate results are saved properly. CitationIf you use the data or code from this repository, please cite the following paper (to be filled): Authors, Title, Journal/Conference, Year.