gpr-sidl-inv/README.md

### **🔍 Source-Independent Full Waveform Inversion for GPR using Deep Learning** 

A PyTorch-based implementation of a source-independent full waveform inversion (FWI) framework tailored for ground-penetrating radar (GPR) data, leveraging deep learning techniques to reconstruct subsurface permittivity models. This method is particularly suitable for GPR investigation in permafrost environments or undulating strata.


### 📌 Features:

🚀 Source-independent inversion: The model learns to invert waveforms even with varying GPR sources.

🧠 Noise simulation: The dataset can simulate real noisy environments.

🌍 Using gprMax to establish reliable datasets in three-dimensional simulation scenarios

📉 Support initial model or none, time-depth conversion, depth-time conversion, GSSI data conversion and other functions...


GPR-SIDL-inv/
│
├── dataset/                   # Training/testing data and synthetic datasets
├── field_data/                 # Network architectures
├── gprMax/                  # Data processing, waveform simulation, etc.
├── IMG/                # Training script
├── impulse/                 # Inversion / prediction script
├── log/       # Custom loss functions
├── Network/       # Custom loss functions
├── readgssi/                 # Inversion / prediction script
├── SAVE/       # Custom loss functions
├── time_result_csv/       # Custom loss functions
├── utils/       # Custom loss functions

├── 1_model_generator.py           # Randomly generate in files as needed to support forward modeling of gprMax in 3D media.
├── 2_forward_simulation.py        # Run the forward modeling program to generate A-scan results
├── 3_combine_dataset.py           # Filter all A-scan data and generate a dataset
├── 4_gssi_data_convert.py         # Convert the dzt file of the measured GSSI GPR to CSV format
├── 5_data_preprocess.py           # Preprocess the measured raw data (dewow, direct wave removal, static correction, etc.)
├── 6_extract_impulse.py           # Extract the true source wavelet from the processed data
├── 7_network_train.py             # Training a deep learning network for inversion
├── 8_prediction.py                # Predicting real measured data
├── 9_time_depth_convert.py        # Convert the predicted results into the deep domain through integration

├── config.yaml                    # Configuration file
├── requirements.txt               # Python dependencies
└── README.md                      # This file