### **🔍 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...


### 📂 Project Structure

GPR-SIDL-inv/

├── dataset/                   # Training/testing data and synthetic datasets

├── field_data/                # Used to store field data for inversion

├── gprMax/                    # Forward modeling package developed by the University of Edinburgh

├── IMG/                       # Used for storing data processing and inversion result graphs

├── impulse/                   # Used to store simulated and measured source wavelet files

├── log/                       # operation log

├── Network/                   # Used for storing network models and data loading programs

├── readgssi/                  # Software package for reading and converting raw data

├── SAVE/                      # Save the trained model

├── time_result_csv/           # Inverse results in the time domain

├── utils/                     # tool kit


├── 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