2.6 KiB
🔍 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