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https://gitee.com/sduem/gpr-sidl-inv.git
已同步 2025-08-06 11:56:52 +08:00
123 行
3.8 KiB
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123 行
3.8 KiB
Markdown
### **🔍 Source-Independent Full Waveform Inversion for GPR using Deep Learning**
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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.
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### 📌 Features:
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🚀 Source-independent inversion: The model learns to invert waveforms even with varying GPR sources.
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🧠 Noise simulation: The dataset can simulate real noisy environments.
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🌍 Using gprMax to establish reliable datasets in three-dimensional simulation scenarios
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📉 Support initial model or none, time-depth conversion, depth-time conversion, GSSI data conversion and other functions...
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### 📂 Project Structure
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GPR-SIDL-inv/
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├── dataset/ # Training/testing data and synthetic datasets
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├── field_data/ # Used to store field data for inversion
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├── gprMax/ # Forward modeling package developed by the University of Edinburgh
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├── IMG/ # Used for storing data processing and inversion result graphs
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├── impulse/ # Used to store simulated and measured source wavelet files
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├── log/ # operation log
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├── Network/ # Used for storing network models and data loading programs
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├── readgssi/ # Software package for reading and converting raw data
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├── SAVE/ # Save the trained model
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├── time_result_csv/ # Inverse results in the time domain
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├── utils/ # tool kit
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├── 1_model_generator.py # Randomly generate in files as needed to support forward modeling of gprMax in 3D media.
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├── 2_forward_simulation.py # Run the forward modeling program to generate A-scan results
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├── 3_combine_dataset.py # Filter all A-scan data and generate a dataset
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├── 4_gssi_data_convert.py # Convert the dzt file of the measured GSSI GPR to CSV format
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├── 5_data_preprocess.py # Preprocess the measured raw data (dewow, direct wave removal, static correction, etc.)
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├── 6_extract_impulse.py # Extract the true source wavelet from the processed data
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├── 7_network_train.py # Training a deep learning network for inversion
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├── 8_prediction.py # Predicting real measured data
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├── 9_time_depth_convert.py # Convert the predicted results into the deep domain through integration
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├── config.yaml # Configuration file
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├── requirements.txt # Python dependencies
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└── README.md # This file
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### 🛠️ Installation
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### 1. Clone the repository
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git clone https://gitee.com/sduem/gpr-sidl-inv
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cd GPR-FWI-DeepLearning
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### 2. Install gprMax (optional but recommended)
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https://www.gprmax.com/
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### 3. Install dependencies
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pip install -r requirements.txt
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### 📊 Usage
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### 1. Dataset generation
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.......
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.......
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### 2. Field data convertion and prepocessing
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https://www.gprmax.com/
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### 3. Source wavelet select and network training
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### 4. Prediction and time-depth convertion
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https://www.gprmax.com/
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### 📝 Citation
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If you use this code in your research, please cite:
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@article{your2025sourceindependent,
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title={Source-independent Full Waveform Inversion for GPR Using Deep Learning},
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author={Junkai Ge, Rui Liu, Shirong Zhang, Xiaodong Li, Huaifeng Sun, Bo Tian, Ziqiang Zheng},
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journal={Computers & Geosciences},
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year={2025},
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doi={under review}
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}
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### 📫 Contact
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If you have questions, reach out to:
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Rui Liu
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Email: your.email@domain.com
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Affiliation: Institute / Lab Name
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