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镜像自地址 https://gitee.com/sduem/gpr-sidl-inv.git 已同步 2025-08-03 10:56:50 +08:00
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Signed-off-by: 葛峻恺 <202115006@mail.sdu.edu.cn>
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@@ -21,61 +21,61 @@ Program language: Python 3.10.4
GPR-SIDL-inv/
├── dataset/ # Training/testing data and synthetic datasets
├── dataset/ # Training/testing data and synthetic datasets
├── data.csv # Already generated dataset (70MHz Ricker wavelet)
├── data.csv # Already generated dataset (70MHz Ricker wavelet)
├── label.csv # Already generated dataset (70MHz Ricker wavelet)
├── label.csv # Already generated dataset (70MHz Ricker wavelet)
（Note: The pre generated data.csv and label.csv datasets have been compressed into the dataset.rar file package）
（Note: The pre generated data.csv and label.csv datasets have been compressed into the dataset.rar file package）
├── field_data/ # Used to store field data for inversion
├── field_data/ # Used to store field data for inversion
├── IMG/ # Used for storing data processing and inversion result graphs
├── IMG/ # Used for storing data processing and inversion result graphs
├── impulse/ # Used to store simulated and measured source wavelet files
├── impulse/ # Used to store simulated and measured source wavelet files
├── Log/ # operation log
├── Log/ # operation log
├── Network/ # Used for storing network models and data loading programs
├── Network/ # Used for storing network models and data loading programs
├── readgssi/ # Software package for reading and converting raw data
├── readgssi/ # Software package for reading and converting raw data
├── SAVE/ # Save the trained model
├── SAVE/ # Save the trained model
├── time_result_csv/ # Inverse results in the time domain
├── time_result_csv/ # Inverse results in the time domain
├── utils/ # tool kit
├── utils/ # tool kit
├── Model.py/ # network model with transformer
├── Model.py/ # network model with transformer
├── Mydataset.py/ # Loading and preprocessing datasets
├── Mydataset.py/ # Loading and preprocessing datasets
├── plot.py/ # the tool kit for plotting the 2D image
├── plot.py/ # the tool kit for plotting the 2D image
├── 1_model_generator.py # Randomly generate in files as needed to support forward modeling of gprMax in 3D media.
├── 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
├── 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
├── 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
├── 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.)
├── 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
├── 6_extract_impulse.py # Extract the true source wavelet from the processed data
├── 7_network_train.py # Training a deep learning network for inversion
├── 7_network_train.py # Training a deep learning network for inversion
├── 8_prediction.py # Predicting real measured data
├── 8_prediction.py # Predicting real measured data
├── 9_time_depth_convert.py # Convert the predicted results into the deep domain through integration
├── 9_time_depth_convert.py # Convert the predicted results into the deep domain through integration
├── config.yaml # Configuration file, used to define all paths, variables, and parameters
├── config.yaml # Configuration file, used to define all paths, variables, and parameters
├── requirements.txt # Python dependencies
├── requirements.txt # Python dependencies
└── README.md # This file
└── README.md # This file
### 💡 Hardware requirements：