From 10f0f77a630d8290726b5d018685f603358349e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=91=9B=E5=B3=BB=E6=81=BA?= <202115006@mail.sdu.edu.cn> Date: Fri, 4 Apr 2025 03:02:25 +0000 Subject: [PATCH] update README.md. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 葛峻恺 <202115006@mail.sdu.edu.cn> --- README.md | 30 ++++++++++++++++++++---------- 1 file changed, 20 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index f07c239..057ac30 100644 --- a/README.md +++ b/README.md @@ -20,39 +20,49 @@ GPR-SIDL-inv/ ├── dataset/ # Training/testing data and synthetic datasets -├── field_data/ # Network architectures +├── field_data/ # Used to store field data for inversion -├── gprMax/ # Data processing, waveform simulation, etc. +├── gprMax/ # Forward modeling package developed by the University of Edinburgh -├── IMG/ # Training script +├── IMG/ # Used for storing data processing and inversion result graphs -├── impulse/ # Inversion / prediction script +├── impulse/ # Used to store simulated and measured source wavelet files -├── log/ # Custom loss functions +├── log/ # operation log -├── Network/ # Custom loss functions +├── Network/ # Used for storing network models and data loading programs -├── readgssi/ # Inversion / prediction script +├── readgssi/ # Software package for reading and converting raw data -├── SAVE/ # Custom loss functions +├── SAVE/ # Save the trained model -├── time_result_csv/ # Custom loss functions +├── time_result_csv/ # Inverse results in the time domain -├── utils/ # Custom loss functions +├── 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