update README.md.

Signed-off-by: 葛峻恺 <202115006@mail.sdu.edu.cn>

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