update README.md.

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

README.md

@@ -1,37 +1,45 @@
-# GPR-SIDL-inv.
+### **🔍 Source-Independent Full Waveform Inversion for GPR using Deep Learning** 
 
-#### 介绍
-Deep Learning-Based GPR Imaging for Tibetan Plateau Permafrost: A Source Wavelet-Independent Inversion Approach
-
-#### 软件架构
-软件架构说明
+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.
 
 
-#### 安装教程
+### 📌 Features:
 
-1.  xxxx
-2.  xxxx
-3.  xxxx
+🚀 Source-independent inversion: The model learns to invert waveforms even with varying GPR sources.
 
-#### 使用说明
+🧠 Noise simulation: The dataset can simulate real noisy environments.
 
-1.  xxxx
-2.  xxxx
-3.  xxxx
+🌍 Using gprMax to establish reliable datasets in three-dimensional simulation scenarios
 
-#### 参与贡献
-
-1.  Fork 本仓库
-2.  新建 Feat_xxx 分支
-3.  提交代码
-4.  新建 Pull Request
+📉 Support initial model or none, time-depth conversion, depth-time conversion, GSSI data conversion and other functions...
 
 
-#### 特技
+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
+
 
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