update README.md.

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

README.md

@@ -1,4 +1,4 @@
-### **🔍 Source-Independent Full Waveform Inversion for GPR using Deep Learning** 
+## **🔍 Source-Independent Full Waveform Inversion for GPR using Deep Learning** 
 
 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 suitable for GPR investigation in permafrost environments or undulating strata.
 
@@ -9,7 +9,7 @@ Program size: 98.6 Mb.
 Program language: Python 3.10.4 
 
 
-### 📌 Features:
+## 📌 Features:
 
 🚀 Source-independent inversion: The model learns to invert waveforms even with varying GPR sources.
 
@@ -20,7 +20,7 @@ Program language: Python 3.10.4
 📉 Support initial model or none, time-depth conversion, depth-time conversion, GSSI data conversion and other functions...
 
 
-### 📂 Project Structure
+## 📂 Project Structure
 
 GPR-SIDL-inv/
 
@@ -85,7 +85,7 @@ GPR-SIDL-inv/
     └── README.md                      # This file
 
 
-### 💡 Hardware requirements：
+## 💡 Hardware requirements：
 
 Please select the appropriate device according to your requirements. We conducted tests on a Dell laptop with an Intel(R) Core (TM) i7-12700H CPU (maximum physical memory of 15.7 GiB.) and an NVIDIA RTX 3070 Ti Laptop GPU (maximum physical memory of 7.8 GiB).
 
@@ -95,7 +95,7 @@ Please select the appropriate device according to your requirements. We conducte
 No external software. Python dependencies are listed in the requirements.txt. 
 
 
-### 🛠️ Installation
+## 🛠️ Installation
 
 ### 1. Clone the repository
 
@@ -112,7 +112,7 @@ link: https://www.gprmax.com/
 pip install -r requirements.txt
 
 
-### 📊 Usage
+## 📊 Usage
 
 We provide an interface program for calling gprMax to generate custom datasets. If you need to generate a dataset, you can start running it from the first step. If you already have a simulated dataset, you can skip the first three steps. Steps four to six are used for personalized processing of our measured data, and we provide a frozen soil area measured data for testing. If you need to test new field data, please make targeted modifications. We provide a well generated 70MHz Rayleigh wavelet forward modeling dataset to support users in using it directly without having to train the dataset themselves. We also provide on-site collection of permafrost ground penetrating radar data on the Qinghai Tibet Plateau, as well as corresponding trained network models, for users to test our program. Users can directly run the inversion program for testing.
 
@@ -143,7 +143,7 @@ Step8: Run 8_prediction.py    Predicting real measured data. Please refer to the
 Step9: Run 9_time_depth_convert.py    Convert the predicted results into the deep domain through integration.  Please refer to the corresponding documents for specific requirements and details.
 
 
-### 🙏 Acknowledgements
+## 🙏 Acknowledgements
 
 We sincerely thank the developers of the following open-source tools used in this project:
 
@@ -155,7 +155,7 @@ Both gprMax and readgssi are licensed under the GNU General Public License v3 (G
 We are grateful for their contributions to the scientific and open-source ecosystem.
 
 
-### 📝 Citation
+## 📝 Citation
 
 If you use this code in your research, please cite:
 
@@ -176,7 +176,7 @@ If you wish to use this software for commercial purposes and cannot comply with
 Please contact us at [sunhuaifeng@email.sdu.edu.cn] for further details.
 
 
-### 📫 Contact
+## 📫 Contact
 
 If you have questions, reach out to: