update README.md.

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

README.md

@@ -8,6 +8,7 @@ Program size: 98.6 Mb.
 
 Program language: Python 3.10.4 
 
+
 ### 📌 Features:
 
 🚀 Source-independent inversion: The model learns to invert waveforms even with varying GPR sources.
@@ -88,6 +89,7 @@ GPR-SIDL-inv/
 
 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).
 
+
 ### 📚 Software requirements
 
 No external software. Python dependencies are listed in the requirements.txt. 
@@ -109,6 +111,7 @@ link: https://www.gprmax.com/
 
 pip install -r requirements.txt
 
+
 ### 📊 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.
@@ -135,9 +138,22 @@ Step7: Run 7_network_train.py    Training a deep learning network for inversion.
 
 ###  4. Prediction and time-depth convertion 
 
-Step8: Prediction.py    Predicting real measured data. Please refer to the corresponding documents for specific requirements and details.
+Step8: Run 8_prediction.py    Predicting real measured data. Please refer to the corresponding documents for specific requirements and details.
+
+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
+
+We sincerely thank the developers of the following open-source tools used in this project:
+
+- **gprMax**: An open-source electromagnetic wave simulation software widely used for Ground Penetrating Radar (GPR) modeling and research.  
+- **readgssi**: An open-source Python tool designed to read and process GPR data collected by GSSI equipment.
+
+Both gprMax and readgssi are licensed under the GNU General Public License v3 (GPL-3.0), a strong copyleft license approved by the Open Source Initiative (OSI), and widely used in global and Chinese open-source communities.
+
+We are grateful for their contributions to the scientific and open-source ecosystem.
 
-Step9: Run 7_network_train.py    Convert the predicted results into the deep domain through integration.  Please refer to the corresponding documents for specific requirements and details.
 
 ### 📝 Citation