update README.md.

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

README.md

@@ -2,6 +2,9 @@
 
 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.
 
+Program name: GPR-SIDL-inv
+Program size: 98.6 Mb.
+Program language: Python 3.10.4 
 
 ### 📌 Features:
 
@@ -18,33 +21,37 @@ A PyTorch-based implementation of a source-independent full waveform inversion (
 
 GPR-SIDL-inv/
 
-├── dataset/                   # Training/testing data and synthetic datasets
+├── dataset/                       # Training/testing data and synthetic datasets
 
     ├── data.csv                   # Already generated dataset (70MHz Ricker wavelet)
 
-    ├── label.csv                   # Already generated dataset (70MHz Ricker wavelet) 
-
-├── field_data/                # Used to store field data for inversion
-
-├── IMG/                       # Used for storing data processing and inversion result graphs
-
-├── impulse/                   # Used to store simulated and measured source wavelet files
-
-├── log/                       # operation log
-
-├── Network/                   # Used for storing network models and data loading programs
-
-├── readgssi/                  # Software package for reading and converting raw data
-
-├── SAVE/                      # Save the trained model
-
-├── time_result_csv/           # Inverse results in the time domain
-
-├── utils/                     # tool kit
-
-    ├── Model.py/                     # network model with transformer
+    ├── label.csv                  # Already generated dataset (70MHz Ricker wavelet) 
     
-    ├── Mydataset.py/                     # Loading and preprocessing datasets
+    （Note: The pre generated data.csv and label.csv datasets have been compressed into the dataset.rar file package）
+
+├── field_data/                    # Used to store field data for inversion
+
+├── IMG/                           # Used for storing data processing and inversion result graphs
+
+├── impulse/                       # Used to store simulated and measured source wavelet files
+
+├── Log/                           # operation log
+
+├── Network/                       # Used for storing network models and data loading programs
+
+├── readgssi/                      # Software package for reading and converting raw data
+
+├── SAVE/                          # Save the trained model
+
+├── time_result_csv/               # Inverse results in the time domain
+
+├── utils/                         # tool kit
+
+    ├── Model.py/                  # network model with transformer
+    
+    ├── Mydataset.py/              # Loading and preprocessing datasets
+
+    ├── plot.py/                   # the tool kit for plotting the 2D image 
 
 ├── 1_model_generator.py           # Randomly generate in files as needed to support forward modeling of gprMax in 3D media.
 
@@ -64,13 +71,22 @@ GPR-SIDL-inv/
 
 ├── 9_time_depth_convert.py        # Convert the predicted results into the deep domain through integration
 
-├── config.yaml                    # Configuration file
+├── config.yaml                    # Configuration file, used to define all paths, variables, and parameters
 
 ├── requirements.txt               # Python dependencies
 
 └── README.md                      # This file
 
 
+### 💡 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).
+
+### 📚 Software requirements
+
+No external software. Python dependencies are listed in the requirements.txt. 
+
+
 ### 🛠️ Installation
 
 ### 1. Clone the repository
@@ -79,9 +95,9 @@ git clone https://gitee.com/sduem/gpr-sidl-inv
 
 cd GPR-FWI-DeepLearning
 
-### 2. Install gprMax (optional but recommended). link: https://www.gprmax.com/ 
+### 2. Install gprMax (optional but recommended).  
 
-https://www.gprmax.com/ 
+link: https://www.gprmax.com/ 
 
 ### 3. Install dependencies