update README.md.

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

README.md

@@ -109,7 +109,7 @@ pip install -r requirements.txt
 
 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.
 
-### ### 1. Dataset generation
+###  1. Dataset generation
 
 Step1: Run program 1_model_generator.py   Generate the "in" files for simulation. Set the dataset model parameters, including model num, wavelet, size, permittivity range, etc. Please refer to the corresponding documents for specific requirements and details.
 
@@ -117,7 +117,7 @@ Step2: Run program 2_forward_simulation.py   Generate the results of formward si
 
 Step3: Run program 3_combine_dataset.py   Filter, sort, normalize, and integrate the forward modeling data into a dataset. Please refer to the corresponding documents for specific requirements and details.
 
-### ### 2. Field data convertion and prepocessing
+###  2. Field data convertion and prepocessing
  
 Step4: Run 4_gssi_data_convert.py   Convert the raw data collected by GSSI ground penetrating radar into CSV file format. If the user is using a different model of ground penetrating radar, please convert the raw data accordingly. Please refer to the corresponding documents for specific requirements and details.
 
@@ -129,7 +129,7 @@ Step6: Run 6_extract_impulse.py    Extract the true source wavelet from the proc
 
 Step7: Run 7_network_train.py    Training a deep learning network for inversion. Set the parameters for learning rate, batch_size, etc.  Please refer to the corresponding documents for specific requirements and details.
 
-### ### 4. Prediction and time-depth convertion 
+###  4. Prediction and time-depth convertion 
 
 Step8: Prediction.py    Predicting real measured data. Please refer to the corresponding documents for specific requirements and details.