From e429611bd15fd981fdb33628607691a9ba21691a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E8=91=9B=E5=B3=BB=E6=81=BA?= <202115006@mail.sdu.edu.cn> Date: Tue, 8 Apr 2025 09:27:08 +0000 Subject: [PATCH] update README.md. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: 葛峻恺 <202115006@mail.sdu.edu.cn> --- README.md | 68 ++++++++++++++++++++++++++++++++++--------------------- 1 file changed, 42 insertions(+), 26 deletions(-) diff --git a/README.md b/README.md index a6e5e30..ed8145c 100644 --- a/README.md +++ b/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 ### πŸ“Œ Features: @@ -18,33 +21,37 @@ A PyTorch-based implementation of a source-independent full waveform inversion ( GPR-SIDL-inv/ -β”œβ”€β”€ dataset/ # Training/testing data and synthetic datasets +β”œβ”€β”€ dataset/ # Training/testing data and synthetic datasets β”œβ”€β”€ data.csv # Already generated dataset (70MHz Ricker wavelet) - β”œβ”€β”€ label.csv # Already generated dataset (70MHz Ricker wavelet) - -β”œβ”€β”€ field_data/ # Used to store field data for inversion - -β”œβ”€β”€ IMG/ # Used for storing data processing and inversion result graphs - -β”œβ”€β”€ impulse/ # Used to store simulated and measured source wavelet files - -β”œβ”€β”€ log/ # operation log - -β”œβ”€β”€ Network/ # Used for storing network models and data loading programs - -β”œβ”€β”€ readgssi/ # Software package for reading and converting raw data - -β”œβ”€β”€ SAVE/ # Save the trained model - -β”œβ”€β”€ time_result_csv/ # Inverse results in the time domain - -β”œβ”€β”€ utils/ # tool kit - - β”œβ”€β”€ Model.py/ # network model with transformer + β”œβ”€β”€ label.csv # Already generated dataset (70MHz Ricker wavelet) - β”œβ”€β”€ Mydataset.py/ # Loading and preprocessing datasets + (Note: The pre generated data.csv and label.csv datasets have been compressed into the dataset.rar file packageοΌ‰ + +β”œβ”€β”€ field_data/ # Used to store field data for inversion + +β”œβ”€β”€ IMG/ # Used for storing data processing and inversion result graphs + +β”œβ”€β”€ impulse/ # Used to store simulated and measured source wavelet files + +β”œβ”€β”€ Log/ # operation log + +β”œβ”€β”€ Network/ # Used for storing network models and data loading programs + +β”œβ”€β”€ readgssi/ # Software package for reading and converting raw data + +β”œβ”€β”€ SAVE/ # Save the trained model + +β”œβ”€β”€ time_result_csv/ # Inverse results in the time domain + +β”œβ”€β”€ utils/ # tool kit + + β”œβ”€β”€ Model.py/ # network model with transformer + + β”œβ”€β”€ Mydataset.py/ # Loading and preprocessing datasets + + β”œβ”€β”€ plot.py/ # the tool kit for plotting the 2D image β”œβ”€β”€ 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/ β”œβ”€β”€ 9_time_depth_convert.py # Convert the predicted results into the deep domain through integration -β”œβ”€β”€ config.yaml # Configuration file +β”œβ”€β”€ config.yaml # Configuration file, used to define all paths, variables, and parameters β”œβ”€β”€ requirements.txt # Python dependencies └── README.md # This file +### πŸ’‘ 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). + +### πŸ“š Software requirements + +No external software. Python dependencies are listed in the requirements.txt. + + ### πŸ› οΈ 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