Abstract
General full-wave electromagnetic solvers, such as those utilizing the finite-difference time-domain (FDTD) method, are computationally demanding for simulating practical GPR problems. We explore the performance of a near-real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture. To ease the process, we have developed a framework that is capable of generating these ML-based forward solvers automatically. The framework uses an innovative training method that combines a predictive dimensionality reduction technique and a large data set of modeled GPR responses from our FDTD simulation software, gprMax. The forward solver is parameterized for a specific GPR application, but the framework can be extended in a straightforward manner to different electromagnetic problems.
Original language | English |
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Title of host publication | 2021 11th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2021 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Number of pages | 6 |
ISBN (Electronic) | 9781665422536 |
DOIs | |
Publication status | Published - 2 Aug 2022 |
Event | 11th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2021 - Valletta, Malta Duration: 1 Dec 2021 → 4 Dec 2021 |
Publication series
Name | International Workshop on Advanced Ground Penetrating Radar (IWAGPR) |
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Publisher | IEEE |
ISSN (Print) | 2687-7899 |
Conference
Conference | 11th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2021 |
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Country/Territory | Malta |
City | Valletta |
Period | 1/12/21 → 4/12/21 |
Bibliographical note
Funding Information:The project was funded via the Google Summer of Code (GSoC) 2021 programme. GSoC initiative is a global program focused on bringing student developers into open source software development. The source code for this project can be found at https://github.com/gprMax/gprMax/pull/294.
Keywords
- Full-Waveform Inversion (FWI)
- Machine Learning (ML)
- Principle Component Analysis (PCA)
- Random Forest
- Singular Value Decomposition (SVD)
- XGBoost (Extreme Gradient Boosting)