GPR-FWI-Py: Open-source Python']Python software for multi-scale regularized full waveform inversion in Ground Penetrating Radar using random excitation sources
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作者:
Wang, Xiangyu
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机构:
Guangzhou Univ, Sch Civil Engn & Transportat, Guangzhou 510006, Peoples R ChinaGuangzhou Univ, Sch Civil Engn & Transportat, Guangzhou 510006, Peoples R China
Wang, Xiangyu
[1
]
Liu, Hai
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机构:
Guangzhou Univ, Sch Civil Engn & Transportat, Guangzhou 510006, Peoples R China
Guangzhou Univ, Guangdong Engn Res Ctr Underground Infrastructural, Guangzhou 510006, Peoples R ChinaGuangzhou Univ, Sch Civil Engn & Transportat, Guangzhou 510006, Peoples R China
Liu, Hai
[1
,2
]
Meng, Xu
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机构:
Guangzhou Univ, Sch Civil Engn & Transportat, Guangzhou 510006, Peoples R ChinaGuangzhou Univ, Sch Civil Engn & Transportat, Guangzhou 510006, Peoples R China
Meng, Xu
[1
]
Hu, Hesong
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机构:
Guangzhou Inst Bldg Sci Grp Co Ltd, Guangzhou 510006, Peoples R ChinaGuangzhou Univ, Sch Civil Engn & Transportat, Guangzhou 510006, Peoples R China
Hu, Hesong
[3
]
机构:
[1] Guangzhou Univ, Sch Civil Engn & Transportat, Guangzhou 510006, Peoples R China
[2] Guangzhou Univ, Guangdong Engn Res Ctr Underground Infrastructural, Guangzhou 510006, Peoples R China
[3] Guangzhou Inst Bldg Sci Grp Co Ltd, Guangzhou 510006, Peoples R China
Full Waveform Inversion (FWI) of Ground Penetrating Radar (GPR) is crucial for enhancing subsurface imaging, yet its applications often confronts computational and usability challenges. This paper introduces GPR-FWI-Py, a comprehensive 2D GPR FWI code package that addresses these challenges through a multi-scale strategy, a random excitation source strategy, and Total Variation (TV) regularization. Optimized for high-performance computing, the software is developed in pure Python, ensuring both high efficiency and accessibility. Key features include user-friendly design and readability, which empower users to easily adapt and maintain the software to meet specific project needs. Performance evaluations on layered and Over-Thrust models confirm that our strategies significantly improve FWI results. The modular architecture of GPR-FWI-Py not only simplifies the integration of the FWI algorithm into GPR imaging but also enhances adaptability by supporting the introduction of additional functionalities.
机构:
Jilin Univ, Coll Geoexplorat Sci & Technol, Changchun, Jilin, Peoples R ChinaJilin Univ, Coll Geoexplorat Sci & Technol, Changchun, Jilin, Peoples R China
Liu, Tieyu
Li, Jing
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机构:
Jilin Univ, Coll Geoexplorat Sci & Technol, Changchun, Jilin, Peoples R ChinaJilin Univ, Coll Geoexplorat Sci & Technol, Changchun, Jilin, Peoples R China
Li, Jing
Cheng, Dandan
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机构:
China Res Inst Radio Wave Propagat, Qingdao, Shandong, Peoples R ChinaJilin Univ, Coll Geoexplorat Sci & Technol, Changchun, Jilin, Peoples R China
Cheng, Dandan
Wang, Chenghao
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机构:
China Res Inst Radio Wave Propagat, Qingdao, Shandong, Peoples R ChinaJilin Univ, Coll Geoexplorat Sci & Technol, Changchun, Jilin, Peoples R China
Wang, Chenghao
JOURNAL OF INVERSE AND ILL-POSED PROBLEMS,
2024,
32
(04):
: 587
-
601
机构:
Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Hunan, Peoples R China
Minist Educ, Key Lab Metallogen Predict Nonferrous Met & Geol, Changsha 410083, Hunan, Peoples R ChinaCent South Univ, Sch Geosci & Info Phys, Changsha 410083, Hunan, Peoples R China
Feng, Deshan
Wang, Xun
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机构:
Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Hunan, Peoples R China
Minist Educ, Key Lab Metallogen Predict Nonferrous Met & Geol, Changsha 410083, Hunan, Peoples R ChinaCent South Univ, Sch Geosci & Info Phys, Changsha 410083, Hunan, Peoples R China
Wang, Xun
Zhang, Bin
论文数: 0引用数: 0
h-index: 0
机构:
Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Hunan, Peoples R China
Minist Educ, Key Lab Metallogen Predict Nonferrous Met & Geol, Changsha 410083, Hunan, Peoples R ChinaCent South Univ, Sch Geosci & Info Phys, Changsha 410083, Hunan, Peoples R China