3-D Gravity and Magnetic Joint Inversion Based on Deep Learning Combined With Measurement Data Constraint

Jiao, Jian; Dong, Siyuan; Zhou, Shuai; Zeng, Zhaofa; Lin, Tao

doi:10.1109/tgrs.2023.3339303

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…The second group is the joint inversion of different kinds of geophysical data corresponding to different physical property models. For this type of joint inversion, correlations between different petrophysical parameters or geometries are established to facilitate complementary interactions among diverse geophysical methods, such as cross-gradient joint inversion methods [21][22][23][24][25][26], correlationconstrained methods [27][28][29], Gramian-constrained joint inversion methods [30][31][32][33], and joint inversion based on deep learning [34][35][36]. In addition, several researchers have incorporated geologic information into the inversion of gravity and magnetic data to produce models with higher resolution.…”

Section: Of 17mentioning

confidence: 99%

Power-Type Structural Self-Constrained Inversion Methods of Gravity and Magnetic Data

Ming,

Ma,

Wang

et al. 2024

Remote Sensing

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The inversion of gravity and magnetic data can obtain the density and magnetic structure of underground space, which provide important information for resource exploration and geological structure division. The most commonly used inversion method is smooth inversion in which the objective function is built with L2-norm, which has good stability, but it produces non-focused results that make subsequent interpretation difficult. The power-type structural self-constrained inversion (PTSS) method with L2-norm is proposed to improve the resolution of smooth inversion. A self-constraint term based on the power gradient of the results is introduced, which takes advantage of the structural feature that the power gradient can better focus on the model boundary to improve the resolution. For the joint inversion of gravity and magnetic data, the power-type mutual-constrained term between different physical structures and the self-constrained term can be simultaneously used to obtain higher-resolution results. The modeling tests demonstrated that the PTSS method can produce converged high-resolution results with good noise immunity in both the respective inversions and the joint inversion. Then, the PTSS joint inversion was applied to the airborne gravity and magnetic data of the iron ore district in Shandong, revealing the shape and location of the mineralized rock mass, which are crucial information for subsequent detailed exploration.

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Section: Of 17mentioning

confidence: 99%

Power-Type Structural Self-Constrained Inversion Methods of Gravity and Magnetic Data

Ming,

Ma,

Wang

et al. 2024

Remote Sensing

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Deep Learning in Geophysics: Current Status, Challenges, and Future Directions

Yu,

Oh,

Kong

et al. 2024

J. Korean Soc. Miner. Energy Resour. Eng.

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With the rapid development of artificial intelligence technology, machine learning and deep learning techniques have been actively applied in the field of exploration geophysics. In this study, we extensively investigated the application of deep learning in geophysical techniques including seismic, gravity, magnetic, electromagnetics, ground penetrating radar, and joint inversion. Recent research has confirmed that deep learning techniques optimized for geophysical problems are being developed that transcend traditional theory-based approaches, suggesting that deep learning is becoming a new paradigm in the field of geophysics. Despite these technological advances, there are still challenges to be overcome, such as a lack of training data, model opacity, and heterogeneity between training data and field data, and this study suggests future research directions to address these aspects.

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3-D Gravity and Magnetic Joint Inversion Based on Deep Learning Combined With Measurement Data Constraint

Cited by 2 publications

References 52 publications

Power-Type Structural Self-Constrained Inversion Methods of Gravity and Magnetic Data

Power-Type Structural Self-Constrained Inversion Methods of Gravity and Magnetic Data

Deep Learning in Geophysics: Current Status, Challenges, and Future Directions

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