Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint

Guo, Rui; Yao, He Ming; Li, Maokun; Ng, Michael K.; Jiang, Lijun; Abubakar, Aria

doi:10.1109/tgrs.2020.3032743

Cited by 48 publications

(4 citation statements)

References 49 publications

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“…Choosing between these two approaches or other approaches using Monte Carlo inversions (Moorkamp et al., 2010) is dependent on the details of the particular data sets and the structure involved. Combining the two approaches is possible, and exhibits enhanced performance for structural similarity in the joint inversion (Colombo & Rovetta, 2018; Guo et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Shear Velocity Inversion Guided by Resistivity Structure From the PI‐LAB Experiment for Integrated Estimates of Partial Melt in the Mantle

et al. 2021

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Plate tectonic theory is predicated on the idea of a rigid lithosphere that overrides a weaker underlying asthenosphere (McKenzie & Parker, 1967), but the nature of the lithosphere-asthenosphere system remains the subject of vigorous debate. The oceanic lithosphere comprises the majority of the surface of the Earth and has the simplest evolution and history. It is classically thought to be thermally defined as a boundary layer in a simple thermal model (Parker & Oldenburg, 1973). In this model, increasing temperature with depth causes mantle rocks to weaken, creating the asthenosphere (e.g.,

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Section: Introductionmentioning

confidence: 99%

Shear Velocity Inversion Guided by Resistivity Structure From the PI‐LAB Experiment for Integrated Estimates of Partial Melt in the Mantle

et al. 2021

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“…Finally, DL techniques also provide new perspectives of integrating EM methods with other imaging modalities to achieve better resolution [68]- [70], but how to embed different physical principles in a unified neural network remains open.…”

Section: B Physicsmentioning

confidence: 99%

Physics Embedded Machine Learning for Electromagnetic Data Imaging

Guo¹,

Huang²,

Li³

et al. 2022

Preprint

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Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive.Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM imaging tasks. Consequently, generalizability becomes a major concern. On the other hand, physical principles underlie EM phenomena and provide baselines for current imaging techniques. To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work. This article surveys various schemes to incorporate physics in learning-based EM imaging. We first introduce background on EM imaging and basic formulations of the inverse problem. We then focus on three types of strategies combining physics and ML for linear and nonlinear imaging and discuss their advantages and limitations. Finally, we conclude with open challenges and possible ways forward in this fast-developing field. Our aim is to facilitate the study of intelligent EM imaging methods that will be efficient, interpretable and controllable.

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“…Unlike conventional geophysical inversions, deep learningbased inversion approaches rely on data-driven methodologies to map observed data to a 3D physical property model [13]. Due to its superior performance, deep learning-based inversions are widely used in geophysical data processing and inverse problems, including seismic [14][15][16], electromagnetic [17][18][19], gravity, and magnetic [13,[20][21][22][23][24]. Moreover, deep learning algorithms are also utilized for the joint interpretation of geophysical data through the integration of unsupervised cluster analysis and supervised classification, enhancing the coherence of the obtained solution [24].…”

Section: Introductionmentioning

confidence: 99%

Improved Gravity Inversion Method Based on Deep Learning with Physical Constraint and Its Application to the Airborne Gravity Data in East Antarctica

Wu,

Wei,

Dong

et al. 2023

Remote Sensing

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This paper aims to solve the limitations of traditional gravity physical property inversion methods such as insufficient depth resolution and difficulties in parameter selection, by proposing an improved 3D gravity inversion method based on deep learning. The deep learning network model is established using the fully convolutional U-net network. To enhance the generalization ability of the sample set, the large-scale training set and test set are generated by the random walk, based on the forward theory. Founded on the traditional loss function’s definition, this paper introduces an improvement incorporating a physical constraint to measure the degree of data fitting between the predicted and the real gravity data. This improvement significantly boosted the accuracy of the deep learning inversion method, as verified through both a single model and an intricate combination model. Finally, we applied this improved inversion method to the gravity data from the Gamburtsev Subglacial Mountains in the interior of East Antarctica, obtaining a comprehensive 3D crustal density structure. The results provide new evidence for the presence of a dense crustal root situated beneath the central Gamburtsev Province near the Gamburtsev Suture.

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Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint

Cited by 48 publications

References 49 publications

Shear Velocity Inversion Guided by Resistivity Structure From the PI‐LAB Experiment for Integrated Estimates of Partial Melt in the Mantle

Shear Velocity Inversion Guided by Resistivity Structure From the PI‐LAB Experiment for Integrated Estimates of Partial Melt in the Mantle

Physics Embedded Machine Learning for Electromagnetic Data Imaging

Improved Gravity Inversion Method Based on Deep Learning with Physical Constraint and Its Application to the Airborne Gravity Data in East Antarctica

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