2021
DOI: 10.1190/geo2020-0760.1
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Physics-driven deep-learning inversion with application to transient electromagnetics

Abstract: Machine learning, and specifically deep learning techniques applied to geophysical inverse problems, is an attractive subject, which has promising potential and, at the same time, presents some challenges in practical implementation. Some of the obstacles relate to scarce knowledge of the searched geological structures, a problem that can limit both the interpretability and the generalizability of the trained deep learning networks when applied to independent scenarios in real applications. Commonly used (phys… Show more

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Cited by 62 publications
(10 citation statements)
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“…2, in the generation of the DL-RMD. We also train an additional network using the random resistivity models, similarly to several DL studies (Colombo et al, 2021b;Moghadas, 2020;Moghadas et al, 2020;Noh et al, 2020;Puzyrev and Swidinsky, 2021;Qin et al, 2019;Wu et al, 2021b) as mentioned in Table 1. To have the same level of complexity, the number of layers, depth discretization, and the number of random resistivity models are kept the same as used to train the other two networks for a fair comparison, and the resistivity of each layer is chosen randomly from a log-uniform distribution to take into account the nonlinearity of the forward responses with the resistivity values.…”
Section: Surrogate Forward-modelling Resultsmentioning
confidence: 99%
“…2, in the generation of the DL-RMD. We also train an additional network using the random resistivity models, similarly to several DL studies (Colombo et al, 2021b;Moghadas, 2020;Moghadas et al, 2020;Noh et al, 2020;Puzyrev and Swidinsky, 2021;Qin et al, 2019;Wu et al, 2021b) as mentioned in Table 1. To have the same level of complexity, the number of layers, depth discretization, and the number of random resistivity models are kept the same as used to train the other two networks for a fair comparison, and the resistivity of each layer is chosen randomly from a log-uniform distribution to take into account the nonlinearity of the forward responses with the resistivity values.…”
Section: Surrogate Forward-modelling Resultsmentioning
confidence: 99%
“…Fuks and Tchelepi [21] embedded the initial and boundary conditions in a composite loss function and performed training on smaller datasets. Colombo et al [22] incorporated DL techniques into standard deterministic inversion schemes to obtain better, more stable and unique transient electromagnetic (TEM) inversion results. Inspired by the conventional inversion considering the governing wave equation, some other works incorporated the physical laws of the inverse problem into the DL inversion architecture.…”
Section: Introductionmentioning
confidence: 99%
“…Meanwhile, modern machine learning methods such as deep learning (DL) are now widely employed for solving various scientific and engineering problems [10]- [12]. In particular, DL methods have recently been applied to solve geophysical inverse problems [13], [14]. DL inversion offers the practical possibility of real-time imaging of spatially complex subsurface structures.…”
Section: Introductionmentioning
confidence: 99%