2021
DOI: 10.1002/nme.6593
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Error control and loss functions for the deep learning inversion of borehole resistivity measurements

Abstract: Deep learning (DL) is a numerical method that approximates functions. Recently, its use has become attractive for the simulation and inversion of multiple problems in computational mechanics, including the inversion of borehole logging measurements for oil and gas applications. In this context, DL methods exhibit two key attractive features: (a) once trained, they enable to solve an inverse problem in a fraction of a second, which is convenient for borehole geosteering operations as well as in other real-time … Show more

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Cited by 33 publications
(36 citation statements)
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“…We investigate the electromagnetic wave propagation eigenproblem. In particular, we consider a three-layer heterogeneous Earth model as a common case study in geosteering applications (see, e.g., [7,68]). This 2D Earth model is essential in studying the full 3D wave propagation eigenproblem in transversely isotropic media [69].…”
Section: Case Studies 621 Electromagnetic Wave Propagation In a Three...mentioning
confidence: 99%
“…We investigate the electromagnetic wave propagation eigenproblem. In particular, we consider a three-layer heterogeneous Earth model as a common case study in geosteering applications (see, e.g., [7,68]). This 2D Earth model is essential in studying the full 3D wave propagation eigenproblem in transversely isotropic media [69].…”
Section: Case Studies 621 Electromagnetic Wave Propagation In a Three...mentioning
confidence: 99%
“…These traditional methods evaluate the inverse solution pointwise (i.e., for a given set of measurements), but they rarely provide a global representation of the inverse operator. To overcome this problem and approximate the full inverse function, it is possible to use Deep Learning (DL) methods (see, e.g., [9,10,11,12,13,14]), which allow to approximate complex mappings via a composition of linear and non-linear functions.…”
Section: Introductionmentioning
confidence: 99%
“…Such processes require the solution of possibly thousands of forward problems under strict time constraints (Pardo et al, 2015;Shahriari et al, 2018). However, deterministic inversion suffers from nonuniqueness of the solutions (Shahriari et al, 2020c).…”
Section: Introductionmentioning
confidence: 99%