Geophysical electromagnetic modeling and evaluation: A review

Wang, Bochen; Liu, Jianxin; Hu, Xiangping; Liu, Jiawei; Guo, Zhenwei; Xiao, Jianping

doi:10.1016/j.jappgeo.2021.104438

Cited by 6 publications

(3 citation statements)

References 174 publications

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“…MT forward modeling has become highly mature after several decades. Numerical simulation methods such as finite difference, finite element, integral equation, and even deep learning can solve the forward calculation of various complex models (Wang et al, 2021). Kelbert et al (2014) developed ModEM, an open-source 3D magnetotelluric and controlled-source electromagnetic forward and inversion system, which was used for forward modeling of the 3D model in this method.…”

Section: Mt Forward Modelingmentioning

confidence: 99%

Synthesizing magnetotelluric time series based on forward modeling

Wang

Chen²,

Zhang³

2023

Front. Earth Sci.

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The validity of magnetotelluric time-series processing methods has been lacking reasonable testing criteria. Since the time series synthesized by existing techniques are not fully derived from a given model, they are not reliable. In this paper, we present a novel approach to synthesize magnetotelluric time series based on forward modeling and the correspondence between frequency and time domain electromagnetic fields. In this approach, we obtain the electromagnetic response of two orthogonal polarization sources for a given model by magnetotelluric forward modeling, and simulate the randomness of the polarization of the natural field source by a linear combination of the two polarization sources. Based on the correspondence between the frequency and time domain electromagnetic fields, the electromagnetic fields obtained by forward modeling in the frequency domain are transformed into the time domain, and finally the time series are synthesized. The test results on 1D and 3D models validate the effectiveness of the proposed method and the correctness of the procedure. After adding noise to the synthesized time series, we can test the performance of each method by comparing the results of the time series processing methods with the response of the given model. Therefore, the method presented in this paper can be used to construct standard magnetotelluric time series, which can be used as a carrier to construct synthetic data satisfying various noise distributions, and for the study of related methods. This method can also be used to synthesize time series of other frequency-domain electromagnetic methods.

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Section: Mt Forward Modelingmentioning

confidence: 99%

Synthesizing magnetotelluric time series based on forward modeling

Wang

Chen²,

Zhang³

2023

Front. Earth Sci.

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“…Several works and experiments demonstrated the potential of EM imaging for detecting hydrocarbon-rich zones (Eidesmo et al, 2002;Edwards, 2005;Sheard et al, 2005;Srnka et al, 2006;Newman and Commer, 2009;Gray et al, 2012;Chung et al, 2014;Cai et al, 2017;Castillo-Reyes et al, 2018). Furthermore, the literature reviews by Andreis and MacGregor (2008), Constable (2010), Newman (2014), and Wang et al (2021) detailed the journey of EM methods and its applicability in the hydrocarbon industry. However, the same technological basis can be deployed to detect fluids or minerals or monitor their migration.…”

Section: Electromagnetic Data For Enhanced Imaging Of the Earthmentioning

confidence: 99%

“…Electromagnetic (EM) methods are among the most important tools for exploring the subsurface (Andreis and MacGregor, 2008). EM imaging technologies have been used for hydrocarbon exploration for several decades, but lately, worldwide recognition of the importance of the method has significantly increased (Wang et al, 2021). Its widespread applications range from regional-scale geoenergy studies (Kana et al, 2015;Castillo-Reyes et al, 2021) and mineral exploration to mapping thin shallow layers (Guo Z. et al, 2020) or characterization of unexploited groundwater reservoirs (Weymer et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Castillo-Reyes,

Hu,

Wang

et al. 2023

Front. Earth Sci.

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Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources.

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