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Sensors and Geophysical Electromagnetics

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 7783

Special Issue Editors


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Guest Editor
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130000, China
Interests: modelling; optimization; electromagnetics
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130000, China
Interests: numerical modeling; 3D inversion; airborne electromagnetic
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130000, China
Interests: electromagnetic exploration; data processing; modeling and inversion

Special Issue Information

Dear Colleagues,

Recently, we have seen growing interest in geophysical electromagnetic exploration equipment and technologies, which offer new opportunities for geophysical exploration through advanced numerical simulation and inversion optimization methods, as well as sensing equipment manufacturing technologies. These technologies will support the development of airborne electromagnetic, satellite electromagnetic, marine electromagnetic, magnetotelluric and borehole electromagnetic technologies, and so on, and lay a solid foundation for multi-space exploration of Earth. Therefore, this Special Issue aims to collect original research and review articles on the latest progress, technology, solutions, applications, and new challenges in the electromagnetic field of geophysical exploration.

Prof. Dr. Yunhe Liu
Dr. Bo Zhang
Dr. Xiuyan Ren
Guest Editors

Manuscript Submission Information

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Keywords

  • sensors
  • electromagnetic method
  • geo-exploration equipment
  • optimization inversion method

Published Papers (6 papers)

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Research

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16 pages, 6793 KiB  
Article
Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System
by Jinbo Huang, Zhen Zheng, Yu Zhou, Yuran Tan, Chengjun Wang, Guangbo Xu and Bingting Zha
Sensors 2023, 23(22), 9293; https://doi.org/10.3390/s23229293 - 20 Nov 2023
Cited by 1 | Viewed by 807
Abstract
In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads [...] Read more.
In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads to difficulty in potential function regulation. In this paper, a new compound tri-stable stochastic resonance (CTSR) model is proposed to address this problem by combining a Gaussian Potential model and the mixed bi-stable model. The weak magnetic anomaly signal detection system consists of the CTSR system and judgment system based on statistical analysis. The system parameters are adjusted by using a quantum genetic algorithm (QGA) to optimize the output signal-to-noise ratio (SNR). The experimental results show that the CTSR system performs better than the traditional tri-stable stochastic resonance (TTSR) system and BSR system. When the input SNR is -8 dB, the detection probability of the CTSR system approaches 80%. Moreover, this detection system not only detects the magnetic anomaly signal but also retains information on the relative motion (heading) of the ferromagnetic target and the magnetic detection device. Full article
(This article belongs to the Special Issue Sensors and Geophysical Electromagnetics)
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13 pages, 5098 KiB  
Communication
Identification of Underground Artificial Cavities Based on the Bayesian Convolutional Neural Network
by Jigen Xia, Ronghua Peng, Zhiqiang Li, Junyi Li, Yizhuo He and Gang Li
Sensors 2023, 23(19), 8169; https://doi.org/10.3390/s23198169 - 29 Sep 2023
Viewed by 689
Abstract
The development of underground artificial cavities plays an important role in the exploitation of urban spatial resources. As the rapidly growing number of underground artificial cavities with different depths and scales increases, the detection and identification of underground artificial cavities has become a [...] Read more.
The development of underground artificial cavities plays an important role in the exploitation of urban spatial resources. As the rapidly growing number of underground artificial cavities with different depths and scales increases, the detection and identification of underground artificial cavities has become a key issue in underground engineering studies. Geophysical techniques have been widely used for the construction, management, and maintenance of underground artificial cavities. In this study, we present two identification methods for underground artificial cavities. Apparent resistivity imaging is the most popular technique for quickly identifying underground artificial cavities, using the forward simulation results of a three-dimensional earth model and comparing these with the preset positions of artificial cavities, as demonstrated in the experiment. To further improve the efficiency of underground artificial cavity identification, we developed a fast recognition approach for underground artificial cavities based on the Bayesian convolutional neural network (BCNN). Compared to a traditional convolutional neural network, the performance of the BCNN method was greatly improved in terms of the classification accuracy and efficiency of identifying underground artificial cavities with apparent resistivity image datasets. Full article
(This article belongs to the Special Issue Sensors and Geophysical Electromagnetics)
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25 pages, 9863 KiB  
Article
Time-Lapse 3D CSEM for Reservoir Monitoring Based on Rock Physics Simulation of the Wisting Oil Field Offshore Norway
by Mohammed Ettayebi, Shunguo Wang and Martin Landrø
Sensors 2023, 23(16), 7197; https://doi.org/10.3390/s23167197 - 16 Aug 2023
Viewed by 1123
Abstract
The marine controlled-source electromagnetic (CSEM) method has been used in different applications, such as oil and gas reservoir exploration, groundwater investigation, seawater intrusion studies and deep-sea mineral exploration. Recently, the utilization of the marine CSEM method has shifted from petroleum exploration to active [...] Read more.
The marine controlled-source electromagnetic (CSEM) method has been used in different applications, such as oil and gas reservoir exploration, groundwater investigation, seawater intrusion studies and deep-sea mineral exploration. Recently, the utilization of the marine CSEM method has shifted from petroleum exploration to active monitoring due to increased environmental concerns related to hydrocarbon production. In this study, we utilize the various dynamic reservoir properties available through reservoir simulation of the Wisting field in the Norwegian part of the Barents Sea. In detail, we first developed geologically consistent rock physics models corresponding to reservoirs at different production phases, and then transformed them into resistivity models. The constructed resistivity models pertaining to different production phases can be used as input models for a finite difference time domain (FDTD) forward modeling workflow to simulate EM responses. This synthetic CSEM data can be studied and analyzed in the light of production-induced changes in the reservoir at different production phases. Our results demonstrate the ability of CSEM data to detect and capture production-induced changes in the fluid content of a producing hydrocarbon reservoir. The anomalous CSEM responses correlating to the reservoir resistivity change increase with the advance of the production phase, and a similar result is shown in anomalous transverse resistance (ATR) maps derived from the constructed resistivity models. Moreover, the responses at 30 Hz with a 3000 m offset resulted in the most pronounced anomalies at the Wisting reservoir. Hence, the method can effectively be used for production-monitoring purposes. Full article
(This article belongs to the Special Issue Sensors and Geophysical Electromagnetics)
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19 pages, 9154 KiB  
Article
Three-Dimensional Separate and Joint Inversions of Multi-Component Frequency-Domain Airborne Electromagnetic Data: Synthetic Model Studies
by Jun Yang, Xin Huang, Liangjun Yan and Xiaoyue Cao
Sensors 2023, 23(15), 6842; https://doi.org/10.3390/s23156842 - 01 Aug 2023
Viewed by 732
Abstract
Airborne electromagnetic (AEM) surveys using airborne mobile platforms enable rapid and efficient exploration of areas where groundwork is difficult. They have been widely used in fields such as shallow resource exploration and environmental engineering. Three-dimensional AEM inversion is the main technique used in [...] Read more.
Airborne electromagnetic (AEM) surveys using airborne mobile platforms enable rapid and efficient exploration of areas where groundwork is difficult. They have been widely used in fields such as shallow resource exploration and environmental engineering. Three-dimensional AEM inversion is the main technique used in fine structural interpretation. However, most current methods focus on separate component data inversions, which limit the kinds of structures that can be recovered in the inversion results. To address this issue, a method for the robust 3D joint inversion of multicomponent frequency-domain AEM data was developed in this study. First, a finite element method based on unstructured tetrahedral grids was used to solve the forward problem of frequency-domain AEM data for both isotropic and anisotropic media. During inversion, a limited-memory quasi-Newton (L-BFGS) method was used to reduce the memory requirements and enable the joint inversion of large-scale multicomponent AEM data. The effectiveness of our algorithm was demonstrated using synthetic models for both isotropic and anisotropic cases, with 5% Gaussian noise added to the modeling data to simulate the measured data for separate and joint inversions. The results of the synthetic models show that joint inversion has advantages over separate inversion in that it enables the recovery of finer underground structures and provides a novel approach for the fine interpretation of frequency-domain AEM data. Full article
(This article belongs to the Special Issue Sensors and Geophysical Electromagnetics)
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20 pages, 7001 KiB  
Article
Application of the Electromagnetic Method to the Spatial Distribution of Subsurface Saline and Fresh Water in the Coastal Mudflat Area of Jiangsu Province
by Wei Zhu, Wenguo Wang, Dayong Wang, Gang Wang, Aiming Cui, Yongzai Xi, Fei Li, Baowei Zhang and Gege Zhang
Sensors 2023, 23(14), 6405; https://doi.org/10.3390/s23146405 - 14 Jul 2023
Viewed by 781
Abstract
Interfacial zones straddling terrestrial and marine realms, colloquially known as mudflats, epitomize a dynamic nexus between these environments and are fundamental to the coastal ecosystem. The investigation of these regions is paramount for facilitating infrastructural developments including ports, wharfs, cross-sea bridges, and the [...] Read more.
Interfacial zones straddling terrestrial and marine realms, colloquially known as mudflats, epitomize a dynamic nexus between these environments and are fundamental to the coastal ecosystem. The investigation of these regions is paramount for facilitating infrastructural developments including ports, wharfs, cross-sea bridges, and the strategic utilization of freshwater resources sequestered from mainland islands amid ongoing economic progress. Terrestrial realms conventionally employ electromagnetic techniques as efficacious modalities to delineate subterranean geological information, encompassing structural details and water-bearing strata. However, the peculiar topographic and geological nuances of mudflat regions pose substantial challenges for the efficacious application of electromagnetic methodologies. The present paper endeavors to address these challenges by suggesting innovative modifications to the existing instrumentation and evolving novel data acquisition techniques specifically tailored for electromagnetic exploration within mudflat environments. This paper delves into the electrical characteristics of water-bearing layers within mudflats, and ascertains details pertaining to the subterranean structure and the spatial distribution of fresh and saline water resources, through the holistic interpretation of a multitude of profiles. Full article
(This article belongs to the Special Issue Sensors and Geophysical Electromagnetics)
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Review

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25 pages, 5770 KiB  
Review
A Review of Subsurface Electrical Conductivity Anomalies in Magnetotelluric Imaging
by Wule Lin, Bo Yang, Bo Han and Xiangyun Hu
Sensors 2023, 23(4), 1803; https://doi.org/10.3390/s23041803 - 06 Feb 2023
Cited by 3 | Viewed by 2605
Abstract
After 70 years of development, magnetotelluric (MT), a remote sensing technique for subsurface electrical resistivity imaging, has been widely applied in resource exploration and the deep tectonic evolution of the Earth. The electrical resistivity anomalies and their quantitative interpretation are closely related to [...] Read more.
After 70 years of development, magnetotelluric (MT), a remote sensing technique for subsurface electrical resistivity imaging, has been widely applied in resource exploration and the deep tectonic evolution of the Earth. The electrical resistivity anomalies and their quantitative interpretation are closely related to or even controlled by the interconnected high-conductivity phases, which are frequently associated with tectonic activity. Based on representative electrical resistivity studies mainly of the deep crust and mantle, we reviewed principal electrical conduction mechanisms, generally used conductivity mixing models, and potential causes of high-conductivity including the saline fluid, partial melting, graphite, sulfide, and hydrogen in nominally anhydrous minerals, and the general methods to infer the water content of the upper mantle through electrical anomaly revealed by MT. Full article
(This article belongs to the Special Issue Sensors and Geophysical Electromagnetics)
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