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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 8041

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Prof. Dr. Fridon Shubitidze

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Thayer School of Engineering, Dartmouth College, 14 Engineering Dr, Hanover, NH 03755, USA
Interests: remote sensing; magentic and electromagentic sensors; forward and inverse EM problems and methods; subsurface targets detection and classification; FPGA systems; nano-particle hyperthermia; numerical models; magnetic; electromagnetic; acoustic and optical sensors and unmanned systems for subsurface targets detection and classification
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Special Issue Information

Dear Colleagues,

Recent advances in development of advanced magnetic, electromagnetic, acoustic, and optical sensing technologies have provided high-fidelity, unprecedented data sets for detecting, mapping, and identifying near-surface man-made and natural geophysical anomalies. These sensing technologies are mountable on unmanned systems and provide subsurface hazardous targets detection, classification and remediation safely and cost-effectively.

This Special Issue is open for all contributors in the field of recent developments in the near-surface sensing technologies (hardware) and multi-data processing approaches for mapping electromagnetic properties of near-surface such pavements, permafrost, and etc.; detecting and identifications of man-made and natural geophysical anomalies of interests on land and in underwater environments. We invite submissions of novel and original papers, case studies and reviews to this Special Issue that extend and advance our scientific/technical understanding of current state of the art near-surface sensing multi-data in areas that include, but are not limited to:

High fidelity magnetic, electromagnetic, acoustic, seismic and optical sensors data;
Near-surface multi-data set provided by unmanned (ground robots, aerial system and underwater autonomous) systems for near-surface anomlay detection, mapping, and identification;
The joint inversion methods and approaches for mapping grounds electromagnetic properties.
Unified forward and inverse modelling approaches for processing the multi-data sets;
Classification techniques, such Linear classifiers, support vector machines, quadratic classifiers; neural networks applied to multi-data sets;
Recent developments and studies of multi-data sets inversion and processing for near-surface geophysical anomaly detection and identification;
Case studies during mapping soils electric and magnetic properties for agriculture applications.

Prof. Dr. Fridon Shubitidze
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Prof. Dr. Fridon Shubitidze
Guest Editor

Manuscript Submission Information

Published Papers (6 papers)

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19 pages, 10973 KiB

Open AccessArticle

Efficient 3D Frequency Semi-Airborne Electromagnetic Modeling Based on Domain Decomposition

by Zhejian Hui, Xuben Wang, Changchun Yin and Yunhe Liu

Remote Sens. 2023, 15(24), 5636; https://doi.org/10.3390/rs15245636 - 05 Dec 2023

Cited by 1 | Viewed by 842

Abstract

Landslides are common geological hazards that often result in significant casualties and economic losses. Due to their occurrence in complex terrain areas, conventional geophysical techniques face challenges in early detection and warning of landslides. Semi-airborne electromagnetic (SAEM) technology, utilizing unmanned aerial platforms for rapid unmanned remote sensing, can overcome the influence of complex terrain and serve as an effective approach for landslide detection and monitoring. In response to the low computational efficiency of conventional semi-airborne EM 3D forward modeling, this study proposes an efficient forward modeling method. To handle arbitrarily complex topography and geologic structures, the unstructured tetrahedron mesh is adopted to discretize the earth. Based on the vector finite element formula, the Dual–Primal Finite Element Tearing and Interconnecting (FETI-DP) method is further applied to enhance modeling efficiency. It obtains a reduced order subsystem and avoids directly solving huge overall linear equations by converting the entirety problem into the interface problem. We check our algorithm by comparing it with 1D semi-analytical solutions and the conventional finite element method. The numerical experiments reveal that the FETI-DP method utilities less memory and exhibits higher computation efficiency than the conventional methods. Additionally, we compare the electromagnetic responses with the topography model and flat earth model. The comparison results indicate that the effect of topography cannot be ignored. Further, we analyze the characteristic of electromagnetic responses when the thickness of the aquifer changes in a landslide area. We demonstrate the effectiveness of the 3D SAEM method for landslide detection and monitoring. Full article

(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics)

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25 pages, 11710 KiB

Open AccessArticle

Spreading of Localized Information across an Entire 3D Electrical Resistivity Volume via Constrained EMI Inversion Based on a Realistic Prior Distribution

by Nicola Zaru, Matteo Rossi, Giuseppina Vacca and Giulio Vignoli

Remote Sens. 2023, 15(16), 3993; https://doi.org/10.3390/rs15163993 - 11 Aug 2023

Cited by 3 | Viewed by 974

Abstract

Frequency-domain electromagnetic induction (EMI) methods are commonly used to map vast areas quickly and with minimum logistical efforts. Unfortunately, they are often characterized by a very limited number of frequencies and severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) approaches are usually considered more reliable; for example, they do not require specific calibration procedures and can be easily inverted in 2D/3D. However, ERT surveys are, by far, more demanding and time consuming, allowing for the deployment of a few acquisition lines per day. Ideally, the optimal would be to have the advantages of both approaches: ease of acquisition while keeping robustness and reliability. The present work raises from the necessity to cope with this issue and from the importance of enforcing realistic constraints to the data inversion without being limited to (over)simplistic spatial constraints (for example, characterizing the smooth and/or sharp regularization). Accordingly, the present research demonstrates, by means of synthetic and field data, how the EMI inversion—based on realistic prior models—can be further enhanced by incorporating additional pre-existing pieces of information. While the proposed scheme is quite general, in the specific examples discussed here, these additional pieces of information are, respectively, a reference model along a line across the survey area, and an ERT section. The field EMI results were verified against extensive ground penetrating radar (GPR) measurements and boreholes. Full article

(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics)

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20 pages, 8405 KiB

Open AccessArticle

Modeling and Implementation of a Joint Airborne Ground Penetrating Radar and Magnetometer System for Landmine Detection

by Junghan Lee, Haengseon Lee, Sunghyub Ko, Daehyeong Ji and Jongwu Hyeon

Remote Sens. 2023, 15(15), 3813; https://doi.org/10.3390/rs15153813 - 31 Jul 2023

Cited by 1 | Viewed by 1917

Abstract

We modeled and implemented a joint airborne system integrating ground penetrating radar (GPR) and magnetometer (MAG) models specifically for landmine detection applications. We conducted both simulations and experimental analyses of the joint airborne GPR and MAG models, with a focus on detecting the metallic components of different types of landmines, including antitank (AT) M15 metallic, antipersonnel (AP) M16 metallic, and AT M19 plastic (minimum-metal) landmines. The GPR model employed the finite-difference time-domain (FDTD) method and was evaluated using a singular value decomposition (SVD) and Kirchhoff migration (KM) with matched filtering (MF). These advanced techniques enabled the automatic identification and precise focusing of the reflected hyperbolic signals emitted by the landmines while considering cross-range resolution. Additionally, the MAGs were utilized based on the magnetic dipole model with a de-trend and a spatial median filtering method to estimate the magnetic anomaly of the landmines while considering various data spatial intervals. The joint airborne GPR and MAG system was implemented by combining and integrating the GPR and MAG models for experimental validation. Through this comprehensive approach, which included experiments, simulations, and data processing, the design parameters of the final system were obtained. These design parameters can be used in the development and application of landmine detection systems based on airborne GPR and MAG technology. Full article

(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics)

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17 pages, 8449 KiB

Open AccessArticle

Near-Surface Structure Investigation Using Ambient Noise in the Water Environment Recorded by Fiber-Optic Distributed Acoustic Sensing

by Jie Shao, Yibo Wang, Chi Zhang, Xuping Zhang and Yixin Zhang

Remote Sens. 2023, 15(13), 3329; https://doi.org/10.3390/rs15133329 - 29 Jun 2023

Viewed by 1118

Abstract

Near-surface structure investigation plays an important role in studying shallow active faults and has various engineering applications. Therefore, we developed a near-surface structure investigation method using ambient noise in a water environment. This newly developed seismic acquisition technology, fiber-optic distributed acoustic sensing (DAS), was used to acquire ambient noise from the Yangtze River. The recorded data were processed to reconstruct surface waves based on the theory of seismic interferometry. The fundamental-mode dispersion curves were extracted and inverted to obtain a shear-wave velocity model below the DAS line. We compared the inverted velocity model with the subsurface geological information from near the study area. The results from the inverted model were consistent with the prior geological information. Therefore, ambient noise in the water environment can be combined with DAS technology to effectively investigate near-surface structures. Full article

(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics)

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19 pages, 5467 KiB

Open AccessArticle

Improved Dempster–Shafer Evidence Theory for Tunnel Water Inrush Risk Analysis Based on Fuzzy Identification Factors of Multi-Source Geophysical Data

by Yulin Ding, Binru Yang, Guangchun Xu and Xiaoyong Wang

Remote Sens. 2022, 14(23), 6178; https://doi.org/10.3390/rs14236178 - 06 Dec 2022

Cited by 4 | Viewed by 1493

Abstract

Water inrush is one of the most important risk factors in tunnel construction because of its abruptness and timeliness. Various geophysical data used in actual construction contain useful information related to groundwater development. However, the existing approaches with such data from multiple sources and sensors are generally independent and cannot integrate this information, leading to inaccurate projections. In addition, existing tunnel advanced geological forecast reports for risk projections interpreted by human operators generally contain no quantitative observations or measurements, but only consist of ambiguous and uncertain qualitative descriptions. To surmount the problems above, this paper proposes a tunnel water inrush risk analysis method by fusing multi-source geophysical observations with fuzzy identification factors. Specifically, the membership function of the fuzzy set is used to solve the difficulty in determining the basic probability assignment function in the improved Dempster–Shafer evidence theory. The prediction model of effluent conditions fuses seismic wave reflection data, ground penetrating radar data, and transient electromagnetic data. Therefore, quantitative evaluations of the effluent conditions are achieved, including the strand water, linear water, seepage and dripping water, and anhydrous. Experimental evaluations with a typical tunnel section were conducted, in which the state of the groundwater from a series of geological sketch reports in this sectionpaper were used as ground truth for verification. The experimental results revealed that the proposed method not only has high accuracy and robustness but also aligns well with different evidence effectively that generally contradicts manual interpretation reports. The results from 12 randomly selected tunnel sections also demonstrate the generalization abilities of the proposed method. Full article

(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics)

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15 pages, 5756 KiB

Open AccessTechnical Note

A Deep Learning Gravity Inversion Method Based on a Self-Constrained Network and Its Application

by Shuai Zhou, Yue Wei, Pengyu Lu, Guangrui Yu, Shuqi Wang, Jian Jiao, Ping Yu and Jianwei Zhao

Remote Sens. 2024, 16(6), 995; https://doi.org/10.3390/rs16060995 - 12 Mar 2024

Viewed by 637

Abstract

Gravity inversion can be used to obtain the spatial structure and physical properties of subsurface anomalies through gravity observation data. With the continuous development of machine learning, geophysical inversion methods based on deep learning have achieved good results. Geophysical inversion methods based on deep learning often employ large-scale data sets to obtain inversion networks with strong generalization. They are widely used but face a problem of lacking information constraints. Therefore, a self-constrained network is proposed to optimize the inversion results, composed of two networks with similar structures but different functions. At the same time, a fine-tuning strategy is also introduced. On the basis of data-driven deep learning, we further optimized the results by controlling the self-constrained network and optimizing fine-tuning strategy. The results of model testing show that the method proposed in this study can effectively improve inversion precision and obtain more reliable and accurate inversion results. Finally, the method is applied to the field data of Gonghe Basin, Qinghai Province, and the 3D inversion results are used to effectively delineate the geothermal storage area. Full article

(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics)

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