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Multi-Data Applied to Near-Surface Geophysics (Second Edition)
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Multi-Data Applied to Near-Surface Geophysics (Second Edition)

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3268

Special Issue Editor

Prof. Dr. Fridon Shubitidze

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the 2nd edition of the Special Issue “Multi-data Applied to Near-surface Geophysics”.

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

This Special Issue is open to all contributors in the field of recent developments in near-surface sensing technologies (hardware) and multi-data processing approaches for mapping the electromagnetic properties of near-surface pavements, permafrost, etc., and for detecting and identifying human-made and natural geophysical anomalies of interest 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, the following:

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

Keywords

  • multi-data near-surface
  • joint-inversion
  • remote sensing
  • UXO
  • magnetics
  • electromagnetics induction
  • acoustic
  • permafrost
  • classification
  • hazardous materials
  • unmanned aerial systems

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Related Special Issue

Published Papers (3 papers)

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Research

19 pages, 10147 KiB  
Article
Sparse Magnetization Vector Inversion Based on Modulus Constraints
by Yang Ou, Qingtian Lü, Jie Zhang, Yi Yang, Dingyu Jia, Yang Li, Jinghong Zhai and Zhengzhong Jiang
Remote Sens. 2025, 17(4), 597; https://doi.org/10.3390/rs17040597 - 10 Feb 2025
Viewed by 477
Abstract
Magnetization vector inversion (MVI) is an effective method for simultaneously determining the distribution of magnetization intensity and direction without knowing the direction of magnetization beforehand. Nevertheless, the presence of serious non-uniqueness in MVI imposes challenges in achieving accurate and reliable results. To improve [...] Read more.
Magnetization vector inversion (MVI) is an effective method for simultaneously determining the distribution of magnetization intensity and direction without knowing the direction of magnetization beforehand. Nevertheless, the presence of serious non-uniqueness in MVI imposes challenges in achieving accurate and reliable results. To improve the accuracy of MVI, we propose a method that incorporates a modulus constraint, informed by an analysis of the model constraints in two different frameworks. We employ a sparse operator on the magnetization magnitude and obtain an explicit expression for the magnetization components, establishing correlation constraints among them. Synthetic test results show that this method can achieve models with clear boundaries and consistent magnetization directions. Furthermore, the application of a sparse operator to the gradient’s modulus of the magnetization magnitude helps recover inclined structures. However, the dispersed magnetization directions suggest that we should also constrain the magnetization direction, simultaneously. The inversion of magnetic data measured over the Zaohuohexi iron-polymetallic deposit in Qinghai Province, northwest China, verified the proposed approach’s effectiveness. Full article
(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics (Second Edition))
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24 pages, 13235 KiB  
Article
Facies-Constrained Kriging Interpolation Method for Parameter Modeling
by Zhenbo Nie, Bo Feng, Huazhong Wang, Chengliang Wu, Rongwei Xu and Chao Ning
Remote Sens. 2025, 17(1), 102; https://doi.org/10.3390/rs17010102 - 30 Dec 2024
Viewed by 768
Abstract
In seismic exploration, establishing a reliable parameter model (such as velocity, density, impedance) is crucial for seismic migration imaging and reservoir characterization. The interpolation of well data to obtain a complete spatial model is an important aspect of parameter modeling. However, in practical [...] Read more.
In seismic exploration, establishing a reliable parameter model (such as velocity, density, impedance) is crucial for seismic migration imaging and reservoir characterization. The interpolation of well data to obtain a complete spatial model is an important aspect of parameter modeling. However, in practical applications, well data are often sparse and irregularly distributed, which complicates the accurate construction of subsurface parameter models. The Kriging method is an effective interpolation method based on discrete well data, but its theoretical assumptions do not meet the practical requirements in seismic exploration, resulting in low modeling accuracy. This article introduces seismic facies information into the Kriging method and proposes a novel parameter modeling method named the facies-constrained Kriging (FC-Kriging) method. The FC-Kriging method modifies the Euclidean distance metric used in Kriging so that the distance between two points depends not only on their spatial coordinates but also on their associated facies categories. The proposed method is a multi-information fusion method that integrates facies information based on well data, enabling good interpolation results even with a limited number of wells. The parameter modeling results based on the FC-Kriging method are more consistent with geological logic, exhibiting clearer boundary features and higher resolution. Furthermore, the FC-Kriging method does not introduce additional computational complexity, making it convenient to implement in a 3D situation. The FC-Kriging method is applied to the 2D Sigsbee model, the 3D Standford V reservoir model and F3 block field data. The results demonstrate its accuracy and effectiveness. Full article
(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics (Second Edition))
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27 pages, 31281 KiB  
Article
Tracking Moisture Dynamics in a Karst Rock Formation Combining Multi-Frequency 3D GPR Data: A Strategy for Protecting the Polychrome Hall Paintings in Altamira Cave
by Vicente Bayarri, Alfredo Prada, Francisco García, Carmen De Las Heras and Pilar Fatás
Remote Sens. 2024, 16(20), 3905; https://doi.org/10.3390/rs16203905 - 21 Oct 2024
Cited by 2 | Viewed by 1392
Abstract
This study addresses the features of the internal structure of the geological layers adjacent to the Polychrome Hall ceiling of the Cave of Altamira (Spain) and their link to the distribution of moisture and geological discontinuities mainly as fractures, joints, bedding planes and [...] Read more.
This study addresses the features of the internal structure of the geological layers adjacent to the Polychrome Hall ceiling of the Cave of Altamira (Spain) and their link to the distribution of moisture and geological discontinuities mainly as fractures, joints, bedding planes and detachments, using 3D Ground Penetrating Radar (GPR) mapping. In this research, 3D GPR data were collected with 300 MHz, 800 MHz and 1.6 GHz center frequency antennas. The data recorded with these three frequency antennas were combined to further our understanding of the layout of geological discontinuities and how they link to the moisture or water inputs that infiltrate and reach the ceiling surface where the rock art of the Polychrome Hall is located. The same 1 × 1 m2 area was adopted for 3D data acquisition with the three antennas, obtaining 3D isosurface (isoattribute-surface) images of internal distribution of moisture and structural features of the Polychrome Hall ceiling. The results derived from this study reveal significant insights into the overlying karst strata of Polychrome Hall, particularly the interface between the Polychrome Layer and the underlying Dolomitic Layer. The results show moisture patterns associated with geological features such as fractures, joints, detachments of strata and microcatchments, elucidating the mechanisms driving capillary rise and water infiltration coming from higher altitudes. The study primarily identifies areas of increased moisture content, correlating with earlier observations and enhancing our understanding of water infiltration patterns. This underscores the utility of 3D GPR as an essential tool for informing and putting conservation measures into practice. By delineating subsurface structures and moisture dynamics, this research contributes to a deeper analysis of the deterioration processes directly associated with the infiltration water both in this ceiling and in the rest of the Cave of Altamira, providing information to determine its future geological and hydrogeological evolution. Full article
(This article belongs to the Special Issue Multi-Data Applied to Near-Surface Geophysics (Second Edition))
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