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Geosciences
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3D Modelling/Inversion for Mineral Exploration
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3D Modelling/Inversion for Mineral Exploration

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geophysics".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 10803

Special Issue Editors

Prof. Dr. Maxim Smirnov

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Guest Editor
Department of Civil, Environmental and Natural resources engineering, Luleå University of Technology, 971 87 Luleå, Sweden
Interests: electromagnetic methods; data processing; modelling; inversion; lithosphere; mineral
Dr. Maria Smirnova

E-Mail Website
Guest Editor
Institue of Geophysics and Meteorology, University of Cologne, 50923 Köln, Germany
Interests: near-surface geophysics; geoelectric and electromagnetic methods; inversion; software development
Prof. Dr. Thorkild Maack Rasmussen

E-Mail Website
Guest Editor
Department of Civil, Environmental and Natural resources engineering, Luleå University of Technology, 971 87 Luleå, Sweden
Interests: mineral exploration; regional and local scale geophysics; inversion; data integration

Special Issue Information

Dear colleagues,

This special issue aims to highlight recent developments in 3D modelling and inversion of geophysical data with emphasis on mineral exploration. This includes, but not limited to, airborne, semi-airborne, ground-based, and borehole methods.

Geophysical methods provide tools for subsurface exploration and monitoring. They are particularly valuable in settings with complex geology where direct methods alone can be too expensive. Recently, a lot of efforts have been made to facilitate the development of joint 3D inversion techniques. These algorithms try to create a unique model describing all available information from different types of geophysical data.  Despite concepts like Common Earth Modelling (CEM) has evolved, the joint inversion remains a challenging task.

Additionally, large interest is drawn lately to drone-borne geophysics. Such systems allow faster, high data density and relatively cheap surveying. However, modelling and inversion of large amount of data produced in airborne surveys have high computational requirements. As a result, a lot of efforts have been put into optimisation of the forward and inversion algorithms dealing with multiple sources and frequencies.  

We hope to draw attention to the importance of more integrated interpretation; how to quantify and evaluate uncertainties in geological interpretations, models, and experiments that broaden their applicability and increase their value. Therefore, we welcome contributions covering all aspects of modelling and inversion theory in 3D, and their applications to mineral exploration. Research articles, that provide new insights into multi-disciplinary data integration, CEM, 3D/4D inversion algorithms, and open source software developments are invited. New techniques based on machine learning approaches also fit into the topic.

Prof. Dr. Maxim Smirnov
Dr. Maria Smirnova
Prof. Dr. Thorkild Maack Rasmussen
Guest Editors

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. Geosciences is an international peer-reviewed open access monthly 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 1800 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

  • Mineral exploration
  • 3D inversion
  • 3D modelling
  • joint inversion
  • geophysics
  • exploration
  • minerals
  • airborne
  • geoelectrics
  • seismic
  • potential fields
  • electromagnetic
  • induced polarization
  • common earth modelling
  • interpretation

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Published Papers (3 papers)

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Research

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17 pages, 5714 KiB  
Article
A Comparison Study of Explicit and Implicit 3-D Transient Electromagnetic Forward Modeling Schemes on Multi-Resolution Grid
by Jingyu Gao, Maxim Smirnov, Maria Smirnova and Gary Egbert
Geosciences 2021, 11(6), 257; https://doi.org/10.3390/geosciences11060257 - 15 Jun 2021
Cited by 4 | Viewed by 2840
Abstract
This study compares the efficiency of 3-D transient electromagnetic forward modeling schemes on the multi-resolution grid for various modeling scenarios. We developed time-domain finite-difference modeling based on the explicit scheme earlier. In this work, we additionally implement 3-D transient electromagnetic forward modeling using [...] Read more.
This study compares the efficiency of 3-D transient electromagnetic forward modeling schemes on the multi-resolution grid for various modeling scenarios. We developed time-domain finite-difference modeling based on the explicit scheme earlier. In this work, we additionally implement 3-D transient electromagnetic forward modeling using the backward Euler implicit scheme. The iterative solver is used for solving the system of equations and requires a proper initial guess that has significant effect on the convergence. The standard approach usually employs the solution of a previous time step as an initial guess, which might be too conservative. Instead, we test various initial guesses based on the linear extrapolation or linear combination of the solutions from several previous steps. We build up the implicit scheme forward modeling on the multi-resolution grid, which allows for the adjustment of the horizontal resolution with depth, hence improving the performance of the forward operator. Synthetic examples show the implicit scheme forward modeling using the linearly combined initial guess estimate on the multi-resolution grid additionally reduces the run time compared to the standard initial guess approach. The result of comparison between the implicit scheme developed here with the previously developed explicit scheme shows that the explicit scheme modeling is more efficient for more conductive background models often found in environmental studies. However, the implicit scheme modeling is more suitable for the simulation with highly resistive background models, usually occurring in mineral exploration scenarios. Thus, the inverse problem can be solved using more efficient forward solution depending on the modeling setup and background resistivity. Full article
(This article belongs to the Special Issue 3D Modelling/Inversion for Mineral Exploration)
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19 pages, 1494 KiB  
Article
Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit
by Nilgün Güdük, Miguel de la Varga, Janne Kaukolinna and Florian Wellmann
Geosciences 2021, 11(4), 150; https://doi.org/10.3390/geosciences11040150 - 26 Mar 2021
Cited by 10 | Viewed by 3648
Abstract
Structural geological models are widely used to represent relevant geological interfaces and property distributions in the subsurface. Considering the inherent uncertainty of these models, the non-uniqueness of geophysical inverse problems, and the growing availability of data, there is a need for methods that [...] Read more.
Structural geological models are widely used to represent relevant geological interfaces and property distributions in the subsurface. Considering the inherent uncertainty of these models, the non-uniqueness of geophysical inverse problems, and the growing availability of data, there is a need for methods that integrate different types of data consistently and consider the uncertainties quantitatively. Probabilistic inference provides a suitable tool for this purpose. Using a Bayesian framework, geological modeling can be considered as an integral part of the inversion and thereby naturally constrain geophysical inversion procedures. This integration prevents geologically unrealistic results and provides the opportunity to include geological and geophysical information in the inversion. This information can be from different sources and is added to the framework through likelihood functions. We applied this methodology to the structurally complex Kevitsa deposit in Finland. We started with an interpretation-based 3D geological model and defined the uncertainties in our geological model through probability density functions. Airborne magnetic data and geological interpretations of borehole data were used to define geophysical and geological likelihoods, respectively. The geophysical data were linked to the uncertain structural parameters through the rock properties. The result of the inverse problem was an ensemble of realized models. These structural models and their uncertainties are visualized using information entropy, which allows for quantitative analysis. Our results show that with our methodology, we can use well-defined likelihood functions to add meaningful information to our initial model without requiring a computationally-heavy full grid inversion, discrepancies between model and data are spotted more easily, and the complementary strength of different types of data can be integrated into one framework. Full article
(This article belongs to the Special Issue 3D Modelling/Inversion for Mineral Exploration)
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Review

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15 pages, 3294 KiB  
Review
Advanced Methods of Joint Inversion of Multiphysics Data for Mineral Exploration
by Michael S. Zhdanov, Michael Jorgensen and Leif Cox
Geosciences 2021, 11(6), 262; https://doi.org/10.3390/geosciences11060262 - 21 Jun 2021
Cited by 21 | Viewed by 3434
Abstract
Different geophysical methods provide information about various physical properties of rock formations and mineralization. In many cases, this information is mutually complementary. At the same time, inversion of the data for a particular survey is subject to considerable uncertainty and ambiguity as to [...] Read more.
Different geophysical methods provide information about various physical properties of rock formations and mineralization. In many cases, this information is mutually complementary. At the same time, inversion of the data for a particular survey is subject to considerable uncertainty and ambiguity as to causative body geometry and intrinsic physical property contrast. One productive approach to reducing uncertainty is to jointly invert several types of data. Non-uniqueness can also be reduced by incorporating additional information derived from available geological and/or geophysical data in the survey area to reduce the searching space for the solution. This additional information can be incorporated in the form of a joint inversion of multiphysics data. This paper presents an overview of the main ideas and principles of novel methods of joint inversion, developed over the last decade, which do not require a priori knowledge about specific empirical or statistical relationships between the different model parameters and/or their attributes. These approaches are designated as follows: (1) Gramian constraints; (2) Gramian-based structural constraints; (3) localized Gramian constraints; and (4) joint focusing constraints. We provide a short description of the mathematical foundations of each of these approaches and discuss the practical aspects of their applications in mineral exploration. Full article
(This article belongs to the Special Issue 3D Modelling/Inversion for Mineral Exploration)
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