Special Issue "Geological Modelling, Volume II"

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Deposits".

Deadline for manuscript submissions: closed (15 December 2020).

Special Issue Editor

Dr. José António de Almeida
E-Mail Website
Guest Editor
Earth Sciences Department and GeoBioTec, FCT - NOVA University of Lisbon, 2829-516 Caparica, Portugal
Interests: geological modelling; geostatistics; data analysis; mineral resource assessment; circular economy
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Geological modelling is a broad and multidisciplinary field that aims to build realistic computed representations (geological models) of geological features and structures, representing both morphology and properties. The information used to build such models is extremely heterogeneous, deriving from diverse sources including boreholes, geological field mapping, geophysical surveying, and physical and chemical measurements. Geological models are of paramount importance for activities at the Earth’s surface, such as in exploration and extraction of geological resources like raw materials, groundwater, oil and gas, geotechnical engineering (e.g., for excavation and foundations), remediation of soils and groundwater on contaminated sites, and risk analysis (e.g., slope instability and subsidence).

There are numerous tools available to build geological models, and for each project, a workflow should be planned, showing the tools and procedures to be employed. Although some modelling algorithms have reached a mature stage, there are still many areas where improvements are required, such as the combination of input data of different spatial resolutions, construction of high-resolution models for complex geological formations, and high-density object-based models, such as fractures and channels, and constraining properties to morphology. These issues continue to invigorate the research agenda.

The Special Issue "Geological Modelling, Volume II" invites papers dealing with geological modelling, including original applications and directions in research.

Prof. Dr. José António de Almeida
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 papers will be 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. Minerals 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

  • 3D/4D geological models
  • Applications of geological modelling
  • Genetic approaches
  • Geostatistics
  • High-resolution geological models
  • Integration of multiple data sources
  • Assessment of uncertainty
  • Object-based models
  • Realism

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Published Papers (6 papers)

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Article
A Multi-Model Ensemble Approach for Gold Mineral Prospectivity Mapping: A Case Study on the Beishan Region, Western China
Minerals 2020, 10(12), 1126; https://doi.org/10.3390/min10121126 - 15 Dec 2020
Viewed by 708
Abstract
Mineral prospectivity mapping (MPM) needs robust predictive techniques so that the target zones of mineral deposits can be accurately delineated at a specific location. Although an individual machine learning algorithm has been successfully applied, it remains a challenge because of the complicated non-linear [...] Read more.
Mineral prospectivity mapping (MPM) needs robust predictive techniques so that the target zones of mineral deposits can be accurately delineated at a specific location. Although an individual machine learning algorithm has been successfully applied, it remains a challenge because of the complicated non-linear relations between prospecting factors and deposits. Ensemble learning methods were efficiently applied for their excellent generalization, but their potential has not been fully explored in MPM. In this study, three well-known machine learning models, namely random forest (RF), support vector machine (SVM), and the maximum entropy model (MaxEnt), were fused into ensembles (i.e., RF–SVM, RF–MaxEnt, SVM–MaxEnt, RF–SVM–MaxEnt) to produce a final prediction. The paper aims to investigate the potential application of stacking ensemble learning methods (SELM) for MPM. In this study, 69 hydrothermal gold deposits were split into two parts: 70% for the training model and 30% for testing the model. Then, 11 mineral prospecting factors were selected as a spatial dataset constructed for MPM. Finally, the models’ performance was assessed using the receiver operating characteristic (ROC) curves and five statistical metrics. Compared with other single methods, the SELM framework showed an improved predictive performance in the model evaluation. Therefore, this finding suggests that the SELM framework is promising and should be selected as an alternative technique for MPM. Full article
(This article belongs to the Special Issue Geological Modelling, Volume II)
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Article
Stochastic Open-Pit Mine Production Scheduling: A Case Study of an Iron Deposit
Minerals 2020, 10(7), 585; https://doi.org/10.3390/min10070585 - 29 Jun 2020
Cited by 6 | Viewed by 1315
Abstract
Production planning decisions in the mining industry are affected by geological, geometallurgical, economic and operational information. However, the traditional approach to address this problem often relies on simplified models that ignore the variability and uncertainty of these parameters. In this paper, two main [...] Read more.
Production planning decisions in the mining industry are affected by geological, geometallurgical, economic and operational information. However, the traditional approach to address this problem often relies on simplified models that ignore the variability and uncertainty of these parameters. In this paper, two main sources of uncertainty are combined to obtain multiple simulated block models in an iron ore deposit that include the rock type and seven quantitative variables (grades of Fe, SiO2, S, P and K, magnetic ratio and specific gravity). To assess the effect of integrating these two sources of uncertainty in mine planning decision, stochastic and deterministic production scheduling models are applied based on the simulated block models. The results show the capacity of the stochastic mine planning model to identify and minimize risks, obtaining valuable information in ore content or quality at early stages of the project, and improving decision-making with respect to the deterministic production scheduling. Numerically speaking, the stochastic mine planning model improves 6% expected cumulative discounted cash flow and generates 16% more iron ore than deterministic model. Full article
(This article belongs to the Special Issue Geological Modelling, Volume II)
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Article
Seabed Mapping Using Shipboard Multibeam Acoustic Data for Assessing the Spatial Distribution of Ferromanganese Crusts on Seamounts in the Western Pacific
Minerals 2020, 10(2), 155; https://doi.org/10.3390/min10020155 - 11 Feb 2020
Cited by 3 | Viewed by 1574
Abstract
Cobalt-rich ferromanganese crusts (Fe–Mn crusts), potential economic resources for cobalt, nickel, platinum, and other rare metals, are distributed on the surface of seamounts, ridges, and plateaus. Distribution of Fe–Mn crust deposits and their geomorphological characteristics are prerequisites to selecting possible mining sites and [...] Read more.
Cobalt-rich ferromanganese crusts (Fe–Mn crusts), potential economic resources for cobalt, nickel, platinum, and other rare metals, are distributed on the surface of seamounts, ridges, and plateaus. Distribution of Fe–Mn crust deposits and their geomorphological characteristics are prerequisites to selecting possible mining sites and to predicting the environmental impact of deep-sea mining activity. Here, we map the spatial distribution of Fe–Mn crust deposits on seamount summits and flanks in the Western Pacific using shipboard multibeam echo sounder (MBES) data and seafloor images from a deep-towed camera system (DCS) and evaluate the relationship between acoustic backscatter variations and the occurrence of Fe–Mn crusts. We find a positive correlation between high backscatter intensity, steep seabed slope gradients, and the occurrence of Fe–Mn crusts. However, our analysis was not effective to distinguish the spatial boundary between several seabed types that occur over small areas in mixed seabed zones, particularly where transition zones and discontinuous seabed types are present. Thus, we conclude that MBES data can be a valuable tool for constraining spatial distribution of Fe–Mn crust deposits over a large exploration area. Full article
(This article belongs to the Special Issue Geological Modelling, Volume II)
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Article
Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China
Minerals 2020, 10(2), 102; https://doi.org/10.3390/min10020102 - 24 Jan 2020
Cited by 22 | Viewed by 1707
Abstract
Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including [...] Read more.
Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and a deep learning convolutional neural network (CNN), were employed to conduct a data-driven W prospectivity modelling of the southern Jiangxi Province, China. A total of 118 known W occurrences derived from long-term exploration of this brownfield area and eight evidential layers of multi-source geoscience information related to W mineralization constituted the input datasets. This provided a data-rich foundation for training machine learning models. The optimal configuration of model parameters was trained by a grid search procedure and validated by 10-fold cross-validation. The resulting predictive models were comprehensively assessed by a confusion matrix, receiver operating characteristic curve, and success-rate curve. The modelling results indicate that the CNN model achieves the best classification performance with an accuracy of 92.38%, followed by the RF model (87.62%). In contrast, the RF model outperforms the rest of ML models in overall predictive performance and predictive efficiency. This is characterized by the highest value of area under the curve and the steepest slope of success-rate curve. The RF model was chosen as the optimal model for mineral prospectivity in this region as it is the best predictor. The prospective zones delineated by the prospectivity map occupy 9% of the study area and capture 66.95% of the known mineral occurrences. The geological interpretation of the model reveals that previously neglected Mn anomalies are significant indicators. This implies that enrichment of ore-forming material in the host rocks may play an important role in the formation process of wolframite and can represent an innovative exploration criterion for further exploration in this area. Full article
(This article belongs to the Special Issue Geological Modelling, Volume II)
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Article
Application of a Maximum Entropy Model for Mineral Prospectivity Maps
Minerals 2019, 9(9), 556; https://doi.org/10.3390/min9090556 - 15 Sep 2019
Cited by 4 | Viewed by 1187
Abstract
The effective integration of geochemical data with multisource geoscience data is a necessary condition for mapping mineral prospects. In the present study, based on the maximum entropy principle, a maximum entropy model (MaxEnt model) was established to predict the potential distribution of copper [...] Read more.
The effective integration of geochemical data with multisource geoscience data is a necessary condition for mapping mineral prospects. In the present study, based on the maximum entropy principle, a maximum entropy model (MaxEnt model) was established to predict the potential distribution of copper deposits by integrating 43 ore-controlling factors from geological, geochemical and geophysical data. The MaxEnt model was used to screen the ore-controlling factors, and eight ore-controlling factors (i.e., stratigraphic combination entropy, structural iso-density, Cu, Hg, Li, La, U, Na2O) were selected to establish the MaxEnt model to determine the highest potential zone of copper deposits. The spatial correlation between each ore-controlling factor and the occurrence of a copper mine was studied using a response curve, and the relative importance of each ore-controlling factor was determined by jackknife analysis in the MaxEnt model. The results show that the occurrence of copper ore is positively correlated with the content of Cu, Hg, La, structural iso-density and stratigraphic combination entropy, and negatively correlated with the content of Na2O, Li and U. The model’s performance was evaluated by the area under the receiver operating characteristic curve (AUC), Cohen’s maximized Kappa and true skill statistic (TSS) (training AUC = 0.84, test AUC = 0.8, maximum Kappa = 0.5 and maximum TSS = 0.6). The results indicate that the model can effectively integrate multi-source geospatial data to map mineral prospectivity. Full article
(This article belongs to the Special Issue Geological Modelling, Volume II)
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Case Report
Assessing the Impact of Geologic Contact Dilution in Ore/Waste Classification in the Gol-Gohar Iron Ore Mine, Southeastern Iran
Minerals 2020, 10(4), 336; https://doi.org/10.3390/min10040336 - 09 Apr 2020
Cited by 2 | Viewed by 1696
Abstract
Since the Gol-Gohar iron ore mine (GGIOM), which is located in southeastern Iran, is currently one of the biggest iron mines in this region, increasing the accuracy of its mineral resources model has become a challenge for geologists, metallurgists and mining engineers. Given [...] Read more.
Since the Gol-Gohar iron ore mine (GGIOM), which is located in southeastern Iran, is currently one of the biggest iron mines in this region, increasing the accuracy of its mineral resources model has become a challenge for geologists, metallurgists and mining engineers. Given that an accurate classification of the mining blocks into ore or waste is highly significant in strategic mine planning, three approaches for simulating the iron grades were compared against the true grades obtained from production data. The comparison was done by calculating the ratio between the total number of blocks correctly classified as ore and waste and the total number of misclassified blocks, and it was conducted for each approach in three mined benches at the GGIOM. The results reveal that the grade simulation that ignores the geological boundaries and the grade simulation based on a deterministic geological interpretation are much less accurate than the hierarchical approach, which consists of simulating both the geological boundaries and the grades. Full article
(This article belongs to the Special Issue Geological Modelling, Volume II)
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