•Remote sensing (RS) data have been widely used for mapping mineralization zones.•Machine learning (ML) methods can increase the efficiency of RS data.•Different key features can be extracted by ...applying ML methods to RS data.•Lithological units, alteration zones, and structures are from the key features.•Recently developed ML methods have to be applied for mapping mineralization zones.
The decline of the number of newly discovered mineral deposits and increase in demand for different minerals in recent years has led exploration geologists to look for more efficient and innovative methods for processing different data types at each stage of mineral exploration. As a primary step, various features, such as lithological units, alteration types, structures, and indicator minerals, are mapped to aid decision-making in targeting ore deposits. Different types of remote sensing datasets, such as satellite and airborne data, make it possible to overcome common problems associated with mapping geological features. The rapid increase in the volume of remote sensing data obtained from different platforms has encouraged scientists to develop advanced, innovative, and robust data processing methodologies. Machine learning methods can help process a wide range of remote sensing datasets and determine the relationship between components such as the reflectance continuum and features of interest. These methods are robust in processing spectral and ground truth measurements against noise and uncertainties. In recent years, many studies have been carried out by supplementing geological surveys with remote sensing datasets, which is now prominent in geoscience research. This paper provides a comprehensive review of the implementation and adaptation of some popular and recently established machine learning methods for processing different types of remote sensing data and investigates their applications for detecting various ore deposit types. We demonstrate the high capability of combining remote sensing data and machine learning methods for mapping different geological features that are critical for providing potential maps. Moreover, we find there is scope for advanced methods such as deep learning to process the new generation of remote sensing data that provide high spatial and spectral resolution for creating improved mineral prospectivity maps.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is ...a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite ...images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent of a study area at the initial stage of mineral exploration. Principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) are the dimensionality reduction techniques known as multivariate statistical methods that convert a set of observed and correlated input variables into uncorrelated or independent components. In this study, these techniques were comprehensively compared and integrated, to show how they could be jointly applied in remote sensing data analysis for mapping hydrothermal alteration zones associated with epithermal Cu–Au deposits in the Toroud-Chahshirin range, Central Iran. These techniques were applied on specific subsets of the advanced spaceborne thermal emission and reflection radiometer (ASTER) spectral bands for mapping gossans and hydrothermal alteration zones, such as argillic, propylitic, and phyllic zones. The fuzzy logic model was used for integrating the most rational thematic layers derived from the transformation techniques, which led to an efficient remote sensing evidential layer for mineral prospectivity mapping. The results showed that ICA was a more robust technique for generating hydrothermal alteration thematic layers, compared to the other dimensionality reduction techniques. The capabilities of this technique in separating source signals from noise led to improved enhancement of geological features, such as specific alteration zones. In this investigation, several previously unmapped prospective zones were detected using the integrated hydrothermal alteration map and most of the known hydrothermal mineral occurrences showed a high prospectivity value. Fieldwork and laboratory analysis were conducted to validate the results and to verify new prospective zones in the study area, which indicated a good consistency with the remote sensing output. This study demonstrated that the integration of remote sensing-based alteration thematic layers derived from the transformation techniques is a reliable and low-cost approach for mineral prospectivity mapping in metallogenic provinces, at the reconnaissance stage of mineral exploration.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques, which do not fully provide quantification of uncertainty in the constructed ...models and fail to optimally weight geological field observations against constraints from geophysical data. Here, using the Bayesian Obsidian software package, we develop a methodology to fuse lithostratigraphic field observations with aeromagnetic and gravity data to build a 3D model in a small (13.5 km × 13.5 km) region of the Gascoyne Province, Western Australia. Our approach is validated by comparing 3D model results to independently-constrained geological maps and cross-sections produced by the Geological Survey of Western Australia. By fusing geological field data with aeromagnetic and gravity surveys, we show that 89% of the modelled region has >95% certainty for a particular geological unit for the given model and data. The boundaries between geological units are characterized by narrow regions with <95% certainty, which are typically 400–1000 m wide at the Earth’s surface and 500–2000 m wide at depth. Beyond ~4 km depth, the model requires geophysical survey data with longer wavelengths (e.g., active seismic) to constrain the deeper subsurface. Although Obsidian was originally built for sedimentary basin problems, there is reasonable applicability to deformed terranes such as the Gascoyne Province. Ultimately, modification of the Bayesian engine to incorporate structural data will aid in developing more robust 3D models. Nevertheless, our results show that surface geological observations fused with geophysical survey data can yield reasonable 3D geological models with narrow uncertainty regions at the surface and shallow subsurface, which will be especially valuable for mineral exploration and the development of 3D geological models under cover.
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•Bayesian fusion of lithostratigraphic observations with geophysical data•Technique validated in a data-rich area of the Gascoyne Province, Western Australia•Almost 90% of region’s surface modelled at >95% certainty•Less precise constraints at depths; poor constraints deeper than 4 km.•Technique useful for greenfields mineral exploration
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
In recent years, a variety of remote sensing data have been widely used in mineral exploration, particularly at the reconnaissance stage for mapping alteration zones and investigating the ...relationship between tectonic structures and target mineralization. Moreover, different image processing methods such as band ratios, dimensionality reduction, and classification have been developed for enhancing target features in various mineralization systems. The Sanandaj-Sirjan metamorphic zone in Iran is a potential zone for different types of metallic mineralization, such as copper, molybdenum, and gold. Contrary to other metallic deposits, only a few studies have been carried out to map gold deposits in this region using remote sensing data. This study aims to map tectonic lineaments and those alteration zones known to be relevant to orogenic gold deposits in the Qolqoleh-Kasnazan shear zone, a portion of the Sanandaj-Sirjan zone in the southwest of Saqez, Iran. We use two well-known and efficient image processing methods, i.e., band ratios and principal component analysis, for processing ASTER images and mapping alteration zones. Moreover, we map tectonic lineaments using the first principal component of Landsat 8 data. The study of alteration zones and lineaments reveals a major trend of NW-SE for gold mineralization in the study area. It is observed that mineralized zones are associated with iron oxides and argillic alteration zones and the phyllic alteration is predominant at the margin and away from the center of mineralization zones.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
There are several statistical methodologies presented for separating anomalous values from background leading to determination of anomalous areas. These methods range from simple approaches to ...complicated ones and include nonstructural and structural methods, subtraction separation method and so on. Structural methods take the sampling locations and their spatial relation into account for estimating the anomalous areas. The U-statistic method is one of the most important structural methods. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and also to determine anomalous areas. In the present study, several nonstructural methods including assessment of threshold based on median and standard deviation, median absolute deviation (MAD) and P.N product are used and U-statistic is considered as structural method to assess prospective areas of Parkam district. Results show that MAD method reduced background well and P.N method increased correlation of points. However, U-statistic method plays the role of both mentioned advantages meaning in addition to reducing outlier data effect, it regularizes anomalous values and also their dispersion is reduced significantly. It is possible to determine anomaly areas according to anomalous samples positioning so that denser areas are more important. Finally, lithogeochemical map of study area is provided for lead and zinc.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In the present study, an investigation was carried out on Parvadeh coal mine in Tabas, Iran, to survey the effect of fractures on unusual methane gas emission in coal mines. This coal mine was chosen ...to be investigated because of its high methane gas content in the coal body and available data from sensors in desired locations. Gas concentration monitoring programs were carried out at the mine site and a large amount of data were collected and analyzed. It is revealed that there is a good correlation between excavating the fracture-bearing faces and high methane gas emission events at the mine site. High gas emissions have been observed before, during, or after excavating the fracturebearing faces. When gas content is high and all boundary conditions are met, rockbursts, faults movement and also mining activities can trigger unusual gas emission, and sometimes the gas gushes are violent enough to fit into the category of gas outbursts. Since the fracture generation is happening before the increase of gas concentration in the air, a sensitive and highly accurate microseismic monitoring system can be used to detect locations of rock fracturing, thus provide an effective means to issue warnings of high gas emission in the working area.
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•Novel machine learning-based framework for exploring critical mineral deposits.•Improved generative adversarial network for data augmentation.•Prospectivity maps show a strong ...correlation with known mineral occurrences.•Models outperform standard random forest classifier.•Geophysical features crucial in mapping prospective critical mineral regions.
The demand for critical minerals is rapidly increasing worldwide, yet future global supply remains uncertain due to the difficulty in discovering new deposits using traditional methods. To increase the success rate of exploration projects for these vital resources, the use of artificial intelligence is continuously increasing for big and complex data analysis. This study proposes a new machine learning-based framework that tackles common problems associated with exploring critical mineral deposits, such as the shortage of known mineral occurrences, challenges in selecting negative samples in barren regions, and unbalanced training data. Our framework combines an improved generative adversarial network with positive and unlabelled learning to enhance efficiency. To test the performance of the framework, we create prospectivity maps of mafic–ultramafic intrusion-hosted mineralisation for cobalt, chromium, and nickel in the Gawler Craton, South Australia. The models are trained on a carefully selected set of independent features based on a conceptual model derived from open-access exploration data, resulting in high and stable performance. The prospectivity maps show a strong spatial correlation between high probabilities and known mineral occurrences and predict potential greenfield regions for future exploration. Our models demonstrate a significantly higher accuracy compared to a conventional approach using a standard random forest classifier and reveal that geophysical features play a crucial role in mapping prospective regions of critical minerals. Overall, our framework has the potential to significantly enhance critical mineral exploration by providing a more accurate and efficient approach to identifying prospective regions for future mining operations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
The extraction of tectonic lineaments from digital satellite data is a fundamental application in remote sensing. The location of tectonic lineaments such as faults and dykes are of interest for a ...range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to tectonic lineament extraction is lacking. We present a framework for extracting tectonic lineaments using computer vision techniques. The proposed framework is a combination of edge detection and line extraction algorithms for extracting tectonic lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. The efficiency of two convolutional filters are compared in terms of enhancing the lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia. To validate the results, the extracted lineaments are compared to geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted tectonic lineaments and the GSWA tectonic lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter shows a strong correlation with known sites of hydrothermal mineralization. Hence, our method using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data.
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