Exploration Of Mineral Resources Research Articles

Mapping alteration zones in the Southern section of Yulong copper belt, Tibet using multi-source remote sensing data

Methods for extracting mineralized alteration information using remote sensing images have recently become both efficient and cost-effective. Technology involving the extraction of alteration information based on multi-spectral data has been widely practiced and effectively verified. In recent years, research on fine mineral extraction methods based on hyperspectral data has also been rapidly developing. The Yulong copper belt is a porphyry copper belt located in China with high prospects for mineralization. However, most previous studies focused on the northern section of the Yulong copper belt, with limited exploration of the southern section. In this study, alteration information of the southern section of the Yulong copper belt was extracted from remote sensing data from Landsat-8 OLI, ASTER, and ZY1-02D, and the prospecting potential of this area was evaluated. Principal component analysis was used to extract iron oxide and hydroxyl alteration from Landsat-8 data, in addition to Al hydroxyl and propylitic alterations from ASTER data. Considering the challenge of the extraction of too many pseudo-anomalies using traditional methods, the mixture-tuned matched filtering (MTMF) method was used to more accurately extract iron oxide alterations. Regarding hyperspectral data, the minimum noise fraction and pure pixel index algorithms were used to extract white mica and carbonatite endmembers. The MTMF method was also used for alteration mapping, which took advantage of sub-pixel abundance mapping to finely divide the white mica and carbonatite alterations into five classes. The extraction results of multi-source remote sensing data were then compared and analyzed to avoid occasional single-image extraction results, which confirmed the superiority of the hyperspectral remote sensing and MTMF methods. Combined with field verification, the mineralization alteration information coincided with the spatial location of the Secuo, Mamupu, and Jicuo deposits, which confirmed the accuracy of alteration information extraction. The results of this study confirmed the application potential of remote sensing alteration information extraction in the field of mineral resource exploration. The results have important reference significance for further geological prospecting and exploration in the southern section of the Yulong copper belt.

Gravity inversion with a binary structure constraint imposed by magnetotelluric data and its application to a Carlin-type gold deposit

With characteristics of low cost and high efficiency, the gravity method is one of the most important means for the deep exploration of mineral resources. However, due to its inherent physical properties, its inversion results provide a limited vertical resolution. To reduce the uncertainty of gravity inversion, it is necessary to impose constraints from other geophysical methods, drilling, or geologic models. Based on the high vertical resolution of the multifrequency magnetotelluric (MT) data, we have developed a novel 2D gravity inversion method with a binary structure constraint imposed by MT data. The guided model is the density model reconstructed by the cross-gradient joint inversion of MT and gravity data, and the target and background regions of the guided model are divided using a fuzzy c-means clustering algorithm. The obtained binary target-background model is used as a structural constraint of the gravity inversion. The gravity inversion changes the density values of the target region rather than the background region, which can significantly reduce the dimensions of the inversion solution equation. The experimental results demonstrate that, even if the resistivity model and the density model indicate an inconsistent structure, the binary structure-constrained gravity inversion can reconstruct the density model with a higher degree of resolution than the traditional gravity inversion and cross-gradient joint inversion. Finally, we apply this algorithm to density modeling for the exploration of Carlin-type gold deposits in the southeast of the Jilin province, China, where the image determines the distribution of deep Paleoproterozoic Formation and high-density deposits, providing important technical support for the exploration of Carlin-type gold deposits.

Integration of remote sensing and geophysical data to enhance lithological mapping utilizing the Random Forest classifier: a case study from Komopa, Papua Province, Indonesia

Lithological information is important in mineral resource exploration, geological observations, mine planning or degradation vulnerability assessment. Currently, lithology mapping can be performed in a fast, inexpensive, and easy way using remote sensing data and machine learning. Remote sensing techniques have become a valuable and promising tool for mapping lithological units and searching for minerals. Typically, the integration of remote sensing data with geophysical data provides a better diagnosis to lithological units than single-source mapping methodologies. Accordingly, this study used a combination of remote sensing and airborne geophysical data utilizing the Random Forest algorithm with small training samples to enhance lithology mapping in Komopa, Papua Province, Indonesia. Geophysical data consisting of magnetic, electromagnetic, and radiometric were added one by one gradually to the remote sensing data, which includes Sentinel 2A, ALOS PALSAR, and DEM (digital elevation model) to compare the accuracy of the classification results from each dataset. The results showed that the model that combined remote sensing data and the three types of geophysical data produced the best classification, with an overall accuracy of 0.81, precision of 0.66, recall of 0.47, and F1 score of 0.52. This fused data can increase the accuracy of the classification results by 8% overall accuracy, 6% precision, 11% recall, and 13% F1 score when compared to the model that only used remote sensing data.

Detection of geochemical anomalies related to mineralization using the Random Forest model optimized by the Competitive Mechanism and Beetle Antennae Search

This study aimed to detect the geochemical anomalies related to Fe-mineralization in the Hunjiang area in Jilin Province, China. For this purpose, a Random Forest model was optimized from two aspects of parameters' search and seeking the optimal solution by integrating the “Beetle Antennae Search” and “Competitive Mechanism”. The optimized Random Forest model was compared with the original Random Forest model in the detection of geochemical anomalies related to Fe-mineralization. The results showed that the optimized Random Forest model avoids some limitations of the original Random Forest model, with significant advantages in the detection of mineralization-related anomaly information from exploration geochemical data. It allowed automatic optimization of parameters by integrating two heuristic searching processes. Simultaneously, it focussed more on the quest for the best global optimal solution. Through the case study, it is found that the optimized Random Forest algorithm significantly improved the overall performance for the detection of geochemical anomalies related to Fe-mineralization. The geochemical anomalies detected by the optimized Random Forest model showed close spatial correlation with known iron polymetallic deposits. Therefore, using the optimized Random Forest model to detect geochemical anomalies related to mineralization is of great importance. It also provides a better supporting foundation for geochemical exploration and mineral resource exploration in the Hunjiang area.

Design of the swimming system of a bionic jellyfish robot for seabed exploration

The unmanned sampling and analysis after return of seabed rocks is an important method for deep-sea mineral resources exploration. A novel underwater robot for ultrasonic drilling and sampling tasks of seabed rocks is proposed based on the bionic design method in this paper. The jellyfish-like structure is adopted for the swimming system of the robot, which can move flexibly and stably with low noise, outstanding cruising ability and strong environmental adaptability. The mechanical system and driving control strategy of the robot are designed in this paper. Based on co-simulation method of ADAMS and MATLAB software, the structural parameters of the transmission mechanism of the swimming system are optimized to get larger swing angle under relative less driving power, which will contribute to the improvement of propulsion efficiency. Through the fluid dynamics simulation and analysis in FLUENT software, the propulsion process can be visualized and the propulsion mechanism is revealed. To verify the feasibility of the design, a robot prototype is developed and the experimental platform for the robot's swimming test is built. The underwater experiments on motion characteristics for the robot demonstrates that the maximum propulsive force generated by the swimming system can reach about 16N, and the propulsion speed can reach 0.19m/s. The design and development of the bionic robot for seabed ultrasonic drilling and sampling missions provides new ideas for seabed resource exploration technology.

ELF-EM fields in the multi-layer spherical ‘Earth-ionosphere’ model based on WKB

Abstract With a high signal-to-noise ratio and a great depth of exploration, the wireless electromagnetic method (WEM) has wide applications in the exploration of deep mineral resources and oil and gas reservoirs. Extremely low-frequency electromagnetic (ELF) waves emitted from a horizontal antenna are used to achieve synchronous acquisition for different receivers of multi-coverage information in a global region. However, previous research based on a planar model ignored the curvature of the Earth. This work focuses on the electromagnetic fields (EM fields) in the model of a spherical ‘Earth ionosphere’ to extend the coverage of WEM. By transferring the EM fields from a vertical electric dipole (VED) as well as a vertical magnetic dipole (VMD) in the multi-layered medium of the Earth, we obtain the formulae for the EM fields emitted by a horizontal electric dipole (HED) by using a reciprocity theorem. The correctness of the proposed method is verified by comparing it with the approximate analytical formula and previous work. Based on the above results, we have studied the propagation and frequency characteristics of electromagnetic fields in a spherical waveguide consisting of the ionosphere and earth. The results show that the electromagnetic fields under the spherical model produce interference effects that are different from those of the planar model. The electromagnetic response of the layered Earth was then discussed, and its potential as an electromagnetic technique for exploring the deep Earth was demonstrated.

Application of Induced Polarization Method in Mineral Resource Exploration

The search for hidden minerals with a large, buried depth depends on the guidance of geological and metallogenic theory, but it is difficult to master the geological characteristics of deep deposits. In this paper, taking a survey area as an example, the stimulated polarization geophysical method is used to obtain the information of deep minerals indirectly. Through the analysis and study of IP (induced polarization) anomaly and its characteristics in a tin polymetallic mining area, we delineate the IP anomaly distribution zone of metal ore, identify the distribution of polarized abnormal bodies by IP sounding, and determine the distribution of metallogenic fracture zones in the working area. Through the least square inversion of resistivity and polarizability, the spatial correspondence between the abnormal area of resistivity and polarizability and the location of ore bodies are obtained. The strategy of deep-mineral-induced polarization analysis adopted by us can provide a reliable geophysical basis for rapid deep geological prospecting.

Evidence of hydrothermal fluid migration pathways in pores and microfractures based on nuclear magnetic resonance and electron microscopic analyses from the Southwest Indian Ridge

The formation of massive sulfide deposits on the seafloor is closely related to sub-seafloor hydrothermal circulation. Hydrothermal circulation depends on the connected pores and fractures. Most previous studies have focused on hydrothermal circulation at the macroscopic scale; however, the transport pathways of hydrothermal fluids have rarely been studied at the microscopic scale. This study showed that multiple measurement techniques could characterize the pores of rocks and the mineral fraction surrounding these pores. Nuclear magnetic resonance (NMR) was used to measure the porosity of fresh basalt, altered basalt, massive sulfide, and sulfide chimneys obtained from the Southwest Indian Ridge. Meanwhile, the distribution of pore radius and pore throats in these samples was obtained using the NMR transverse relaxation time distribution. The scanning electron microscopy results and polarizing light microscope images showed that the pores of altered basalts are more developed than those of fresh basalts, which is consistent with the NMR measurement results that altered basalts have a large pore size distribution. In addition, metallic minerals such as pyrite were found to be precipitated in these pores, and microfractures were observed in altered basalts. This demonstrates that the pores and microfractures in these altered basalts provide pathways for fluid transport and are enriched with metal minerals. Altered basalt is closely related to the formation of massive sulfide deposits and is an important ore-prospecting indicator. Therefore, the study of rock pore structures and components can provide a better understanding of hydrothermal circulation and contribute to the exploration of mineral resources.

A Paradigm Shift: North China Craton's North Margin Orogen Is the Collisional Suture With the Columbia Supercontinent

AbstractIn a new study, Wu, Wang, Zhou, Zhao, Haproff, et al. (2022, https://doi.org/10.1029/2022GC010662) present a comprehensive study of the North Margin Orogen of the North China Craton (NCC), showing that older accreted rocks in this belt preserve a record of active margin magmatism from 2.2 to 2.0 Ga, followed by collisional tectonics, marked by mélange and mylonitic shear zones, then granulite facies metamorphism at 1.9–1.8 Ga, marking the final collision of the NCC with the Columbia Supercontinent. The multidisciplinary studies presented in this work support earlier suggestions that the North China Craton amalgamated during accretionary orogenesis in the Neoarchean to earlier Paleoproterozoic, and that the late widespread 1.85 Ga high‐grade metamorphism is craton‐wide in scale, and not confined to a narrow orogen in the center of the craton. This new understanding creates new possibilities for refining reconstructions of one of Earth's earliest, best documented supercontinents, showing a globally linked plate network at 1.85 Ga, and suggests drastic new correlations and models for mineral resource exploration.

Hundred years of geophysics (1834–1933)

Geophysics is relatively a new discipline in comparison to physics or geology. Contrary to general perception as an ancillary to geology, the discipline has been developed with a distinct focus of using the principles and methods of physics for the earth processes. Geophysics was born in the nineteenth century as a necessary element of theoretical geography. Academicians and researchers in the field of geography played chief roles from its birth to its present level of maturity. Historical scholarship in this paper provides an account of the development of the discipline over hundred years from its inception in the early nineteenth century. The article covers, how the discipline was conceptualised and what necessary steps were taken, such as establishing learned societies, publishing technical journals and text books, endowing university chairs, opening up departments and course curriculum, and most importantly, appearing as a primary discipline to cater to exploration for mineral resources.

The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Abstract. Geoscientific models are based on geoscientific data; hence, building better models, in the sense of attaining better predictions, often means acquiring additional data. In decision theory, questions of what additional data are expected to best improve predictions and decisions is within the realm of value of information and Bayesian optimal survey design. However, these approaches often evaluate the optimality of one additional data acquisition campaign at a time. In many real settings, certainly in those related to the exploration of Earth resources, a large sequence of data acquisition campaigns possibly needs to be planned. Geoscientific data acquisition can be expensive and time-consuming, requiring effective measurement campaign planning to optimally allocate resources. Each measurement in a data acquisition sequence has the potential to inform where best to take the following measurements; however, directly optimizing a closed-loop measurement sequence requires solving an intractable combinatoric search problem. In this work, we formulate the sequential geoscientific data acquisition problem as a partially observable Markov decision process (POMDP). We then present methodologies to solve the sequential problem using Monte Carlo planning methods. We demonstrate the effectiveness of the proposed approach on a simple 2D synthetic exploration problem. Tests show that the proposed sequential approach is significantly more effective at reducing uncertainty than conventional methods. Although our approach is discussed in the context of mineral resource exploration, it likely has bearing on other types of geoscientific model questions.

Research on Intelligent recognition of Rock samples based on Deep Learning method

Rock sample recognition plays an important role in oil and gas exploration and mineral resources exploration. At present, the main rock sample identification methods are logging, seismic, gravity and magnetism, geochemistry, hand samples and thin slice analysis and so on. In particular, the mainstream thin section analysis requires professionals to analyze the rock slices manually, but manual detection and preparation of rock slices take a long time. In order to reduce the workload and shorten the time period, the image depth learning method is used to establish the model. After the cuttings and core samples are collected by the industrial camera in the mud logging site, the real-time automatic identification and classification of rock samples and the evaluation of oil and gas-bearing properties are realized. In this paper, the file in .JPG format is cut to discard the information that does not contain rock at the edge. Then it was divided into 4* 4, 6*6, 8*8, 10*10 segments. After image enhancement, the transfer learning method is used to train the neural network using Desnet201 in the built-in library of torch, and the Desnet201 network is selected as our neural network framework. Then the last softmax layer is changed into a multi-layer perceptron with three hidden layers, and the improved Desnet201 is used as a model to identify rock types in this paper.

3-D Gravity Intelligent Inversion by U-Net Network With Data Augmentation

Gravity inversion has become the main method to obtain the 3D density distribution in the exploration of petroleum and mineral resources as well as the study of geological structure. In recent years, with rapid development of deep learning algorithms, gravity intelligent inversion based on convolutional neural networks have also achieved certain good results. However, in the previous study on 3D gravity intelligent inversion, some issues in "data-driven" inversion still need to be faced, that is, insufficient number and lack of the diversity of data sets. This paper selects a suitable U-net network with four-layer down-sampling structure, designs typical block and inclined plate and their combined models to construct the data sets. In order to reduce the overfitting of the network, the data augmentation is introduced to improve the training process for datasets. The data augmentation method greatly alleviates the limited number and types of samples, allowing the network to learn more situations and improve the generalization ability. Theoretical model data tests show that the data augmentation method of Mixup and flip can effectively improve the generalization ability of U-net network, and the noise-adding augmentation has good inversion result for noisy data. The tests on the measured gravity data also verify the effectiveness of the proposed method, which indicates that data augmentation does play a significant role in the improvement of gravity intelligent inversion.

An integrated approach for rapid exploration of carbonatites and related mineral resources

AbstractCarbonatites are proven significant repositories of several critical and strategic elements such as rare earth elements, niobium, thorium, and uranium. Owing to their economic significance, mapping of carbonatites and associated mineral deposits has occupied prominent place in mineral resource exploration programs. In this study an integrated approach was developed to map carbonatite and related mineral deposits in the Loe‐Shilman, Northwest Himalaya of Pakistan, using remotely sensed advance space‐borne thermal emission and reflection radiometer (ASTER) multispectral data and visible near infrared and short‐wave infrared (VNIR‐SWIR) spectral characteristics of minerals in these deposits. Several image enhancement techniques, including band ratio (i.e., B4/B3), principal component and minimum noise fraction transformation (PC6 and MNF5, respectively) helped in highlighting the targeted rocks. The results demonstrate the suitability of ASTER data for discriminating carbonatite related mineral deposits from other surrounding lithologies. Results obtained from these methods were validated through field observations in the area and further confirmed through petrographic and chemical analyses of collected specimens. Field data have also served as training data to perform a supervised classification, allowing further improvement of the mapping results. Moreover, the obtained results from the techniques used for exploring carbonatites and related mineral deposits were stacked together for comparison with each other, to check their sensitivities, and assess their efficiency and accuracy. Generally, all these methods successfully highlighted carbonatites and related mineral deposits; however, when used integratively they exhibit higher degree of accuracy, and has proven to be relatively rapid and cost‐effective.

MINERAL WEALTH OF THE METALLURGICAL FACTORIES OF THE VYATKA PROVINCE IN THE PRE-REFORM PERIOD

The author studied the specifics of the mineral resource base of the iron and copper smelting factories of the mining department on the territory of the Vyatka province at the end of the XVIII - first half of the XIX centuries. The article pays attention to mining legislation of the mineral resources exploration in the Russian Empire. The dynamics of the mines number is studied on the example of the northern and southern economic districts of the Vyatka province. The percentage of fields put into operation has been determined. The reasons of stopping of mines have been revealed. The dependence of the productivity level on the quality of natural resources, technical and technological conditions (predominance of manual labor, lack of mechanisms), personnel problems (shortage of specialists and workers) has been determined. Conclusions are drawn that, on the one hand, the mining and metallurgical industry of the Vyatka province occupied a significant place in the income structure of the region, and on the other hand, the percentage of iron and copper production was half as much as in the Urals, due to the peculiarities of local ores and a number of other reasons.

Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image

Abstract Mineralized indicator minerals are an important geological and mineral exploration indicator. Rapid extraction of mineralized indicator minerals from hyperspectral remote sensing images using ensemble learning model has important geological significance for mineral resources exploration. In this study, two mineralized indicator minerals, limonite and chlorite, exposed at the surface of Qinghai Gouli area were used as the research objects. Sparrow search algorithm (SSA) was combined with random forest (RF) and gradient boosting decision tree (GBDT) ensemble learning models, respectively, to construct hyperspectral mineralized indicative mineral information extraction models in the study area. Youden index (YD) and ore deposit coincidence (ODC) were applied to evaluate the performance of different models in the mineral information extraction. The results indicate that the optimization of SSA parameter algorithm is obvious, and the accuracy of both the integrated learning models after parameter search has been improved substantially, among which the SSA-GBDT model has the best performance, and the YD and the ODC can reach 0.661 and 0.727, respectively. Compared with traditional machine learning model, integrated learning model has higher reliability and stronger generalization performance in hyperspectral mineral information extraction and application, with YD greater than 0.6. In addition, the distribution of mineralized indicative minerals extracted by the ensemble learning model after parameter optimization is basically consistent with the distribution pattern of the fracture tectonic spreading characteristics and known deposits (points) in the area, which is in line with the geological characteristics of mineralization in the study area. Therefore, the classification and extraction model of minerals based on hyperspectral remote sensing technology, combined with the SSA optimization algorithm and ensemble learning model, is an efficient mineral exploration method.

Thoughts on Teaching Reform of Resources Majors in Colleges and Universities under the Background of Carbon Peak and Carbon Neutralization—Taking Mineral Resources Exploration Course as an Example

On 22 September 2020, General Secretary Xi made a solemn commitment to the international community at the general debate of the seventy-fifth session of the United Nations General Assembly to achieve carbon peak and carbon neutrality, and he announced specific goals at the Climate Ambition Summit. China is striving to achieve carbon peak by 2030 and carbon neutrality by 2060. At the same time, during the Fifth Plenary Session of the Nineteenth Central Committee of the CPC and the Central Economic Work Conference, specific arrangements for related goals have been made. During these two sessions in 2021, carbon peak and carbon neutralization were also mentioned in the government work report for the first time, which became the “hot words” discussed by the delegates and one of the key subjects of the 14th Five-Year Plan. Against the current backdrop of “double carbon”, reforms must urgently be made in the education and teaching of the resource major in colleges and universities to achieve the goal of carbon neutralization with high quality, promote discipline construction, and explore and implement the development model of “new engineering”. With regard to the resource exploration industry, mining, dressing and smelting will produce a large amount of carbon dioxide, solid waste and tailings, which can be neutralized by injecting solid waste or tailings reservoirs through green mine construction and carbon capture. On the one hand, the comprehensive utilization efficiency of mines can be improved in this way. On the other hand, a large amount of carbon can be stored underground, which is an important method of achieving the goal of carbon neutrality. In this study, we took the core course “Mineral Resources Exploration” from the resource exploration major as an example to think about and study the ways the education and teaching of resource majors could be reformed, such as exploration engineering under the guidance of the carbon neutrality target, which has certain guiding significance regarding the improvement of the training quality of resource major students.

Prediction of Prospecting Target Based on ResNet Convolutional Neural Network

In recent years, with the development of geological prospecting from shallow ore to deep and hidden ore, the difficulty of prospecting is increasing day by day, so the application of computer technology and new methods of geological and mineral exploration is paid more and more attention. The mining and prediction of geological prospecting information based on deep learning have become the frontier field of earth science. However, as a deep artificial intelligence algorithm, deep learning still has many problems to be solved in the big data mining and prediction of geological prospecting, such as the small number of training samples of geological and mineral images, the difficulty of building deep learning network models, and the universal applicability of deep learning models. In this paper, the training samples and convolutional neural network models suitable for geochemical element data mining are constructed to solve the above problems, and the model is successfully applied to the prediction research of gold, silver, lead and zinc polymetallic metallogenic areas in South China. Taking the Pangxidong research area in the west of Guangdong Province as an example, this paper carries out prospecting target prediction research based on a 1:50000 stream sediment survey original data. Firstly, the support vector machine (SVM) model and statistical method were used to determine the ore-related geochemical element assemblage. Secondly, the experimental data of geochemical elements were augmented and a dataset was established. Finally, ResNet-50 neural network model is used for data training and prediction research. The experimental results show that the areas numbered 9, 29, 38, 40, 95, 111, 114, 124, 144 have great metallogenic potential, and this method would be a promising tool for metallogenic prediction. By applying the ResNet-50 neural network in metallogenic prediction, it can provide a new idea for the future exploration of mineral resources. In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of B area, another deposit research area in South China. The results show that 100% of the prediction area obtained by using the proposed method covers the known ore deposit area. This model also provides method support for further delineating the prospecting target area in study area B.

A transient solution on potential equation of diffusion type with time-varying coefficients

Research on hydrothermal sulphide ore of marine mineral resources exploration is hot recently. According to the general solution of first order differential equation, Laplace transform is used to transform the linear homogeneous differential equation of second order variable coefficient into first order differential equation of complex domain. The general solution of the first-order differential equations in complex domain is similarly obtained without regard for the difference between real domains and complex domains. Further more, the general solutions are deduced to the second order linear differential equations with variable coefficients. Then it is used to solve the thermoelectric coupling electric field model in oceanic strike exploration; A closed analytical solution with integral form of elementary functions is obtained to the simplified diffusion electric field potential model. To get an explicit solution, with the help of MAPLE software, the general solution of the linear combination of Kummer functions is obtained. This research has theoretical significance for the engineering implementation of the frequency domain induced dipole drag system.

The synergistic use of multi-scale remote sensing data for the identification of hydrothermal alteration patterns in Los Frailes, Spain

• FieldSpec4, SisuROCK, AisaFenix and WorldView-3 results are used synergistically. • Spectral data characterise hydrothermal alterations typical for massive sulphides. • Chlorite and white mica mapping results coincide mostly between the data scales. • Mapping discrepancies are explained by different data types and analysis methods. • Multi-scale spectral data can help to identify potential exploration targets. Remote sensing data can be successfully applied in mineral resource prospecting and exploration. With the increasing number of remote sensing sensors and methods, the application of multi-scale remote sensing data has met a broad audience in the geological remote sensing community over the last few years. Multi-scale remote sensing data close the scale gap between distal (satellite) and proximal (laboratory) remote sensing data, offering both a general and detailed overview of the target and allowing a synergistic use. In this study, the volcano-sedimentary hosted massive sulphide ore deposit Los Frailes in the east of the Iberian Pyrite Belt (IPB) in Spain is investigated for the first time with multi-scale remote sensing techniques. A typical hydrothermal alteration zonation is associated to the massive sulphide ore body in Los Frailes, which is characterised by an inner chlorite-rich zonation showing a strong Fe-enrichment and a peripheral muscovite-rich zonation, which is moderately enriched in Fe. The characterisation of these zones support traditional mineral exploration techniques especially in the prospecting phase by detecting regions of interest, thereby reducing time and cost consumption. By evaluating the synergistic use of multi-scale remote sensing data for massive sulphide exploration, an enhanced application for the economic prospecting and exploration of mineral resources in Los Frailes is introduced. Optical data are used for the detection, identification and characterisation of chlorite and white mica occurrences and compositions. A drill core is analysed with the FieldSpec4 Hi-res hyperspectral point spectrometer. Rock samples are investigated in the laboratory using the SisuROCK hyperspectral core imaging station. AisaFENIX hyperspectral field-based and WorldView-3 multispectral satellite data of the open pit are analysed. The mineral(-group) identification and characterisation are performed with either spectral feature fitting methods (hyperspectral) or mineral indices (multispectral). Hyperspectral analyses show Mg-/Al-substitutions due to Fe-enrichment owing to wavelength shifts of the absorption maximum in chlorite and white mica, respectively. The identification and characterisation of the Fe-OH absorption feature of chlorite at around 2250 nm is more challenging compared to the Al-OH absorption feature of muscovite at around 2200 nm because the spectral signature is more subtle regarding the depth and width. However, the chloritic hydrothermal alteration zonation in the footwall is successfully detected in all scales. The hydrothermal muscovite-rich zonation in the footwall is barely outcropping in the pit and is not captured in the drill core. However, the muscovite-rich alteration zonation in the hanging wall is indicated in all scales. Discrepancies in the results arise mainly from different spectral and spatial resolutions of the sensors and differing data acquirement conditions, such as different surface exposures, viewing angles and illumination conditions. The results show that the multi-scale method offers great opportunities to localise potential prospecting and exploration targets in the Iberian Pyrite Belt. The synergistic use of the different remote sensing scales offers a complete picture of the target by gaining macroscopic to regional spectral information collectively, which is superior to using only one scale. The method is relatively fast and cost-effective, which makes the identification of potential exploration targets more efficient.