Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources

Wei Xi; YuanYe Ping; JinTao Tao; Chaoyang Liu; Junru Shen; YaWen Zhang

doi:10.1515/geo-2022-0588

Artikel Open Access

Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources

Wei Xi , YuanYe Ping , JinTao Tao , Chaoyang Liu , Junru Shen und YaWen Zhang

Veröffentlicht/Copyright: 15. Dezember 2023

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Abstract

The Kalatag Ore Cluster Area, located in the Eastern Tianshan metallogenic belt of Xinjiang, stands out as a notable copper polymetallic mineralization zone, recognized for its diverse ore types and untapped potential. Despite the foundational nature of traditional exploration methods, they have not fully exploited this potential. Addressing this, our study leverages modern geospatial technologies, especially ArcGIS, combined with multi-source geoscience data to refine ore formation predictions in Kalatag. We identified key ore-controlling factors: the ore-bearing strata of Daliugou and Dananhu Groups, buffer zones around faults and intrusions, and geophysical anomalies. From these, a conceptual model was developed using the weight of evidence model. This model pinpointed four ‘A’ class and three ‘B’ class targets for mineral exploration, highlighting the central role of faults in ore control. Significantly, all known ore deposits were encompassed within these targets. Our approach not only paves the way for improved ore prediction in Kalatag but also offers a blueprint for other mineral-rich areas. Merging traditional geology with advanced technology, we elevate mineral exploration’s precision, emphasizing the synergy of an integrated method, especially in geologically complex areas. The effectiveness of our model provides insights for future exploration, particularly in mining areas’ deeper zones.

Keywords: mineralization prediction; copper polymetallic deposits; Kalatag; evidence weighting model; ArcGIS; multi-source data fusion

1 Introduction

The exploration and prediction of metallogenic deposits are central to geological research [1,2,3]. As global demands intensify, with industrial behemoths like China leading to the charge, the urgency for efficient and precise prediction models has become increasingly apparent. Traditional studies have undoubtedly made commendable contributions; however, they present a discernible gap: the integration of up-to-date geospatial technologies and datasets with established methodologies [4,5,6]. This situation naturally prompts the crucial question: “How can these modern geospatial tools and datasets bolster our capability to predict and grasp concealed reserves, especially those residing deeper?” Addressing this query, our research endeavors to bridge this gap. By tapping into the weight of evidence model and blending it with state-of-the-art datasets, our goal is to enhance predictions and probe deeper into metallogenic deposits, with a particular focus on the Kalatag region. Nevertheless, it is essential to recognize that conventional methodologies, despite their foundational significance, come with their own set of constraints. They predominantly zero in regions that are well-documented and readily accessible, overlooking the vast potential proffered by cutting-edge tools like ArcGIS and in-depth datasets [7,8,9]. This selective approach has inadvertently left mineral-rich domains, such as the Kalatag Ore Cluster, relatively untouched by these innovative techniques [10].

Building on this, recent research honing in the East Tianshan region and the Kalatag district has unveiled a myriad of facets of these mineral deposits. While invaluable, many of these studies tend to offer fragmented insights, sometimes glossing over the broader, complex dynamics of the region. Delving deeper, a substantial portion of the existing literature is oriented toward the geochemical and chronological nuances of specific deposits, encompassing topics from fluid inclusions interactions to the influential application of remote sensing in mineral exploration. Renowned studies by the likes of Sun et al. [11], Cheng et al. [12], Zhou et al. [13], and Zhang et al. [14] have provided holistic analyses of ore deposit formation and evolution, bringing to light crucial geological events and contexts. In parallel, studies led by researchers such as Cheng et al. [15], Yang et al. [16], and Deng et al. [17] have illuminated the intricate geochemical processes underpinning these deposits. Furthermore, pioneering works by Wu et al. [18] and Tao et al. [19] underscore the transformative potential of modern geospatial tools in mineral exploration. Collectively, this body of work elucidates the complex interplay of geological, geochemical, and geochronological factors steering mineralization processes. These collective endeavors have vastly augmented our comprehension of the region’s mineral potential, thereby setting a solid groundwork for upcoming exploration projects.

Yet, despite this depth of knowledge, a palpable void remains. Current studies, comprehensive as they might be, often stop short of providing a truly integrated perspective that cohesively bridges the myriad datasets and methodologies at play. Embracing a more holistic strategy could not only deepen our insights into existing deposits but also open avenues for identifying new exploration targets and fostering a profound understanding of the region’s overarching geological dynamics. It is against this backdrop that our research comes to the fore, offering three core contributions:

Comprehensive approach: While numerous studies delve deep into specific ore deposits, our research champions a panoramic, integrated perspective.
Deep deposit exploration: With surface deposits dwindling, the exploration and prediction of subterranean deposits have emerged as a pressing concern, a domain often overlooked in extant research.
Harnessing advanced analytics: While there is a burgeoning interest in modern geospatial technologies, the vast potential of data analysis and machine learning remains largely underutilized.

In wrapping up, our research signifies a groundbreaking step in the exploration and prediction of metallogenic deposits within the East Tianshan framework. By synergistically melding advanced geospatial tools, avant-garde analytical techniques, and a profound understanding of diverse geological nuances, we are on a mission to unravel the entirety of the Kalatag district’s mineral potential, thereby charting a path for subsequent exploration initiatives.

2 Geological background

The East Tianshan in Xinjiang, as an essential part of the Central Asian metallogenic domain, is located at the easternmost part of the Tianshan orogenic belt, extending about 600 km in length and 300 km in width, distributed between the Tarim Basin and the Junggar Basin in an east-west direction [20,21,22]. The Kalatag Ore Cluster Area is situated in the middle part of the southern edge of the copper ore belt in the Turpan-Hami Basin in East Tianshan, belonging to a secondary uplift structural unit of the northern belt of the Dadao Lake-Tousuqundao Arc [23,24] (Figure 1). The exposed strata in the area, from inside to outside, include the Ordovician Huangcaopo Group of thick volcanic clastic rocks-volcanic rocks, the Devonian Dadao Lake Group volcanic clastic rocks, Carboniferous Qishan Group volcanic rocks, Middle Permian Karagang Group volcanic rocks, and Triassic Gonghe River Group volcanic rocks, as well as the Cenozoic sediments. The Ordovician Daliugou Group and the Devonian Dadao Lake Group strata developed in the central and southern parts of the Kalatag Ore Cluster Area are the primary host rocks of copper polymetallic deposits.

Figure 1

(a) Tectonic sketch map of the Central Asian Orogenic Belt (modified after [35]) and (b) geological map and deposit distributions in the Kalatag Mining area (modified after [30]).

The Daliugou Group is a marine volcanic rock construction set dominated by effusive phases with continuous and intense volcanic activity. The lithology is a calc-alkaline series, including rhyolite, trachyte, andesite, and basalt, with intermediate-acid volcanic rock being the most developed, similar to the combination of island arc volcanic rocks [25]. The Dadao Lake Group unconformably overlies the Daliugou Group. It is a set of marine volcanic clastic rocks (mainly tuffs), and volcanic clastic sedimentary rocks (mainly tuffs, tuffaceous sandstone), interbedded with carbonate rocks and intermediate volcanic lavas. Additionally, the middle and lower parts of the limestone contain many coral and brachiopod fossils. The area is mainly controlled by N-NW, W-NW, and N-NE trending faults, among which the N-NW and N-NE faults control the volcanic activities in the area, and the W-NW faults control the early Paleozoic magmatism and sedimentation [26,27]. The intrusive rocks in the Kalatag Ore Cluster Area are relatively developed, mainly consisting of Ordovician and Silurian diorites, gabbros, granodiorites, porphyries, granitic porphyries, and other granites. Among them, the Kalatag pluton is the largest, covering an area of 70 km², as a multi-phase intrusive complex, including granitic porphyry, monzogranite, and quartz diorite. Other intrusions are minor in scale, including late Carboniferous quartz diorite, porphyry, gabbro, and other granites.

Rare multi-type copper polymetallic deposits are developed in the area, including volcanic massive sulfide (VMS) type (such as Honghai Cu (Zn) mine), porphyry type (such as Yudai Cu (Mo–Au) mine), epithermal type (such as Hongshi Cu (Mo–Au) mine), skarn type (such as Xierqu Cu–Fe (Au) mine), and magmatic-hydrothermal type (such as Yueyawan Cu–Ni mine), with mineralization epochs ranging from the Ordovician to the Permian. Among them, the late Ordovician–Silurian (450–430 Ma) mineralization-related granites, with complex composition, are closely related to vein hydrothermal copper, gold, and zinc polymetallic mineralization. Typical deposits include the Yudai porphyry copper–gold deposit, the Honghai (Huangtupo) VMS copper–zinc deposit, and the Hongshi hydrothermal vein copper deposit. In the Yudai copper–gold deposit, the quartz diorite porphyry is 452 ± 2.8 Ma [28], and the Re–Os test of molybdenum shows its mineralization age is 449 Ma. The age of the rhyolite porphyry in the Hongshi copper deposit is 439 Ma [29], the altered andesite is 441 Ma [30,31], and the granite is 449.5 Ma [26,27]. Late Carboniferous (320–300 Ma) mineralization typical deposits include the Meiling and Hongshan hydrothermal copper–gold deposits. Among them, the Meiling rhyolite porphyry is 317.4 Ma; quartz porphyry is 298.7 Ma, andesite is 313 Ma [32,33], while the Hongshan quartz diorite is 296.5 Ma [34].

3 Ore-controlling factors analysis

3.1 Interpretation of multi-source ore-forming information

3.1.1 Ore-bearing strata

The primary wall rocks in the study area are various intermediate-acidic rocks (andesite, dacite, rhyolite, etc.) and volcanic clastic rocks (tuff), which provide a portion of the source material for mineralization (Figure 1b). The outcropping strata mainly include the Ordovician Huangcaopo Group Daliugou Formation, the Devonian Dananhu Formation, the Carboniferous Qishan Formation, the Middle Permian Karagang Formation, and the Sanyigou Group. The various types of deposits mainly occur in the Middle Ordovician Huangcaopo Group Daliugou Formation and the Lower Devonian Dananhu Formation, with representative deposits mainly consisting of the shallow, low-temperature type copper–zinc–gold–silver mines of Hongshan and Meiling, the porphyry copper–gold deposit at Yudai, and the VMS-type loess slope and Honghai copper mines. Accordingly, these two stratum units can be considered favorable mineralization evidence layers (predictive variables). The mineral distribution map in the Kalatag Ore Cluster Area also indicates that various types of copper polymetallic deposits in the study area all developed in the Daliugou Formation and the Dananhu Formation (Figure 2).

Figure 2

Distribution map of the number of deposits in different strata of Kalatag area.

3.1.2 Ore-controlling faults

The Kalatag Ore Cluster Area is highly developed with faults, displaying prominent ore-controlling features. Various metallic ore deposits are distributed near both sides of the faults. The grid-like fault structure pattern in the area, formed by the northwest–northwest to west–northwest trending main faults and the north–northeast trending secondary faults, has a closer relationship with mineralization (Figure 1b). From west to east, a continuous distribution of various types of copper polymetallic deposits, such as Xierqu, Yueyawan, Yudai, Hongshan, Meiling, Hongshi, Honghai, Dongerqu, and Shaerhu, all develop on both sides of the north–northwest main faults of Kalatag. It is evident that the north–northwest trending Kalatag faults has a significant ore-controlling role and is the main ore-guiding structure in the region. The near north–northeast trending secondary faults control the occurrence of ore bodies and are critical ore-bearing structures in the area. Among these, the porphyry copper mine known as Yudai is influenced by faults trending predominantly in the north–east and also in an almost east–west direction. Hongshan copper–gold the north–northwest faults control the mine; the northwest controls the Hongshi copper mine and nearly north–south tensional and tensional–torsional faults; Meiling mining area is controlled by the northwest, north–south, and north–northwest feathered faults; nearly northwest and northeast faults mainly control Shaer Lake copper mine. The epithermal mineralization is controlled by northwest, north–northwest, northeast, and nearly east–west, while porphyry is controlled by northwest and nearly east–west faults. Through ArcGIS analysis of the fault-controlled ore buffer zone, combined with overlay processing of the locations of existing significant copper types polymetallic deposits, the optimal buffer zone radius for fault-controlled ore is 1.2 km (Figure 3a). All nine large- and medium-sized copper polymetallic deposits in this region fall within and nearby the 1.2 km fault-controlled ore buffer zone, demonstrating the close spatial distribution relationship between the copper polymetallic ore deposits and faults.

Figure 3

Relationship between the buffer zones (granite bodies and fault) and mineral distribution (a) shows the relationship between buffer zones of granite bodies and mineral distribution, (b) illustrates the relationship between buffer zones of fault and mineral distribution).

3.1.3 Magmatic rocks

The Kalatag Ore Cluster Area is characterized by vigorous magmatic activity, not only extensive in range but also enduring over a long duration, which provides a constant heat source and mineralizing materials for ore formation (Figures 1 and 3b). Within the area, the Ordovician and Silurian diorites, gabbros, granodiorites, diorite porphyry, granite porphyry, and other granites, as well as a small amount of late Carboniferous quartz diorite and rhyolite porphyry and other intermediate-acidic intrusions, all have close relationships with mineralization. Among these, the porphyry-type mineralization is predominantly copper–gold mineralization, epithermal mineralization includes zinc–silver mineralization, and VMS type is accompanied by zinc mineralization. However, they are dominated mainly by copper mineralization, demonstrating that magmatic activity in the region is a prerequisite for the multi-metallic mineralization of copper and other metals in this area.

The contact zone between intrusive bodies and strata is often favorable for mineralization. We performed a buffer zone analysis using the line data of the contact zone between the intrusive body and the striatum. Depending on different buffer zone sizes, we determined that the optimal buffer zone size for the contact zone between the intrusive body and the stratum is 700 m. At the same time, the distribution of mineral resources shows that 95% of known deposits are developed within or at the edge of the 700 m buffer zone of the contact area between the intrusive body and the stratum (Figure 3a), indicating a close spatial connection between them.

3.2 Extraction of aeromagnetic and bouguer gravity anomaly information

Gravity and magnetic anomalies form the foundation for interpreting geological structures. The gradient zones of these anomalies, transitional zones of high and low anomaly variations, often reflect faults or contact zones between rocks and formations closely related to mineral exploration. Among them, gravity information at medium scales primarily reveals regional tectonic structures, while aeromagnetic information at large scales reflect more detailed local features. Therefore, combining both is particularly advantageous for locating multi-metal deposits such as copper, facilitating ore prediction at both macro and micro scales.

This study extracted the gravity and magnetic anomaly information from 1:200,000 Bouguer gravity and aeromagnetic data. Kriging interpolation analysis was conducted using ArcGIS to depict the distribution of Bouguer gravity and aeromagnetic anomalies finely. Furthermore, by considering the positions of known mineral deposits from various categories, it was found that the Bouguer gravity anomaly range (−135 to −126 × 10^–5 m/s²) and the aeromagnetic anomaly range (−20 to 345 nT) both fully cover the deposits above. This indicates that the gravity above and magnetic anomaly information can be used as predictive factors for further research on mineralization (Figure 4).

Figure 4

Relationship between aeromagnetic,Bouguer gravity anomalies, and mineraldistribution (A) shows the aeromagnetic anomalies and mineral distribution; (B) presents the Bouguer gravity anomalies and mineral distribution).

4 Establishment of the evidence weighting method and prediction model

4.1 Introduction to the evidence weighting method

Based on the weighted overlay and integration of multi-source geoscientific data relevant to mineralization, Agterberg, a Canadian mathematical geologist, proposed a novel geostatistical method known as the evidence-weighting method aimed at mineralization prediction. The evidence-weighted model represents each ore-forming factor (evidence layer) using a binary variable, with 1 denoting the presence of evidence and 0 indicating its absence. It then examines the conditional independence between different pairs of evidence. Moreover, a pair of weight coefficients is calculated for each piece of evidence. Finally, the evidence layers are statistically integrated to compute the posterior probability of ore formation. The fundamental mathematical principles can be summarized as follows: the study area is divided into T partitions, and for n evidence factors, when all evidence factors satisfy the assumption of independence, the probability of a specific k unit within the area being a mineralization point is represented by the posterior probability O. This is obtained based on the following formula:

O = exp ln D 1 − D + ∑ j = 1 n W j k

where j represents the number of evidence factors, ranging from 1 to n; W ^k _j denotes the weight of the ith evidence factor; and D represents the number of mineral resources.

In the event of missing data, the weight value is zero. When the evidence factor is present or absent, the weight values are defined as follows:

W + = ln { P ( B / D ) / P ( B / D ¯ } W − = ln { P ( B ¯ / D ) / P ( B ¯ / D ¯ }

where B denotes a unit where certain mineralization information is present and signifies the number of units without minerals and P represents the probability of a mining area’s existence.

The application process of the weight-of-evidence method can be roughly divided into four steps:

Determining the evidence layers: establishing a connection between minerals and their controlling elements.
Obtaining weight value 1: using the ArcSDM software to determine the weight values (W ⁺ and W ⁻) for each evidence layer and applying a binary conversion process.
Obtaining weight value 2: the optimal evidence layer combinations are identified for a second weight value calculation by employing a conditional independence test.
Conducting mineralization prediction: a mineralization prediction map is produced based on the posterior probability results of each prediction unit in the weight-of-evidence model.

4.2 Variable selection and mineralization model construction

The prediction and evaluation of mineral resources based on GIS technology first require extracting each ore-controlling evidence layer, establishing a connection between prospecting information categories and prospecting information characteristics, and constructing a scientific and feasible regional prospecting model. Based on the above analysis and fusion of multi-source geoscience information, a total of five predictive variables (evidence layers) closely related to copper polymetallic mineralization were extracted from the Kalatag mining area, namely the ore-bearing strata such as the Daliugou Group and the Dananhu Group, the 1.2 km buffer zone of faults, the 700 m buffer of intrusions, Bouguer gravity anomaly, and aeromagnetic anomaly. The evidence weight values of each predictive variable are obtained through the ArcSDM software, and finally, the probability of mineralization for each unit in the work area is calculated, and a copper polymetallic prospecting model is constructed (Table 1).

Table 1

Prospecting model of Kalatag Ore mining district

Information category for finding ore	Information feature for finding ore
Ore-bearing strata	Daliugou Formation of Middle Ordovician Huangcaopo Group and Dananhu Formation of Lower Devonian Series
700 m buffer of intrusive body	The Ordovician and Silurian diorite, gabbro, granodiorite, diorite porphyry, granite porphyry and other granite and other medium acid rock bodies in the area
1.2 km buffer of faults	NWW-WNW main faults and secondary NEE trending faults
Buer Gravity	Abnormal interval (−126 to −135 × 10⁻⁵m/s²)
Navigation	Abnormal interval (−28 to 320 nt)

Using the nine known copper-polymetallic deposits in the Kalatag mining area as model units, the variable selection is conducted using the maximum C-value method. The excellent weight value is denoted as W ⁺, the negative weight value is denoted as W ⁻, and the sum of the positive and negative weight values’ absolute values is represented by C. Therefore, the absolute value of C represents the strength of the correlation between the deposit (point) production status and each predictive variable. If the absolute value of C is smaller, it reflects a worse prospecting indication. Conversely, if the absolute value of C is more significant, it indicates a better prospecting indication. In addition, if the absolute value of C is more significant than, equal to, or less than zero, it respectively represents that the aforementioned predictive variable is more favorable, unfavorable, or has no indicative relationship with mineralization. The ranking of C values in the Kalatag mining area from high to low is as follows: 1.2 km buffer zone of faults, Bouguer gravity anomaly, aeromagnetic anomaly, 700 m buffer zone of intrusion, and ore-bearing strata. It can be seen that the faults have the most excellent control over the copper polymetallic mineralization in the Kalatag area, and later exploration should focus on the area near the faults. Other detailed evidence weight data are seen in Table 2.

Table 2

Evidence-weight values of each evidence factor in the Kalatag mining area

Number	Evidence factor (variable)	W ⁺	W ⁻	C	Sorted by C value
1	Ore-bearing strata	2.075654516	−2.182757264	4.258411781	5
2	700 m buffer of intrusive body	2.283556452	−2.206372633	4.489929085	4
3	1.2 km buffer of faults	1.395441529	−6.623433007	8.018874536	1
4	Buer Gravity	0.995338319	−6.446943225	7.442281544	2
5	Navigation	0.951531267	−6.420380646	7.371911914	3

5 Target area delimitation and evaluation

The study area for prediction evaluation is 40 km × 40 km. In this article, the size of the unit chosen for mineral prediction is determined based on the scale of the map [36,37] We adopted a grid unit size of 250 m × 250 m, with a total of 26,040 grid units across the entire area. Posterior probability calculations were performed using the ArcSDM software, yielding a contour map of posterior probabilities. The range of posterior probability values lies between 0 and 1. A higher value indicates a greater likelihood of the presence of a mineral deposit, while a lower value indicates a correspondingly lower probability.

Based on the above analysis and the contour map of the posterior probabilities, we select areas with posterior probability values >0.059 as B-class or above favorable mineralization zones, eventually delineating 4 A-class target areas and 3 B-class target areas (Figure 5). Then, we compare the currently discovered mineral resources in the area with the prediction results, analyze the mineralization potential of each target area, and finally conduct field engineering verification to confirm the accuracy of the model prediction. The results show that the delineated target areas cover all known deposits, demonstrating good reliability of the mineralization prediction. In addition, the target areas are controlled by the northwest–southwest to west–northwest main faults and the northeast-oriented secondary faults. The copper polymetallic deposits in the area are all distributed in the region controlled by the above structural strata. The situations of the A- and B-class predicted target areas in the Kalatag mining area are described as follows:

Figure 5

Distribution map of copper and polymetallic ore prospecting targets.

Four A-class target areas: the posterior probability values are high, mainly distributed around the deep and peripheral areas of existing deposits, which can be considered as the expansion of existing ore bodies. They are the best areas urgently needing exploration and development, with significant potential for copper and other polymetallic mineral resources. With a certain amount of exploration work, the probability of discovering a batch of new deposits is high, and the risk of exploration investment is small. It is suggested to conduct a small amount of deep drilling exploration on the smallest predicted area of A-class, following the principle of structure-controlling ore, and strengthen the control of known deep ore veins.

Six B-class target areas: compared with A-class predicted areas, their geological exploration work is relatively low, but the posterior probability is high, and some mineralized points or some prospecting clues have been distributed. In the predicted area, the exploration of copper polymetallic deposits should be actively carried out.

In the predicted area, the exploration of copper polymetallic deposits should be actively carried out. To achieve this, it is advisable to strengthen the large-scale geochemical profile through systematic sampling and analysis of soil, rock, and water samples. Additionally, conducting trench exploration, which involves the excavation of trenches to expose subsurface mineralization, can provide valuable insights into the deposit’s characteristics and potential. Moreover, a small amount of drilling is recommended to gather more detailed information about the deposit’s depth, extent, and grade. Drilling core samples can be analyzed to determine the presence of economically viable mineralization and to assess the deposit’s overall quality. By combining the results of geochemical profiling, trench exploration, and limited drilling, a comprehensive understanding of the copper polymetallic deposit can be achieved. This integrated approach will facilitate accurate resource estimation and inform future mining plans, ensuring efficient and sustainable extraction of the mineral resources in the predicted area.

6 Conclusions

The exploration and prediction of metallogenic deposits are paramount, particularly in areas like the Kalatag mining region. Integrating traditional geological insights with modern geospatial technologies is essential for this endeavor. Our research has not only successfully merged these two realms but has also introduced an innovative approach to mineral exploration in the region.

Prospecting model establishment: in our study, we have crafted a comprehensive prospecting model tailored for the hydrothermal copper polymetallic deposits in the Kalatag mining area. This model amalgamates five predictive elements: strata, intrusion, faults, Bouguer gravity anomaly, and aeromagnetic anomaly. Among these, the faults stands out as the primary ore-controlling factor, highlighting the synergy between geophysical data and geological comprehension.
Prospecting prospects and applicability: the practical implications of our findings are profound. We have delineated four A-class and three B-class prospective areas, offering a clear trajectory for subsequent exploration ventures. The congruence of these areas with existing mining zones reinforces the efficacy of our model and hints at the region’s latent mineralization potential. Such insights elevate the scientific merit of our study and offer tangible benefits for mining stakeholders.
Recommendations and future research: our findings underscore the faults’ crucial role in ore control. Consequently, we advocate for targeted deep drilling in A-class prospective areas, especially where faults activity is pronounced. For B-class zones, a comprehensive strategy encompassing geochemical sections, trench exploration, and drilling is recommended. These suggestions stem from our in-depth analysis, aiming to optimize exploration outcomes.
Limitations and future directions: despite our research’s significant contributions to mineral exploration in the Kalatag area, it is not without limitations. Geological processes are inherently dynamic, necessitating periodic updates to models. Upcoming research could explore the integration of even more diverse datasets, adapting to technological advancements. Embracing machine learning and AI could potentially refine prediction precision.
Scientific significance and broader implications: our research, by seamlessly merging varied datasets and methodologies, has carved a niche in mineral exploration paradigms. While our prospecting model is bespoke for the Kalatag region, its essence can be extrapolated to other mineral-abundant areas globally. The true value of our research transcends its findings; it is rooted in a holistic approach that melds knowledge, technological prowess, and pragmatic utility.

Funding information: This study was financially supported by the following funding sources: the Key Scientific Research Project in Higher Education Institutions of Henan Province (No. 24A170032), the Directional Research Bidding Project (No. 70120089), the Henan Province Federation of Social Sciences Joint Research Project (No. SKL-2023-2529), the Young Key Teacher Training Program of Zhengzhou Normal University (No. ZHJX-2022001221882), the Special funding support project for scientific research initiation of Zhengzhou Normal University (No. 702439), and the Student Research and Innovation Fund Project of Zhengzhou Normal University (No. DCY2024005).
Conflict of interest: The authors declare that they have no conflicts of interest.
Data availability statement: The data that support the findings of this study are available from the corresponding author Xi upon reasonable request.

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Received: 2023-08-25

Revised: 2023-10-21

Accepted: 2023-11-22

Published Online: 2023-12-15

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

https://doi.org/10.1515/geo-2022-0588

Schlagwörter für diesen Artikel

mineralization prediction; copper polymetallic deposits; Kalatag; evidence weighting model; ArcGIS; multi-source data fusion

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