Cu–Au exploration target generation in the eastern Carajás Mineral Province using random forest and multi-class index overlay mapping

Martins, Taís Ferreira; Seoane, José Carlos Sícoli; Tavares, Felipe Mattos

doi:10.1016/j.jsames.2022.103790

Cited by 11 publications

(16 citation statements)

References 70 publications

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“…It emphasizes the fusion of different sources of geoscience datasets such as geophysical data, geochemical data, remote sensing data, and geological data for polymetallic mineral prospecting. Numerous studies used geoscience information mutually or separately (e.g., geological, geophysical, geochemical, and remote sensing datasets) for polymetallic mineral prospectivity mapping [36,[49][50][51][52][53]. This investigation used remote sensing data to extract hydrothermal alteration zones, aeromagnetic data for identifying deep intrusive masses and hidden fault systems, and geological maps and filed data to show mineral occurrences and mine locations, faults and fractures, and potential host-rock units.…”

Section: Discussionmentioning

confidence: 99%

Fusion of Remote Sensing, Magnetometric, and Geological Data to Identify Polymetallic Mineral Potential Zones in Chakchak Region, Yazd, Iran

et al. 2022

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Exploration geologists are urged to develop new, robust, and low-cost approaches to identify high potential zones related to underground/unexplored mineral deposits because of increased depletion of ore deposits and high consumption of basic metal production industries. Fusing remote sensing, geophysical and geological data has great capability to provide a complete range of prerequisite data to accomplish this purpose. This investigation fuses remote sensing data, such as Sentinel-2 and Landsat 7, aerial magnetic geophysical data, and geological data for identifying polymetallic mineralization potential zones in the Chakchak region, Yazd province, Iran. Hydrothermal alteration mineral zones and surface and deep intrusive masses, hidden faults and lineaments, and lithological units were detected using remote sensing, aerial magnetic, and geological data, respectively. The exploratory/information layers were fused using fuzzy logic modeling and the multi-class index overlap method. Subsequently, mineral potential maps were generated for the study area. Some high potential zones of polymetallic mineralization were identified and verified through a detailed field campaign and drilling programs in the Chakchak region. In conclusion, the fusion of remote sensing, geophysical, and geological data using fuzzy logic modeling and the multi-class index overlap method is a robust, reliable, and low-cost approach for mining companies to explore the frontier areas with identical geologic conditions that are alleged to indicate polymetallic mineralization potential.

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Section: Discussionmentioning

confidence: 99%

Fusion of Remote Sensing, Magnetometric, and Geological Data to Identify Polymetallic Mineral Potential Zones in Chakchak Region, Yazd, Iran

et al. 2022

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“…Rainfall is one of the factors predisposing an area to geological hazards (Figure 2j) [46]. A large number of studies have shown that geological hazards mostly occur in the high-rainfall season [47], and Muli County is situated in southwest Sichuan, where rainfall is concentrated from May-October, and especially from June to August.…”

Section: (10) Average Annual Precipitationmentioning

confidence: 99%

Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Song

Chen

et al. 2023

Sustainability

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Landslides seriously threaten human life and property. The rapid and accurate prediction of landslide geological hazard susceptibility is the key to disaster prevention and mitigation. Traditional landslide susceptibility evaluation methods have disadvantages in terms of factor classification and subjective weight determination. Based on this, this paper uses a random forest model built using Python language to predict the landslide susceptibility of Muli County in western Sichuan and outputs the factor weight and model accuracy. The results show that (1) the three most important factors are elevation, distance from the road, and average annual rainfall, and the sum of their weights is 67.54%; (2) the model’s performance is good, with ACC = 99.43%, precision = 99.3%, recall = 99.48%, and F1 = 99.39%; (3) the landslide development and susceptibility zoning factors are basically the same. Therefore, this model can effectively and accurately evaluate regional landslide susceptibility. However, there are some limitations: (1) the landslide information statistical table is incomplete; (2) there are demanding requirements in terms of training concentration relating to the definition of landslide and non-landslide point sets, and the landslide range should be accurately delineated according to field surveys.

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“…Several recent research papers have explored the application of the random forest algorithm in mineral prediction using various data sources, including remote sensing imagery, spectral indices, and terrain variables [43][44][45]. These studies have demonstrated the effectiveness of random forest in accurately identifying and mapping lithological compositions, hydrothermal alterations, and geological features related to mineral deposits.…”

Section: Introductionmentioning

confidence: 99%

“…The recent advancements in random forest-based mineral prediction have also focused on addressing challenges encountered in traditional methods, such as overcoming the limitations of manual interpretation and reducing subjectivity in the analysis. By leveraging the power of machine learning and data-driven approaches, these studies have demonstrated the potential for improved mineral resource assessments and exploration strategies [43,44].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Au-Polymetallic Deposits Based on Spatial Multi-Layer Information Fusion by Random Forest Model in the Central Kunlun Area of Xinjiang, China

Zhang,

Ye,

Xie

et al. 2023

Minerals

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In recent years, there has been a growing emphasis on combining intelligent prospecting algorithms, such as random forest, with extensive geological and mineral data for the purpose of quantitatively predicting exploration geochemistry. This approach holds significant importance for enhancing the accuracy of target delineation. The central Kunlun area in Xinjiang possesses highly favorable ore-forming geological conditions, offering excellent prospects for mineral exploration. However, the depletion of shallow deposits coupled with a decade-long gap in geological exploration have presented increasing challenges in the quest to discover substantial metal resources. Consequently, there is now a severe shortage of reserve assets in the region, prompting an urgent need for the implementation of new theories, methods, and technologies in mineral resource investigation and evaluation efforts. The researchers used geological and regional geochemical data to construct a random forest metallogenic discriminant model for predicting the mineralization of gold polymetallic minerals in the central Kunlun area of Xinjiang and delineating the metallogenic target area. Two different sampling methods were compared to quantitatively predict gold polymetallic mineral resources. The results indicate that the selected training samples offer higher prediction accuracy and reliability by fully capturing the complex information of the original data. The random forest model using select training samples has valuable applications in metallogenic prospect prediction and potential division due to its ability to consider the actual exploration cost and identify small areas with high potential and a high proportion of ore. This study significantly improves prediction accuracy, reduces exploration risk, and expands the use of machine learning algorithms in mathematical geology in the central Kunlun area of Xinjiang.

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Cu–Au exploration target generation in the eastern Carajás Mineral Province using random forest and multi-class index overlay mapping

Cited by 11 publications

References 70 publications

Fusion of Remote Sensing, Magnetometric, and Geological Data to Identify Polymetallic Mineral Potential Zones in Chakchak Region, Yazd, Iran

Fusion of Remote Sensing, Magnetometric, and Geological Data to Identify Polymetallic Mineral Potential Zones in Chakchak Region, Yazd, Iran

Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Prediction of Au-Polymetallic Deposits Based on Spatial Multi-Layer Information Fusion by Random Forest Model in the Central Kunlun Area of Xinjiang, China

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