Quantitative Remote Sensing of Metallic Elements for the Qishitan Gold Polymetallic Mining Area, NW China

Cheng, Gong; Hui-kun, Huang; Li, Huan; Deng, Xiaojun; Khan, Rehan; Tamehe, Landry Soh; Atta, Asad; Lang, Xuechong; Guo, Xiaodong

doi:10.3390/rs13132519

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…The optimal index method analyzes all RGB color combinations based on the total variance and correlation coefficients of multispectral remote-sensing data. Currently, the most widely used method for selecting the best bands is the optimal index factor (OIF) method, which comprehensively considers the information content of single-band images and the correlation between different bands [65,66]. It is closer to the basic principle of band selection, and the calculation is simple and easy to implement [62,67].…”

Section: Optimal Index Analysismentioning

confidence: 99%

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Yang,

Zheng,

Chen

et al. 2023

Applied Sciences

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Chromite is a strategic mineral resource for many countries, and chromite deposit occurrences are widespread in the ultramafic rocks of the Yarlung Zangbo ophiolite belt, particularly in the harzburgite unit of the mantle section. Conducting field surveys in complex and poorly accessible terrain is challenging, expensive, and time-consuming. Remote sensing is an advanced method of achieving modern geological work and is a powerful technical means of geological research and mineral exploration. In order to delineate outcrops of chromite-bearing mantle peridotite, the present research study integrates seven image-enhancement techniques, including optimal band combination, decorrelation stretching, band ratio, independent component analysis, principal component analysis, minimum noise fraction, and false color composite, for the interpretation of Landsat8 OLI and WorldView-2 satellite data. This integrated approach allows the effective discrimination of chromite-containing peridotite outcrops in the Luobusa area, Tibet. The interpretation results derived from these integrated image-processing techniques were systematically verified in the field and formed the basis of the feature selection process of different lithologies supported by the support vector machine algorithm. Furthermore, the distribution range of the ferric contamination anomaly is detected through the de-interference abnormal principal component thresholding technique, which shows a high spatial matching relationship with mantle peridotite. This is the first study to utilize Landsat8 OLI and WorldView-2 remote sensing satellite data to explore the largest chromite deposit in China, which enriches the research methods for the chromite deposits in the Luobusa area. Accordingly, the results of this investigation indicate that the integration of information extracted from image-processing algorithms using remote sensing data could be a broadly applicable tool for prospecting chromite ore deposits associated with ophiolitic complexes in mountainous and inaccessible regions such as Tibet’s ophiolitic zones.

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Section: Optimal Index Analysismentioning

confidence: 99%

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Yang,

Zheng,

Chen

et al. 2023

Applied Sciences

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“…With the continuous development of remote sensing technology, the spatial resolution and spectral resolution of remote sensing images are improved, which makes it possible to analyze geochemical anomaly characteristics by using the remote sensing method [1][2]. Researchers have used laboratory spectra to effectively invert the contents of N, P, K and other elements and heavy metal elements in soil [3][4][5] and others have used ground reflection spectra for geochemical prospecting [6][7][8]. They carry out inversion and prediction of surface element content using an empirical model, which is established with surface sampling geochemical analysis data and remote sensing pixel reflection spectrum by regression or machine learning.…”

Section: Introductionmentioning

confidence: 99%

Research on Remote Sensing Geochemical Modeling Based on Surface to Surface Model

Cheng,

Li,

Tang

et al. 2023

J. Phys.: Conf. Ser.

Self Cite

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Remote sensing geochemistry is a simple, fast and economical advanced prospecting method, which carries out inversion and prediction of surface element content using the empirical model by regression or machine learning. The key problem faced by quantitative remote sensing is the low inversion accuracy of the model due to the mismatch of “point surface” information. How to overcome this problem? This paper proposes a “surface to surface” modeling method, which converts point data into surface data through Kriging interpolation to solve this problem. This paper uses geochemical interpolation data of Cu elements at different scales in the Qishitan gold mine area, Xinjiang, and ASTER remote sensing data to conduct geochemical modeling. In order to test the effect of Kriging on decreasing the scale effect, five sets of experiments were designed for comparison. The first four sets of sample data were interpolated according to different cell sizes, and the last set of data was not interpolated. The results show that the Kriging interpolation based on the ground resolution of the remote sensing image can effectively improve the accuracy of the remote sensing quantitative inversion model. When the square interpolation is close to the ground resolution of the used remote sensing data, the modeling accuracy gets the best value. This paper provides a new idea for improving the accuracy of remote sensing geochemical modeling.

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Metallogenic models as the key to successful exploration — a review and trends

Pohl

2022

Miner Econ

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Metallogeny is the science of ore and mineral deposit formation in geological space and time. Metallogeny is interdisciplinary by nature, comprising elements of natural science disciplines such as planetology to solid state physics and chemistry, and volcanology. It is the experimental forefront of research and bold thinking, based on an ever-growing foundation of solid knowledge. Therefore, metallogeny is not a closed system of knowledge but a fast-growing assemblage of structured and unstructured information in perpetual flux. This paper intends to review its current state and trends. The latter may introduce speculation and fuzziness. Metallogeny has existed for over 100 years as a branch of Earth Science. From the discovery of plate tectonics (ca. 1950) to the end of the last century, metallogeny passed through a worldwide phase of formally published ‘metallogenetic’ maps. In the last decades, a rapidly growing number of scientists, digitization and splendid new tools fundamentally boosted research. More innovations may be expected by the growing use of an evolving systematic ‘Geodata Science’ for metallogenic research by an increasingly global human talent pool. Future requirements for metallic and mineral raw materials, especially the critical natural elements and compounds that are needed for the nascent carbon-free economy, already drive activities on stock markets and in the resource industry. State geological surveys, academia and private companies embrace the challenges. The new age requires intensified metallogenic backing. In this paper, principles of metallogeny are recalled concerning concepts and terms. A metallogenic classification of ore and mineral deposits is proposed, and the intimate relations of metallogenesis with geodynamics are sketched (ancient lid tectonics and modern plate tectonics). Metallogenic models assemble a great diversity of data that allow an ever better understanding of ore formation, foremost by illuminating the geological source-to-trap migration of ore metals, the petrogenetic and geodynamic–tectonic setting, the spatial architecture of ore deposits and the nature and precise timing of involved processes. Applied metallogeny allows companies to choose strategy and tactics for exploration investment and for planning the work. Based on comprehensive metallogenic knowledge, mineral system analysis (MSA) selects those elements of complex metallogenic models, which are detectable and can guide exploration in order to support applications such as mineral prospectivity mapping, mineral potential evaluation and targeting of detailed investigations. MSA founded on metallogenic models can be applied across whole continents, or at the scale of regional greenfield search, or in brownfields at district to camp scale. By delivering the fundamental keys for MSA, supported by unceasing innovative research, the stream of new metallogenic insights is essential for improving endowment estimates and for successful exploration.

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Quantitative Remote Sensing of Metallic Elements for the Qishitan Gold Polymetallic Mining Area, NW China

Cited by 8 publications

References 33 publications

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Chromite-Bearing Peridotite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Luobusa Area, Tibet, China

Research on Remote Sensing Geochemical Modeling Based on Surface to Surface Model

Metallogenic models as the key to successful exploration — a review and trends

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