Application of Supervised Classification and CrostaTechnique for Lithological Discrimination in Parts of South Khetri Belt, Sikar District, Rajasthan

Muthamilselvan, A.

doi:10.1007/s12524-016-0589-y

Cited by 2 publications

(2 citation statements)

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“…Digital image classification by computers can greatly improve the objectivity of the image classification process. The the support vector machine algorithm (SVM) method is widely used in machine learning methods for geological applications, and it has proven to be robust and effective, especially in helping in the identification of rock units over the years [26][27][28]77,89].…”

Section: Machine Learning Classificationmentioning

confidence: 99%

“…Currently, geological mapping techniques have combined with machine learning, such as a support vector machine (SVM). Several geologists have made remote sensing data more effective in lithological and mineral mapping by employing advanced machine-learning algorithm techniques [26][27][28]. The spectral bands of the multispectral sensor are characteristic for absorptions of such minerals, and thus the bands of the sensor can be used to discriminate the ferric iron-rich weathered surfaces of harzburgites [29].…”

Section: Introductionmentioning

confidence: 99%

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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: Machine Learning Classificationmentioning

confidence: 99%

Section: Introductionmentioning

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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High Temperature Effects on Global Heritage Stone Resources: A Systematic Review

Lobarinhas,

Dionísio,

Paneiro

2024

Heritage

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Throughout history, natural stone has been a crucial building material due to its strength, durability, and aesthetic qualities. Today, it continues to be a valuable resource, representing both a cultural heritage asset and a significant economic material. However, the increasing frequency of heat waves and fires driven by climate change poses a growing threat to stone building materials. This paper reviews the scientific attention given to the effects of high temperatures on Global Heritage Stone Resources (GHSRs), an international classification designed to enhance the recognition and status of building stones. Through a systematic SCOPUS search with refined filtering criteria, the study aims to quantify the existing research on these heritage stones. The search applied the standardized lithotype terms from GHSR publications to ensure consistency, followed by the exclusion of irrelevant terms when identified. Additionally, a relevance filter was applied to restrict the number of articles per lithotype and ensure that only the most pertinent studies were considered. Key findings from the literature reveal that exposure to high temperatures (ranging from 200 °C to 900 °C) significantly affected the studied GHSRs, leading to thermal micro-fissuring, increased porosity, and changes in water absorption, which compromise the mechanical properties of the stones. Moreover, these conditions can result in irreversible chemical transformations, exacerbating the deterioration of cultural heritage assets. The study emphasizes the critical need for research to better understand how these stone materials behave when exposed to high temperatures. It also provides a relevant framework for future investigations aimed at predicting and mitigating the effects of external threats such as fires.

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Application of Supervised Classification and CrostaTechnique for Lithological Discrimination in Parts of South Khetri Belt, Sikar District, Rajasthan

Cited by 2 publications

References 5 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

High Temperature Effects on Global Heritage Stone Resources: A Systematic Review

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