Mineral exploration and regional surface geochemical datasets: An anomaly detection and k-means clustering exercise applied on laterite in Western Australia

Gonçalves, Mário A.; da Silva, Diogo Rasteiro; Duuring, Paul; Gonzalez-Alvarez, Ignacio; Ibrahimi, Tania

doi:10.1016/j.gexplo.2024.107400

Cited by 5 publications

(1 citation statement)

References 68 publications

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“…It is easy to describe, simple, efficient, and suitable for handling large data sets. As a result, it is widely used in various risk assessments. − The calculation of centroids in clustering algorithms provides a basis for risk factor classification. Clustering algorithms determine the similarity of data points based on the distances between them.…”

Section: Methodsmentioning

confidence: 99%

Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data

Wang,

Li,

Zhu

et al. 2024

ACS Omega

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Pipeline risk assessment is crucial for pipeline safety management and operation. The aim of this study is to develop a comprehensive assessment model that accurately evaluates pipeline risks and ensures the safe and reliable operation of the pipeline system. The model is based on multisource spatial data and is primarily applicable to long-distance oil and gas pipelines that traverse complex geological conditions in mountainous areas. The research is conducted using the example of the Jinliwen natural gas pipeline in Zhejiang Province, China. By analyzing the geological data of the study area and the potential risks that the pipeline may encounter, a comprehensive risk assessment indicator system for the pipeline was developed using slope units to divide pipeline sections. The pipeline risk levels are classified using the K-means clustering-entropy weighted-random forest algorithm. The model is evaluated using accuracy (Acc), precision (Pre), recall (R), F1-score, and the ROC curve. The results show that the model has an accuracy of 0.917, a precision of 0.92, a recall of 0.916, an F1-score of 0.914, and an AUC (Area Under Curve) of 0.93, indicating its strong predictive capability. The risk assessment results demonstrate a strong consistency when compared with actual incident events. This indicates that the constructed model effectively reflects the influencing factors of pipeline risk, providing a basis for pipeline risk assessment and disaster prevention and mitigation efforts in similar regions.

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Section: Methodsmentioning

confidence: 99%

Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data

Wang,

Li,

Zhu

et al. 2024

ACS Omega

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Dataset formation for sem-based images of commercial minerals using ml algorithms: case study for zircon in the Egyptian mountains

khalil,

El-Fishawy,

Ali

et al. 2024

Multimed Tools Appl

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Mineral identification holds paramount importance in geological and mineralogical endeavors, encompassing exploration, mining, and mineral processing. This work underscores the time-consuming and equipment-dependent nature of conventional identification methods, advocating for the integration of artificial intelligence techniques, particularly machine learning and computer vision. Commercial minerals, including zircon, are identified as linchpins of various industries, particularly ceramics and dentistry. The work elaborates on the pivotal role of SEM imaging techniques in discerning economic minerals in granitic rocks and pegmatite, emphasizing their utility in environmental science and mineral exploration. A novel computational approach is introduced, offering automation of mineral grain recognition, thereby mitigating the laborious and resource-intensive aspect of the process. The subsequent discussion pertains to the creation of a specialized SEM image dataset focusing on Egyptian commercial minerals, commencing with zircon, a dataset with foreseeable extensions. The authors anticipate that this dataset will significantly contribute to mineralogical research, facilitating precise mineral identification through AI techniques and enriching insights into Egypt’s geological wealth.

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Analyzing rare earth mine distributions in mainland China: a machine learning approach with k-means clustering and SVM

Yang

2024

Earth Sci Inform

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Mineral exploration and regional surface geochemical datasets: An anomaly detection and k-means clustering exercise applied on laterite in Western Australia

Cited by 5 publications

References 68 publications

Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data

Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data

Dataset formation for sem-based images of commercial minerals using ml algorithms: case study for zircon in the Egyptian mountains

Analyzing rare earth mine distributions in mainland China: a machine learning approach with k-means clustering and SVM

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