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NARROW
GeoRef Subject
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all geography including DSDP/ODP Sites and Legs
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A novel data-knowledge dual-driven model coupling artificial intelligence with a mineral systems approach for mineral prospectivity mapping
Deciphering differential exhumation in the Gangdese orogen in southern Tibet using exposed porphyry alteration systems and geomorphic analysis
Hadean tectonics: Insights from machine learning
Geographically weighted regression in mineral exploration: A new application to investigate mineralization
ABSTRACT Geographically weighted regression (GWR) is an effective model for the investigation of spatially nonstationary relations among variables in the geographical and social sciences. GWR was introduced to the field of mineral exploration to further understanding of the location, controlling factors, and coupling mechanisms related to the triggering of mineralization—in other words, the where, what, and how. Previous studies reported that Cu and Au in a porphyry system present a paragenetic relation at different stages of mineralization, which can be an informative indicator in mineral exploration. As a successor, the current study further applies the GWR model to characterize the paragenetic relation between the ore-forming elements Cu and Au in the Duolong mineral district of Tibet, China, in a spatial scenario. Unlike the spatially varied ore-forming mechanism quantified by the regression coefficients of GWR, the coefficient of determination ( R 2 ) is discussed to verify the existence and to evaluate the strength of the paragenetic relation between Cu and Au, because regression coefficients can only inform the mutual influence between one and the other. Furthermore, the fractal and multifractal-based spectrum–area method is adopted to separate the GWR results into anomaly and background. Areas with GWR results that indicate the existence and intensity of a paragenetic relation are mapped as target areas for mineral exploration. The current quantitative recognition of mineralization represents a meaningful and useful extension to the application and interpretation of the GWR model.
Advances in mathematical geophysics — Introduction
Analysis of geochemical patterns in a soil profile over mineralized bedrock
Seismic imaging of the Caosiyao giant porphyry molybdenum deposit using ambient noise tomography
A new international initiative for facilitating data-driven Earth science transformation
Abstract Data-driven techniques including machine-learning (ML) algorithms with big data are re-activating and re-empowering research in traditional disciplines for solving new problems. For geoscientists, however, what matters is what we do with the data rather than the amount of it. While recent monitoring data will help risk and resource assessment, the long-earth record is fundamental for understanding processes. Thus, how big data technologies can facilitate geoscience research is a fundamental question for most organizations and geoscientists. A quick answer is that big data technology may fundamentally change the direction of geoscience research. In view of the challenges faced by governments and professional organizations in contributing to the transformation of Earth science in the big data era, the International Union of Geological Sciences has established a new initiative: the IUGS-recognized Big Science Program. This paper elaborates on the main opportunities and benefits of utilizing data-driven approaches in geosciences and the challenges in facilitating data-driven earth science transformation. The main benefits may include transformation from human learning alone to integration of human learning and AI, including ML, as well as from known questions seeking answers to formulating as-yet unknown questions with unknown answers. The key challenges may be associated with intelligent acquisition of massive, heterogeneous data and automated comprehensive data discovery for complex Earth problem solving.