Supporting the discovery of new resources to maintain the continuous development of human society is a long-term goal in mineral exploration. Geochemical data as an important geo-information carrier played significant roles in geographical information system (GIS)-based mineral prospectivity. Among the various analytical methodologies, local singularity analysis has been broadly discussed due to its high efficiency in the characterization of non-linear mineralization. Focusing on this topic, the current study first reviews local singularity analysis from its original proposition to recent progresses. Meanwhile, limitations in square and rectangular window-based singularity index α estimation algorithms are discussed. Based on this, analytical window parameters in U-statistics and algorithms to quantify the power-law relationship between the physical quantity and the measuring scale in local singularity are integrated. At each scale, optical elliptical windows along all azimuth angles are determined and further merged, which defines an irregular analytical window. Irregular windows at all scales are further applied to local singularity analysis that can consequently propose an irregular window-based singularity algorithm. Practical application in the Duolong mineral district, northern Tibet, China demonstrates well the comparable efficiency of weak anomaly identification with that of the original algorithm. More importantly, multiscale irregular windows can supplement delicate patterns to characterize anisotropic natures associated with mineralization.

Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis

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