Machine Learning Prediction of Quartz Forming‐Environments

Abstract

Quartz is an abundant and important mineral in the crust that occurs in a wide range of geological environments. Crystalline quartz records the geo-environmental evolutions, and because of its abundance, is a perfect pathfinder mineral to reveal changes of the physical-chemical conditions. As the main gangue mineral in magmatic, transitional magmatic-hydrothermal, and hydrothermal deposits, its trace element abundance and distribution has been increasingly utilized for genetic classifications and to reconstruct the physicochemical conditions during quartz formation (

Machine Learning Investigation of Clinopyroxene Compositions to Evaluate and Predict Mantle Metasomatism Worldwide

“…Whereas elemental ratios do not seem to capture the main drivers of the spatial distribution of metasomatism worldwide, relevant information may be obtained from large‐volume, high‐dimensional geochemical data. In recent years, machine learning (ML) approaches have been applied to mineralogy, petrology, and geochemistry datasets to provide new insights and identify trends and patterns that would otherwise be unobservable (e.g., Petrelli et al., 2020; Petrelli & Perugini, 2016; Thomson et al., 2021; Ueki et al., 2018; Valetich et al., 2021; Wang et al., 2021; Zhao et al., 2019), demonstrating their potential to quantify mantle metasomatism worldwide.…”

Water Recycling in the Deep Earth: Insights From Integrated μ‐XRF, THz‐TDS Spectroscopy, TG, and DCS of High‐Pressure Granulite

“…As the phase transformation of α‐β quartz occurs at a temperature range of ca. 574–890°C, quartz in the studied high‐pressure mafic granulite might have been stabilized at a temperature range of <850–1,000°C (Figure 8), indicating phase transformation and water release during the subduction dehydration process (Li & Chou, 2022; Mechie et al., 2004; Y. Wang et al., 2021; Wu et al., 2023). Therefore, quartz from the rutile‐bearing garnet amphibolite might contain only minor contents of water.…”

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

“…These are the problems that do not have existing accepted solutions, rely heavily on judgments of highly experienced experts, yet could lead to the most profound scientific insights if investigated properly. A few studies explore the promise of ML in multi‐disciplinary data integration for predicting drought behavior in the Colorado River Basin based on various Earth System Models (Talsma et al., 2022), for predicting sea surface variabilities in the South China Sea (Shao et al., 2021), for geothermal heat flow prediction from multiple geophysical and geological datasets (Lösing & Ebbing, 2021), for identifying volcano's transition from non‐eruptive to eruptive states (Manley et al., 2021), for understanding the geodynamic history using geochemical data (Jorgenson et al., 2022; X. Lin et al., 2022; X. Li & Zhang, 2022; Saha et al., 2021; Thomson et al., 2021; Y. Wang et al., 2021), and for characterizing geodetic signals by their sources (Hu et al., 2021). Albert (2022) uses an unsupervised deep NN structure to predict future atmospheric structures from past measurements to enable infrasound propagation modeling.…”