A Test of the Hypothesis That Syn‐Collisional Felsic Magmatism Contributes to Continental Crustal Growth Via Deep Learning Modeling and Principal Component Analysis of Big Geochemical Datasets

Xin, Lin; Cicchella, Domenico; Hong, Jun; Meng, Ganggang

doi:10.1029/2021jb023002

Cited by 5 publications

(3 citation statements)

References 78 publications

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“…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.…”

Section: Highlightsmentioning

confidence: 99%

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

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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After decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Challenges remain, including the formidable task of how geoscience can keep pace with developments in ML while ensuring the scientific rigor that our field depends on, but with improvements in sensor technology and accelerating rates of data accumulation, the methods of ML seem poised to play an important role for the foreseeable future.

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Section: Highlightsmentioning

confidence: 99%

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

O’Malley

Beroza

et al. 2023

JGR Solid Earth

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“…Recently, several studies have demonstrated that the data-driven machine learning methods can be powerful tools for solving complex problems in mineralogy, petrology, and geochemistry (Petrelli and Perugini 2016;Chen et al 2021;Huang et al 2022;Lin et al 2022;Nathwani et al 2022;Qin et al 2022;Wang et al 2022;Zou et al 2022), and for the construction of thermobarometers (Petrelli et al 2020;Higgins et al 2022;Jorgenson et al 2022;Li and Zhang 2022), without having any a priori knowledge. These research advances suggest that the machine learning method has the potential to be used for calibrating a mineral chemical-based oxybarometer.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning oxybarometer developed using zircon trace-element chemistry and its applications

Zou,

Brzozowski,

Chen

et al. 2024

American Mineralogist

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Magmatic oxygen fugacity (fO2) is a fundamental property to understanding the long-term evolution of the Earth’s atmosphere and the formation of magmatic-hydrothermal mineral deposits. Classically, the magmatic fO2 is estimated using mineral chemistry, such as Fe-Ti oxides, zircon, and hornblende. These methods, however, are only valid within certain limits and/or require a significant amount of a priori knowledge. In this contribution, a new oxybarometer, constructed by data-driven machine learning algorithms using trace elements in zircon and their corresponding independent fO2 constraints, is provided. Seven different algorithms are initially trained and then validated on a data set that was never utilized in the training processes. Results suggest that the oxybarometer constructed by the extremely randomized trees model has the best performance, with the largest R2 value (0.91 ± 0.01), smallest RMSE (0.45 ± 0.03), and low propagated analytical error (~0.10 log units). Feature importance analysis demonstrates that U, Ti, Th, Ce, and Eu in zircon are the key trace elements that preserve fO2 information. This newly developed oxybarometer has been applied in diverse systems, including arc magmas and mid-ocean ridge basalts, fertile and barren porphyry systems, and global S-type detrital zircon, which provide fO2 constraints that are consistent with other independent methods, suggesting that it has wide applicability. To improve accessibility, the oxybarometer was developed into a software application aimed at enabling more consistent and reliable fO2 determinations in magmatic systems, promoting further research.

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“…With the rapid development of big geodata, many research topics and methods have been pursued to address particular issues associated with quantitative geology (Wu and Liu, 2019). Studies show that geoscientists are able to study and solve related geological problems by deep mining large geological datasets (Lin et al, 2022) Among all big data analysis techniques, machine learning (particularly deep learning) has been widely used in the geoscience community, and interesting predictive models have been built. By adopting machine learning, we have uncovered a new approach for interrogating geological big data (Luo and Zhang, 2019).…”

Section: Introductionmentioning

confidence: 99%

Quantitative provenance analysis through deep learning of rare earth element geochemistry: A case from the Liuling Group of the East Qinling Orogen, Central China

Zhang

Yang²,

Zhang³

et al. 2022

Front. Earth Sci.

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With the ever-growing availability of massive geo-data, deep learning has been widely applied to geoscientific questions such as sedimentary provenance analysis. However, randomly selected initial weights (and also biases) and possible loss of population diversity in traditional neural network learning remain problematic. To address this issue, in this study, we proposed a new deep neural network model by incorporating genetic algorithm (GA) and simulated annealing algorithm into the BP neural network, i.e., the GA-SA-BP model. We then applied this new model to rare earth element (REE) geochemical data of the Liuling Group of the East Qinling Orogen to investigate its provenance. Our results showed that among other deep learning algorithms, the new model presents the best performance with good measuring metrics (e.g., over 85% of accuracy, over 0.82 of F1-macroaverage, F1-micro-average, and Kappa coefficient, and smallest (<0.15) Hamming distance). Here, we interpreted in accordance with the classification results that the southern margin of the North China Craton and the South Qinling Orogen are likely two major sources of the Liuling Group, suggesting a bidirectional deposition route of sediments from the north and south. Therefore, we proposed a foreland basin environment as the likely tectonic setting for the Liuling Group, which is consistent with current geological understanding. Our observations suggested that the GA-SA-BP model (or improved deep learning models) coupled with REE geochemistry is capable of provenance analysis.

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A Test of the Hypothesis That Syn‐Collisional Felsic Magmatism Contributes to Continental Crustal Growth Via Deep Learning Modeling and Principal Component Analysis of Big Geochemical Datasets

Cited by 5 publications

References 78 publications

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

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

Machine-learning oxybarometer developed using zircon trace-element chemistry and its applications

Quantitative provenance analysis through deep learning of rare earth element geochemistry: A case from the Liuling Group of the East Qinling Orogen, Central China

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