TY - JOUR
T1 - Quantitative provenance analysis through deep learning of rare earth element geochemistry
T2 - A case from the Liuling Group of the East Qinling Orogen, Central China
AU - Zhang, Zhenkai
AU - Yang, Na
AU - Hong, Zenglin
AU - Yang, Jianhua
AU - Du, Biao
AU - Zhao, Duanchang
AU - Chen, Ning
AU - Zhou, Tengfei
N1 - Publisher Copyright:
Copyright © 2022 Zhang, Yang, Hong, Yang, Du, Zhao, Chen and Zhou.
PY - 2022/9/16
Y1 - 2022/9/16
N2 - 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-macro-average, 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.
AB - 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-macro-average, 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.
KW - big geodata
KW - deep learning
KW - Liuling Group
KW - provenance analysis
KW - rare earth elements
UR - http://www.scopus.com/inward/record.url?scp=85139218882&partnerID=8YFLogxK
U2 - 10.3389/feart.2022.1001528
DO - 10.3389/feart.2022.1001528
M3 - 文章
AN - SCOPUS:85139218882
SN - 2296-6463
VL - 10
JO - Frontiers in Earth Science
JF - Frontiers in Earth Science
M1 - 1001528
ER -