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Lithology identification technology using BP neural network based on XRF


The element content obtained by X-ray fluorescence (XRF) mud-logging is mainly used to determine mineral content and identify lithology. This work has been developed to identify dolomite, granitic gneiss, granite, limestone, trachyte, and rhyolite from two wells in Nei Mongol of China using back propagation neural network (BPNN) model based on the element content of drill cuttings by XRF analysis. Neural network evaluation system was constructed for objective performance judgment based on Accuracy, Kappa, Recall and training speed, and BPNN for lithology identification was established and optimized by limiting the number of nodes in the hidden layer to a small range. Meanwhile, six basic elements that can be used for fuzzy identification were determined by cross plot and four sensitive elements were proposed based on the existing research, both of which were combined to establish sixteen test schemes. A large number of tests are performed to explore the best element combination, and the result of experiments indicate that the improved combination has obvious advantages in identification performance and training speed. The author’s pioneer work has contributed to the neural network evaluation system for lithology identification and the optimization of input elements based on BPNN.

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The author would like to thank Prof. Yingjie Ma from Chengdu University of Technology for providing mud-logging data. This study was supported by the National Natural Science Foundation of China (No. 41704171, No. 12075055), Defense Industrial Technology Development Program (No. JCKY2018401C001), Natural Science Foundation of Jiangxi Province (No. 20192BAB202009).

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Correspondence to Xiongjie Zhang.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Communicated by Prof. Jadwiga Jarzyna (ASSOCIATE EDITOR) / Prof. Michał Malinowski (CO-EDITOR-IN-CHIEF).

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Wang, Q., Zhang, X., Tang, B. et al. Lithology identification technology using BP neural network based on XRF. Acta Geophys. (2021).

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  • Lithology identification
  • BP neural network
  • XRF
  • Cross plot