Lithology identification technology using BP neural network based on XRF

• Autorzy: Wang Qingshan , Zhang Xiongjie , Tang Bin , Ma Yingjie , Xing Jisheng , Liu Longfeng

Identyfikatory

DOI

10.1007/s11600-021-00665-8

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

BP neural network; XRF; cross-plots; lithology identification

PL

sieć neuronowa BP; XRF; wykresy krzyżowe

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2021
• Tom: Vol. 69, no. 6
• Strony: 2231--2240
• Opis fizyczny: Bibliogr. 22 poz.

Twórcy

autor

Wang Qingshan

• Engineering Research Center of Nuclear Technology Application (East China University of Technology), Ministry of Education, Nanchang 330013, China

autor

Zhang Xiongjie

• Engineering Research Center of Nuclear Technology Application (East China University of Technology), Ministry of Education, Nanchang 330013, China

autor

Tang Bin

• Engineering Research Center of Nuclear Technology Application (East China University of Technology), Ministry of Education, Nanchang 330013, China

autor

Ma Yingjie

• The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China

autor

Xing Jisheng

• Engineering Research Center of Nuclear Technology Application (East China University of Technology), Ministry of Education, Nanchang 330013, China

autor

Liu Longfeng

• Engineering Research Center of Nuclear Technology Application (East China University of Technology), Ministry of Education, Nanchang 330013, China

Bibliografia

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