Construction and prediction of a prospecting model based on recurrent neural network

ZHANG Yaguang; CHEN Jianping; JIA Zhijie; LI Shi; LIU Suqing; ZHANG Zhiping; ZHANG Ye

2019 Vol. 38, No. 12

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Article navigation > Geological Bulletin of China > 2019 > 38(12): 2033-2042

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ZHANG Yaguang, CHEN Jianping, JIA Zhijie, LI Shi, LIU Suqing, ZHANG Zhiping, ZHANG Ye. Construction and prediction of a prospecting model based on recurrent neural network[J]. Geological Bulletin of China, 2019, 38(12): 2033-2042.

Citation:

ZHANG Yaguang, CHEN Jianping, JIA Zhijie, LI Shi, LIU Suqing, ZHANG Zhiping, ZHANG Ye. Construction and prediction of a prospecting model based on recurrent neural network[J]. Geological Bulletin of China, 2019, 38(12): 2033-2042.

Construction and prediction of a prospecting model based on recurrent neural network

1.
School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
2.
Beijing Key Laboratory of Development and Research for Land Resources Information, Beijing 100083, China
3.
National and Local Joint Engineering Research Center of Shale Gas Exploration and Development, Chongqing Geology and Mineral Research Institute, Chongqing 401120, China

More Information

Corresponding author: CHEN Jianping, 3s@cugb.edu.cn

Received Date: 17 April 2019

Revised Date: 25 July 2019

Publish Date: 15 December 2019

Abstract

Abstract

Under the background of big data and artificial intelligence and on the basis of the establishment and application basis of existing traditional geological prospecting model, this paper proposes a prospecting model construction and prediction method based on cyclic neural network, with the purpose of achieving in-depth analysis and understanding of geological data. According to the requirements for construction and prediction of geological prospecting model, the authors combined the data cleaning theory to systematically summarize and summarize the traditional geological prospecting model, thus establishing a geological prospecting knowledge base and providing training data for deep learning algorithms. The accuracy of the comparison results and the time used for classification were comprehensively analyzed. Finally, the RNN classification algorithm was selected to classify the conceptual model of prospecting. In the process of establishing the prospecting model of the study area, by using the key words and ore control elements to complete the model matching, the model was used to analyze the model matching results so as to realize the construction of the regional geological prospecting model and the prediction and analysis of the mineral resources. With the Dashui gold deposit as an example, the construction of the prospecting model was realized quickly and accurately, which effectively provides guidance for the prediction of mineral resources and verifies the feasibility of the method.
- prospecting model /
- RNN classification algorithm /
- ore control elements /
- model matching

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References

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