Construction of quantitative prediction model of metal mineral resources based on big data mining

Taiyu Fu

doi:10.1117/12.3023466

1 April 2024 Construction of quantitative prediction model of metal mineral resources based on big data mining

Taiyu Fu

Proceedings Volume 12987, Third International Conference on Computer Technology, Information Engineering, and Electron Materials (CTIEEM 2023); 129870H (2024) https://doi.org/10.1117/12.3023466
Event: Third International Conference on Computer Technology, Information Engineering, and Electron Materials (CTIEEM 2023), 2023, Zhengzhou, China

Abstract

With the vigorous development of big data technology, the application of data mining program to complete quantitative prediction and evaluation of mineral resources has become a new trend in the development of mineral exploration industry and an important way to promote the digital transformation of traditional prospecting technology. In this regard, this paper will combine geological theory analysis and data mining algorithm to build a quantitative prediction model of metal mineral resources, realize the analysis, processing and visual interpretation of deep geoscience data, and provide necessary data science support for actual mineral exploration. The prediction model takes the machine learning algorithm as the core, and relies on the class libraries such as Numpy, Pandas and Sklearn in Python environment to complete the development and training. Combined with the spatial attribute database in ArcGIS, the prediction elements and importance evaluation indicators are obtained, and the final quantitative prediction results are obtained, and the result evaluation is completed. Practice has proved that the quantitative prediction model of mineral resources based on machine learning algorithm has high prediction accuracy, and the visual results can clearly show the high probability mineral resources areas, which has high practical promotion value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Taiyu Fu "Construction of quantitative prediction model of metal mineral resources based on big data mining", Proc. SPIE 12987, Third International Conference on Computer Technology, Information Engineering, and Electron Materials (CTIEEM 2023), 129870H (1 April 2024); https://doi.org/10.1117/12.3023466

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Minerals

Mining

Data storage

Data acquisition

Data mining

Random forests

Show All Keywords

Keywords/Phrases

Search In:

Publication Years