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Uncertainty spatial data mining-based regional metallogenic prediction method

A spatial data and uncertainty technology, applied in the field of resource information, which can solve problems such as difficulty in processing geospatial data quickly and efficiently

Inactive Publication Date: 2010-06-16
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

However, with the increasing collection and acquisition of regional and global geospatial data, in the face of massive multi-source geospatial data, it is difficult for traditional regional mineralization prediction methods to quickly and effectively process massive geospatial data.

Method used

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  • Uncertainty spatial data mining-based regional metallogenic prediction method
  • Uncertainty spatial data mining-based regional metallogenic prediction method
  • Uncertainty spatial data mining-based regional metallogenic prediction method

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Embodiment Construction

[0047] In order to better understand the technical solution of the present invention, specific examples are provided below taking East Kunlun, Qinghai, an important metallogenic belt in western China, as an example.

[0048]First, digitize multi-source geological spatial data, including geological mineral data, geochemical data and mineral deposit data; then extract remote sensing mineralization information (this description uses LandSAT ETM data as an example); finally, use the uncertainty spatial data mining in this invention The algorithm extracts the metallogenic association rule information to guide the metallogenic prediction. The following results are the mining results of the metallogenic association rule of the iron deposit in the East Kunlun metallogenic belt in Qinghai (Table 1 below).

[0049] Table 1 Mining results and uncertainty evaluation of metallogenic association rules for iron deposits in the East Kunlun Metallogenic Belt, Qinghai

[0050]

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Abstract

The invention relates to an uncertainty spatial data mining-based regional metallogenic prediction method, and belongs to the technical field of resource information processing and application. The regional metallogenic prediction method makes full use of multi-source mass geological space data (basic geological mineral data, remote sensing prospecting data, geochemical data and mineral deposit data) and effectively extracts regional metallogenic associated information to perform rapid and effective regional metallogenic prediction based on an uncertainty spatial data mining algorithmic model. The regional metallogenic prediction method comprises three main steps of geometrically registering multi-source and multi-scale geological space data, extracting remote sensing mineralizing information and performing metallogenic prediction based on the uncertainty spatial data mining algorithmic model. The method can evaluate a regional metallogenic prospective area more rapidly and objectively.

Description

technical field [0001] The invention relates to a new regional metallogenic prediction method, which belongs to the technical field of resource information. Background technique [0002] The regional metallogenic prediction methods have experienced from the early statistical prediction of ore deposits, the mid-term "geological anomaly" prediction to the recent "triple" quantitative metallogenic prediction and the theory and method of multi-fractal nonlinear metallogenic prediction. These regional metallogenic prediction theories, technologies and methods are becoming more and more mature, and have played an important role in regional metallogenic prediction. However, with the increasing collection and acquisition of regional and global geospatial data, in the face of massive multi-source geospatial data, it is difficult for traditional regional mineralization prediction methods to process massive geospatial data quickly and effectively. The spatial data mining technology th...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G01V9/00
Inventor 何彬彬陈翠华
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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