Method of predicting prospecting model based on machine learning

Abstract

The invention belongs to the field of geological prospecting technology, specifically discloses a method of predicting a prospecting model based on machine learning. The method establishes a unified and easy-to-distinguish prospecting concept model database; analyzes and summarizes existing domestic and foreign prospecting models and ore-controlling elements and data materials of a study area based in the prospecting concept model database by machine learning, and constructs a prospecting prediction model; after determines the ore-controlling elements in the prospecting prediction model, improves data foundation of the prospecting concept model according to a data material list provided about the range of the study area, and recommends an algorithm combination suitable for the ore-controlling elements according to algorithms summarized from a cube quantitative predicting system; finally, based on the prospecting concept prediction model, realizes a quantitative, positioned and probabilistic predictive evaluation. The invention can quickly establish the prospecting model of a study area, and the prospecting model is more comprehensive and objective and more in line with the actual situation.

Description

technical field [0001] The invention belongs to the technical field of geological exploration, in particular to a machine learning-based ore prospecting model prediction method. Background technique [0002] Models or models have been widely used in earth sciences, and are generally valued by the majority of geologists. American geologist Wheaton pointed out that the introduction of models is one of the three major achievements of geological science. Since the successful establishment of the porphyry deposit model, many models have come out one after another, such as petroleum oil generation model, geochemical zoning model, Carlin-type gold deposit model, powder rock mineralization model, etc. The establishment of metallogenic models and prospecting models has promoted the in-depth development of geological exploration and enriched the theory of mineralization of ore deposits. Major breakthroughs in geological understanding of metallogenic models often have an important im...

AI Technical Summary

Problems solved by technology

The expert system can realize the intelligentization of ore prospecting work to a certain extent, but the established model of the existing expert system is not comprehensive and objective, and the existing expert system model is limited, and the established system cannot be updated.

[0007] Summarizing the previous research results, the current theoretical method of modeling is still in the exploratory stage. The existing prospecting models are mainly established on the basis of analyzing the data of the research area, and the geologists are based on their own knowledge and experience. The ore prospecting model has certain subjectivity and limitations in understanding, and the ore prospecting models established by different geologists may be different

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