Multi-element geochemical anomaly identification method based on graph attention self-coding

Abstract

The invention discloses a multi-element geochemical anomaly identification method based on graph attention self-coding. The method comprises the following steps: determining a composition threshold value K; constructing a geochemical element topology network; learning the multi-element geochemical characteristics; reconstructing a multi-element geochemical element background; and calculating multiple abnormal values. According to the method, graph learning is introduced into geochemical exploration anomaly detection, a geochemical element topological relation graph is constructed by using K-nearest neighbor, and a graph attention auto-encoder capable of simultaneously extracting element composition relations and spatial structure features is constructed and trained. The multi-element geochemical background is reconstructed based on the trained graph attention auto-encoder, and an abnormal value of each final sampling point is obtained by calculating . According to the method, an existing neural network model is expanded, so that the model can directly process sampling point data and can be applied to irregular areas, the learning performance of a geochemical exploration background and the practicability of the model are greatly improved, and a practical and reliable geochemical exploration anomaly identification method is provided for complex geological conditions.

Description

technical field [0001] The invention relates to the field of multivariate geochemical exploration anomaly identification and the field of artificial intelligence application, in particular to a multivariate geochemical anomaly identification method based on graph attention self-encoding. Background technique [0002] The identification of multivariate geochemical anomalies is one of the important contents of mineral resources exploration, and its anomaly information helps geologists to judge potential mineral deposits. Geochemical anomalies have strong spatial heterogeneity, and the spatial heterogeneity of geochemical elements must be considered. Traditional anomaly identification methods such as fractal / multiple analysis, kriging method, and spatial factor analysis take into account the correlation of spatial neighbor samples, and are outstanding in the identification of geochemical anomalies. In recent years, with its good and automatic learning ability for complex spati...

AI Technical Summary

Problems solved by technology

[0003] Aiming at the problem that the existing convolutional self-encoder cannot directly use sampling point data and fails to take into account real sampling points and irregular ore-forming areas, the present invention provides a multivariate geochemical anomaly identification method based on graph attention self-encoding, Include the following steps:

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