Multivariate geochemical anomaly identification method based on graph attention self-encoding

A geochemical and anomaly identification technology, applied in the field of multi-geochemical exploration anomaly identification, can solve problems such as failure to take into account real sampling points and irregular metallogenic areas, inability to directly use sampling point data, etc. The effect of secondary error
CN112927767BActive Publication Date: 2022-05-13CHINA UNIV OF GEOSCIENCES (WUHAN)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA UNIV OF GEOSCIENCES (WUHAN)
Publication Date
2022-05-13

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Abstract

The invention discloses a multivariate geochemical anomaly recognition method based on graph attention self-encoding, comprising the following steps: determination of composition threshold K; construction of topological network of geochemical elements; learning of multivariate geochemical features; background reconstruction of multivariate geochemical elements; value calculation. The present invention introduces graph learning into geochemical anomaly detection, uses K nearest neighbors to construct a topological relationship graph of geochemical elements, and constructs and trains a graph attention self-encoder capable of simultaneously extracting element composition relationships and spatial structure features. Based on the trained graph attention self-encoder, the multivariate geochemical background is reconstructed, and the final outlier value of each sampling point is calculated. The present invention expands the existing neural network model, so that the model can directly process sampling point data and be applied to irregular areas, greatly improves the learning performance of the background of chemical exploration and the practicability of the model, and provides a practical solution for complex geological conditions. Reliable method for identifying anomalies in geochemical exploration.
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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...

Claims

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