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

Active Publication Date: 2022-05-13
CHINA UNIV OF GEOSCIENCES (WUHAN)
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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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  • Multivariate geochemical anomaly identification method based on graph attention self-encoding
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  • Multivariate geochemical anomaly identification method based on graph attention self-encoding

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[0019] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation manners of the present invention will now be described in detail with reference to the accompanying drawings.

[0020] Please refer to figure 1 , the multivariate geochemical anomaly identification method based on graph attention self-encoding of the present invention comprises the following steps:

[0021] S1. According to the geological and ore-forming environment of the study area, select the geochemical element concentration data relevant to the detection of mineral anomalies and normalize them. The final processing results are as follows figure 2 ; The present embodiment is Fe example with the detection of mineral resources, and the geochemical element relevant to it is Cu-Zn-Mn-Pb-Fe 2 o 3 ;

[0022] S2. Use the Moran'I index to qualitatively measure the spatial correlation and aggregation of geochemical elements, and s...

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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.

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...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16C20/90G16C20/70G06F16/29G06K9/62G06N3/08
CPCG16C20/90G16C20/70G06F16/29G06N3/08G06F18/24147
Inventor 关庆锋任书良姚尧
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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