Intelligent mineral geological survey method: Daqiao-Yawan area in Gansu Province as an example

MingLi, YANG; Liu, Xue; Ran, Xiangjin; XueJia, SANG; Yan, Qun; Dai, Jinhui

doi:10.18654/1000-0569/2021.12.16

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…The co-occurrence of "entity-relationship-entity" not only controls the flow of data in the network, but also reflects the mineability of implicit information (Figure 3). In this paper, we analyze the information flow between co-occurring nodes and edges of the deposit knowledge graph [11] to determine the interplay between mineralisation information, and analyze mineralisation patterns by calculating the similarity of groups of deposit knowledge nodes in the network.…”

Section: Inference and Analysis Modulementioning

confidence: 99%

Construction and application of mineral deposit knowledge graph application system

Yan

Xue²,

Wang³

et al. 2023

Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022)

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In order to expand the application of knowledge graph in geology, this paper constructs a knowledge graph application system of "knowledge graph visualization of mineral deposits + geospatial information indexing + inference analysis". The system adopts Flask as the back-end framework and Bootstrap as the front-end framework. Using the mineral deposit knowledge graph as the database. The system realises the geospatial indexing function of mineral deposit knowledge by integrating geographic images such as remote sensing images, and realises the inference and analysis function of mineralisation knowledge by. integrating graph algorithms.

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Section: Inference and Analysis Modulementioning

confidence: 99%

Construction and application of mineral deposit knowledge graph application system

Yan

Xue²,

Wang³

et al. 2023

Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022)

View full text Add to dashboard Cite

show abstract

Mineral prospectivity mapping integrated with geological map knowledge graph and geochemical data: A case study of gold deposits at Raofeng area, Shaanxi Province

Yan,

Xue,

et al. 2023

Ore Geology Reviews

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Intelligent Generation of Cross Sections Using a Conditional Generative Adversarial Network and Application to Regional 3D Geological Modeling

et al. 2022

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The cross section is the basic data for building 3D geological models. It is inefficient to draw a large number of cross sections to build an accurate model. This paper reports the use of multi-source and heterogeneous geological data, such as geological maps, gravity and aeromagnetic data, by a conditional generative adversarial network (CGAN) and implements an intelligent generation method of cross sections to overcome the problem of inefficient modeling data based on CGAN. Intelligent generation of cross sections and 3D geological modeling are carried out in three different areas in Liaoning Province. The results show that: (a) the accuracy of the proposed method is higher than the GAN and Variational AutoEncoder (VAE) models, achieving 87%, 45% and 68%, respectively; (b) the 3D geological model constructed by the generated cross sections in our study is consistent with manual creation in terms of stratum continuity and thickness. This study suggests that the proposed method is significant for surmounting the difficulty in data processing involved in regional 3D geological modeling.

show abstract

Intelligent mineral geological survey method: Daqiao-Yawan area in Gansu Province as an example

Cited by 4 publications

References 27 publications

Construction and application of mineral deposit knowledge graph application system

Construction and application of mineral deposit knowledge graph application system

Mineral prospectivity mapping integrated with geological map knowledge graph and geochemical data: A case study of gold deposits at Raofeng area, Shaanxi Province

Intelligent Generation of Cross Sections Using a Conditional Generative Adversarial Network and Application to Regional 3D Geological Modeling

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