Agglomorative Clustering for Fault Network Reconstruction

バージョン 1.0.0 (85.1 KB) 作成者: Yavor Kamer
A penalized likelihood based agglomorative clustering method for detection of planar features in 3D point clouds.

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更新 2020/10/15

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A method for fault network reconstruction based on the 3D spatial distribution of seismicity. This method uses a bottom-up approach that relies on initial sampling of the small scale features and reduction of this complexity by optimal local merging of substructures. The method provides the following advantages: 1) a bottom-up approach that explores all possible merger options at each step and moves coherently towards a global optimum; 2) an optimized atomization scheme to isolate the background (i.e. uncorrelated) points; 3) improved computation performance due to geometrical merging constrains.

The method will be published in the following paper.
Kamer Y., Ouillon G., Sornette D. (2020) "Fault Network Reconstruction using Agglomerative Clustering: Applications to South Californian Seismicity" Natural Hazards and Earth System Sciences

The submission includes the additional scripts to generate the synthetic tests featured in the paper.

引用

Yavor Kamer (2022). Agglomorative Clustering for Fault Network Reconstruction (https://www.mathworks.com/matlabcentral/fileexchange/81193-agglomorative-clustering-for-fault-network-reconstruction), MATLAB Central File Exchange. 取得済み .

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作成: R2015a
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ヒントを得たファイル: Minimal Bounding Box, Draw Cuboid, Inhull

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