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Agglomorative Clustering for Fault Network Reconstruction

version 1.0.0 (85.1 KB) by Yavor Kamer
A penalized likelihood based agglomorative clustering method for detection of planar features in 3D point clouds.

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Updated 15 Oct 2020

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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.

Cite As

Yavor Kamer (2021). Agglomorative Clustering for Fault Network Reconstruction (https://www.mathworks.com/matlabcentral/fileexchange/81193-agglomorative-clustering-for-fault-network-reconstruction), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux
Acknowledgements

Inspired by: Minimal Bounding Box, Draw Cuboid, Inhull

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