Agglomorative Clustering for Fault Network Reconstruction

A penalized likelihood based agglomorative clustering method for detection of planar features in 3D point clouds.

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.