  We propose a new pattern recognition method that is able to reconstruct the
3D structure of the active part of a fault network using the spatial location
of earthquakes. The method is a generalization of the so-called dynamic
clustering method, that originally partitions a set of datapoints into
clusters, using a global minimization criterion over the spatial inertia of
those clusters. The new method improves on it by taking into account the full
spatial inertia tensor of each cluster, in order to partition the dataset into
fault-like, anisotropic clusters. Given a catalog of seismic events, the output
is the optimal set of plane segments that fits the spatial structure of the
data. Each plane segment is fully characterized by its location, size and
orientation. The main tunable parameter is the accuracy of the earthquake
localizations, which fixes the resolution, i.e. the residual variance of the
fit. The resolution determines the number of fault segments needed to describe
the earthquake catalog, the better the resolution, the finer the structure of
the reconstructed fault segments. The algorithm reconstructs successfully the
fault segments of synthetic earthquake catalogs. Applied to the real catalog
constituted of a subset of the aftershocks sequence of the 28th June 1992
Landers earthquake in Southern California, the reconstructed plane segments
fully agree with faults already known on geological maps, or with blind faults
that appear quite obvious on longer-term catalogs. Future improvements of the
method are discussed, as well as its potential use in the multi-scale study of
the inner structure of fault zones.
