Automatic Reconstruction of Fault Networks from Seismicity Catalogs: 3D Optimal Anisotropic Dynamic Clustering

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

Direct Numerical Simulations of Multiphase, Stratified, Environmental Fluid Flows

Many fundamental processes in oceanic transport and limnology occur in geophysical flows that are both local in space and transient in time, and that require equally space and time-resolved methods of analysis. The importance of providing physics-based, quantitative modeling of such flows has driven the development of numerical methods for geophysical fluid dynamics for over three decades. Here, we use direct numerical simulations to investigate a range of stratified, particle-laden flows that are accurately described by the three-dimensional Navier-Stokes equations for an incompressible flow in the Boussinesq limit. We firstly investigate the propagation, transport and mixing dynamics of density-driven gravity currents moving in stratified environments. We propose new models for the intrusion of a turbidity current into a linearly stratified ambient based on three-dimensional simulations. We then describe the interaction between a gravity-current and an internal wave and characterize a phenomenological change in the long-term effect of the interaction at a critical wave height. We then quantify the role of double-diffusive processes in the Dead Sea in Summer and their role in the seasonality of salt crystallization and deposition. We also describe large-scale double-diffusive instabilities that arise in high-Prandtl sedimentary double-diffusive systems such as linearly stratified particle-laden salt water. Finally, we quantify mixing induced by a swarm of small-scale self-propelled organisms migrating in a stratified ambient fluid. We compare the relative contribution to mixing by individual swimmers within the swarm to that of the large-scale motion produced by the collective motion of the swarm

Segmentation of Fault Networks Determined from Spatial Clustering of Earthquakes

We present a new method of data clustering applied to earthquake catalogs, with the goal of reconstructing the seismically active part of fault networks. We first use an original method to separate clustered events from uncorrelated seismicity using the distribution of volumes of tetrahedra defined by closest neighbor events in the original and randomized seismic catalogs. The spatial disorder of the complex geometry of fault networks is then taken into account by defining faults as probabilistic anisotropic kernels, whose structures are motivated by properties of discontinuous tectonic deformation and previous empirical observations of the geometry of faults and of earthquake clusters at many spatial and temporal scales. Combining this a priori knowledge with information theoretical arguments, we propose the Gaussian mixture approach implemented in an Expectation-Maximization (EM) procedure. A cross-validation scheme is then used and allows the determination of the number of kernels that should be used to provide an optimal data clustering of the catalog. This three-steps approach is applied to a high quality relocated catalog of the seismicity following the 1986 Mount Lewis ($M_l=5.7$) event in California and reveals that events cluster along planar patches of about 2 km$^2$, i.e. comparable to the size of the main event. The finite thickness of those clusters (about 290 m) suggests that events do not occur on well-defined euclidean fault core surfaces, but rather that the damage zone surrounding faults may be seismically active at depth. Finally, we propose a connection between our methodology and multi-scale spatial analysis, based on the derivation of spatial fractal dimension of about 1.8 for the set of hypocenters in the Mnt Lewis area, consistent with recent observations on relocated catalogs