22,891 research outputs found
Earthquake Arrival Association with Backprojection and Graph Theory
The association of seismic wave arrivals with causative earthquakes becomes
progressively more challenging as arrival detection methods become more
sensitive, and particularly when earthquake rates are high. For instance,
seismic waves arriving across a monitoring network from several sources may
overlap in time, false arrivals may be detected, and some arrivals may be of
unknown phase (e.g., P- or S-waves). We propose an automated method to
associate arrivals with earthquake sources and obtain source locations
applicable to such situations. To do so we use a pattern detection metric based
on the principle of backprojection to reveal candidate sources, followed by
graph-theory-based clustering and an integer linear optimization routine to
associate arrivals with the minimum number of sources necessary to explain the
data. This method solves for all sources and phase assignments simultaneously,
rather than in a sequential greedy procedure as is common in other association
routines. We demonstrate our method on both synthetic and real data from the
Integrated Plate Boundary Observatory Chile (IPOC) seismic network of northern
Chile. For the synthetic tests we report results for cases with varying
complexity, including rates of 500 earthquakes/day and 500 false
arrivals/station/day, for which we measure true positive detection accuracy of
> 95%. For the real data we develop a new catalog between January 1, 2010 -
December 31, 2017 containing 817,548 earthquakes, with detection rates on
average 279 earthquakes/day, and a magnitude-of-completion of ~M1.8. A subset
of detections are identified as sources related to quarry and industrial site
activity, and we also detect thousands of foreshocks and aftershocks of the
April 1, 2014 Mw 8.2 Iquique earthquake. During the highest rates of aftershock
activity, > 600 earthquakes/day are detected in the vicinity of the Iquique
earthquake rupture zone
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
This paper introduces Non-Autonomous Input-Output Stable Network (NAIS-Net),
a very deep architecture where each stacked processing block is derived from a
time-invariant non-autonomous dynamical system. Non-autonomy is implemented by
skip connections from the block input to each of the unrolled processing stages
and allows stability to be enforced so that blocks can be unrolled adaptively
to a pattern-dependent processing depth. NAIS-Net induces non-trivial,
Lipschitz input-output maps, even for an infinite unroll length. We prove that
the network is globally asymptotically stable so that for every initial
condition there is exactly one input-dependent equilibrium assuming tanh units,
and multiple stable equilibria for ReL units. An efficient implementation that
enforces the stability under derived conditions for both fully-connected and
convolutional layers is also presented. Experimental results show how NAIS-Net
exhibits stability in practice, yielding a significant reduction in
generalization gap compared to ResNets.Comment: NIPS 201
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it is
ideally suited to learn representations for structured data and speed up the
exploration of chemical space. While convolutional neural networks have proven
to be the first choice for images, audio and video data, the atoms in molecules
are not restricted to a grid. Instead, their precise locations contain
essential physical information, that would get lost if discretized. Thus, we
propose to use continuous-filter convolutional layers to be able to model local
correlations without requiring the data to lie on a grid. We apply those layers
in SchNet: a novel deep learning architecture modeling quantum interactions in
molecules. We obtain a joint model for the total energy and interatomic forces
that follows fundamental quantum-chemical principles. This includes
rotationally invariant energy predictions and a smooth, differentiable
potential energy surface. Our architecture achieves state-of-the-art
performance for benchmarks of equilibrium molecules and molecular dynamics
trajectories. Finally, we introduce a more challenging benchmark with chemical
and structural variations that suggests the path for further work
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