  Detecting weak seismic events from noisy sensors is a difficult perceptual
task. We formulate this task as Bayesian inference and propose a generative
model of seismic events and signals across a network of spatially distributed
stations. Our system, SIGVISA, is the first to directly model seismic
waveforms, allowing it to incorporate a rich representation of the physics
underlying the signal generation process. We use Gaussian processes over
wavelet parameters to predict detailed waveform fluctuations based on
historical events, while degrading smoothly to simple parametric envelopes in
regions with no historical seismicity. Evaluating on data from the western US,
we recover three times as many events as previous work, and reduce mean
location errors by a factor of four while greatly increasing sensitivity to
low-magnitude events.
