Our model of seismic signals combines physically meaningful latent variables such as phase travel times, amplitudes, and signal decay rates, with data-driven models based on historical signals. Detailed waveform structure is represented using Gaussian process models of wavelet coefficients, encoding a general assumption that seismic signals are spatially corre- lated, and allowing us to detect and locate events even from weak signals at a single station. We show that the wavelet coefficients can be marginalized out using message passing applied to a state-space representation of the signal model, allowing for practical inference using a reversible jump Metropolis-Hastings algorithm.
We evaluate our system, SIGVISA (Signal-based Vertically Integrated Seismic Analysis), on a task of monitoring the western United States for a two-week period following the magnitude 6.0 event in Wells, NV in February 2008. During this period, SIGVISA detects between two to three times as many events as detection-based systems, while reducing mean location errors by a factor of four. We provide evidence that SIGVISA detects some events that are missed even by the regional monitoring networks that we use as a ground-truth comparison. A primary driver of monitoring research is the verification of nuclear test ban treaties, which are particularly concerned with detecting events in regions with no nearby historical seismicity. In our experiments, SIGVISA matches or exceeds the detection rates of existing systems for such events, and even detects a number of such events missed by human analysts.