High Resolution Long- and Short-Term Earthquake Forecasts for California

TL;DR

This paper introduces two earthquake forecasting models for California, one time-independent and one time-dependent, demonstrating improved prediction accuracy and spatial resolution using adaptive kernels and modified ETAS models.

Contribution

The paper develops and tests novel high-resolution spatial and temporal earthquake forecast models, incorporating adaptive kernels and modified ETAS, with enhanced evaluation methods.

Findings

Time-independent model predicts five-year earthquake probabilities.

Time-dependent ETAS model improves next-day forecast accuracy by a factor of 6.

Spatial distribution of large earthquakes correlates with small event locations.

Abstract

We present two models for estimating the probabilities of future earthquakes in California, to be tested in the Collaboratory for the Study of Earthquake Predictability (CSEP). The first, time-independent model, modified from Helmstetter et al. (2007), provides five-year forecasts for magnitudes m > 4.95. We show that large quakes occur on average near the locations of small m > 2 events, so that a high-resolution estimate of the spatial distribution of future large quakes is obtained from the locations of the numerous small events. We employ an adaptive spatial kernel of optimized bandwidth and assume a universal, tapered Gutenberg-Richter distribution. In retrospective tests, we show that no Poisson forecast could capture the observed variability. We therefore also test forecasts using a negative binomial distribution for the number of events. We modify existing likelihood-based tests to better evaluate the spatial forecast. Our time-dependent model, an Epidemic Type Aftershock Sequence (ETAS) model modified from Helmstetter et al. (2006), provides next-day forecasts for m > 3.95. The forecasted rate is the sum of a background rate, proportional to our time-independent model, and of the triggered events due to all prior earthquakes. Each earthquake triggers events with a rate that increases exponentially with its magnitude and decays in time according to Omori's law. An isotropic kernel models the spatial density of aftershocks for small (< 5.5) events. For larger quakes, we smooth early aftershocks to forecast later events. We estimate parameters by optimizing retrospective forecasts. Our short-term model realizes a gain of about 6.0 over the time-independent model.