Abstract
We present a model for estimating the probabilities of future earthquakes of magnitudes m > 4.95 in Italy. The model, a slightly modified version of the one proposed for California by Helmstetter et al. (2007) and Werner et al. (2010), approximates seismicity by a spatially heterogeneous, temporally homogeneous Poisson point process. The temporal, spatial and magnitude dimensions are entirely decoupled. Magnitudes are independently and identically distributed according to a tapered Gutenberg-Richter magnitude distribution. We estimated the spatial distribution of future seismicity by smoothing the locations of past earthquakes listed in two Italian catalogs: a short instrumental catalog and a longer instrumental and historical catalog. The bandwidth of the adaptive spatial kernel is estimated by optimizing the predictive power of the kernel estimate of the spatial earthquake density in retrospective forecasts. When available and trustworthy, we used small earthquakes m>2.95 to illuminate active fault structures and likely future epicenters. By calibrating the model on two catalogs of different duration to create two forecasts, we intend to quantify the loss (or gain) of predictability incurred when only a short but recent data record is available. Both forecasts, scaled to five and ten years, were submitted to the Italian prospective forecasting experiment of the global Collaboratory for the Study of Earthquake Predictability (CSEP). An earlier forecast from the model was submitted by Helmstetter et al. (2007) to the Regional Earthquake Likelihood Model (RELM) experiment in California, and, with over half of the five-year experiment over, the forecast performs better than its competitors.
Highlights
We document the calibration of a previously published, time-independent model of earthquake occurrences in the region of Italy
For the forecast based on instrumental and historic seismicity, we used the parametric catalog of Italian earthquakes (Catalogo Parametrico dei Terremoti Italiani, version CPTI08) [Rovida and the CPTI Working Group
[1, 50] by choosing the value that maximized the likelihood of the target earthquakes in the target catalog, given the smoothed spatial density estimated from the learning catalog
Summary
We document the calibration of a previously published, time-independent model of earthquake occurrences in the region of Italy. SMOOTHED SEISMICITY EARTHQUAKE FORECASTS kernel-density estimation method because the locations of future – rather than past – earthquakes are predicted, and these locations can be in regions of little previous seismicity This is only a partial solution: Kagan and Jackson [1994] conjectured that the optimal predictive horizon of a forecast based on smoothed seismicity will scale proportionally with the learning catalog because of spatiotemporal clustering. On the other hand, according to Kagan and Jackson [1994], it remains an untested hypothesis that seismicity estimates based on geological observations, i.e. the earthquake history of several thousands of years, can provide more predictive and relevant information for engineering design than high-quality, low-threshold instrumental catalogs.
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