A smoothed stochastic earthquake rate model considering seismicity and fault moment release for Europe

Abstract

We present a time-independent gridded earthquake rate forecast for the European region including Turkey. The spatial component of our model is based on kernel density estimation techniques, which we applied to both past earthquake locations and fault moment release on mapped crustal faults and subduction zone interfaces with assigned slip rates. Our forecast relies on the assumption that the locations of past seismicity is a good guide to future seismicity, and that future large-magnitude events occur more likely in the vicinity of known faults. We show that the optimal weighted sum of the corresponding two spatial densities depends on the magnitude range considered. The kernel bandwidths and density weighting function are optimized using retrospective likelihood-based forecast experiments. We computed earthquake activity rates (a-and b-value) of the truncated Gutenberg-Richter distribution separately for crustal and subduction seismicity based on a maximum likelihood approach that considers the spatial and temporal completeness history of the catalogue. The final annual rate of our forecast is purely driven by the maximum likelihood fit of activity rates to the catalogue data, whereas its spatial component incorporates contributions from both earthquake and fault moment-rate densities. Our model constitutes one branch of the earthquake source model logic tree of the 2013 European seismic hazard model released by the EU-FP7 project 'Seismic HAzard haRmonization in Europe' (SHARE) and contributes to the assessment of epistemic uncertainties in earthquake activity rates. We performed retrospective and pseudo-prospective likelihood consistency tests to underline the reliability of our model and SHARE's area source model (ASM) using the testing algorithms applied in the collaboratory for the study of earthquake predictability (CSEP). We comparatively tested our model's forecasting skill against the ASM and find a statistically significant better performance for testing periods of 10-20 yr. The testing results suggest that our model is a viable candidate model to serve for long-term forecasting on timescales of years to decades for the European region.

The 2013 European Seismic Hazard Model: key components and results

“…We carefully analyzed each individual fit to the data and replaced the PML-results by subjective judgment in case b-values deviated more than Δb = 0.4 from the global estimate of the SHEEC catalogue (b = 0.9: Hiemer et al 2014). The b-values obtained using the PML-approach for area sources characterised by very few data were constrained in the range 0.8-1.2, with the only exception of volcanic and swarm-type seismicity area sources, for which the b-value was allowed to increase up to 1.5 based on observed data.…”

“…In a second step, earthquakes rates are spatially distributed according to a weighted combination of two spatial probability density functions (Hiemer et al 2014). These are estimated from past seismicity and from accumulated moment release along faults, inferred from their slip rates and geometry.…”

“…We treat active faults and subduction zones in the same way, as they are all characterised by their size, geometry and slip rate converted to moment release per unit area (discretized to 5 km 2 patches). The following sections provide a general description of the model; full details are given in Hiemer et al (2014).…”

“…These assumptions are parameterised to estimate the annual earthquake rate as a function of space and magnitude. The total productivity and the Gutenberg-Richter relations are estimated from the entire catalogue considering the space-time completeness histories as defined for the superzones (Hiemer et al 2014). The cumulative annual earthquake rate forecast log 10 λ(M W ≥ 4.5) for crustal seismicity depicts a relatively smooth image with higher rates in currently active regions and along active fault systems (Fig.…”

“…We estimate the activity rate parameters (a-, b-value) for the entire catalogue including the space-time completeness variations (Hiemer et al 2014). The combined maximum likelihood estimate for crustal seismicity is a = 5.87 ± 0.04, b = 0.9 ± 0.01, equaling a total annual number of events N 0 (M W ≥ 4.5) = 65.6.…”