Pseudo-prospective testing of 5-year earthquake forecasts for California using inlabru

Probabilistic earthquake forecasts estimate the likelihood of future earthquakes within a specified time-space-magnitude window and are important because they inform planning of hazard mitigation activities on different timescales. The spatial component of such forecasts, expressed as seismicity models, generally rely upon some combination of past event locations and underlying factors which might affect spatial intensity, such as strain rate, fault location and slip rate or past seismicity. For the first time, we extend previously reported spatial seismicity models, generated using the open source inlabru package, to time-independent earthquake forecasts using California as a case study. The inlabru approach allows the rapid evaluation of point process models which integrate different spatial datasets. We explore how well various candidate forecasts perform compared to observed activity over three contiguous five year time periods using the same training window for the seismicity data. In each case we compare models constructed from both full and declustered earthquake catalogues. In doing this, we compare the use of synthetic catalogue forecasts to the more widely-used grid-based approach of previous forecast testing experiments. The simulated-catalogue approach uses the full model posteriors to create Bayesian earthquake forecasts. We show that simulated-catalogue based forecasts perform better than the grid-based equivalents due to (a) their ability to capture more uncertainty in the model components and (b) the associated relaxation of the Poisson assumption in testing. We demonstrate that the inlabru models perform well overall over various time periods, and hence that independent data such as fault slip rates can improve forecasting power on the time scales examined. Together, these findings represent a significant improvement in earthquake forecasting is possible, though this has yet to be tested and proven in true prospective mode.

A New Smoothed Seismicity Approach to Include Aftershocks and Foreshocks in Spatial Earthquake Forecasting: Application to the Global Mw ≥ 5.5 Seismicity

Seismicity-based earthquake forecasting models have been primarily studied and developed over the past twenty years. These models mainly rely on seismicity catalogs as their data source and provide forecasts in time, space, and magnitude in a quantifiable manner. In this study, we presented a technique to better determine future earthquakes in space based on spatially smoothed seismicity. The improvement’s main objective is to use foreshock and aftershock events together with their mainshocks. Time-independent earthquake forecast models are often developed using declustered catalogs, where smaller-magnitude events regarding their mainshocks are removed from the catalog. Declustered catalogs are required in the probabilistic seismic hazard analysis (PSHA) to hold the Poisson assumption that the events are independent in time and space. However, as highlighted and presented by many recent studies, removing such events from seismic catalogs may lead to underestimating seismicity rates and, consequently, the final seismic hazard in terms of ground shaking. Our study also demonstrated that considering the complete catalog may improve future earthquakes’ spatial forecast. To do so, we adopted two different smoothed seismicity methods: (1) the fixed smoothing method, which uses spatially uniform smoothing parameters, and (2) the adaptive smoothing method, which relates an individual smoothing distance for each earthquake. The smoothed seismicity models are constructed by using the global earthquake catalog with Mw ≥ 5.5 events. We reported progress on comparing smoothed seismicity models developed by calculating and evaluating the joint log-likelihoods. Our resulting forecast shows a significant information gain concerning both fixed and adaptive smoothing model forecasts. Our findings indicate that complete catalogs are a notable feature for increasing the spatial variation skill of seismicity forecasts.

Comparing Short-Term Seismic and COVID-19 Fatality Risks in Italy

Risks assessment and risks comparison are basic concepts for emergency management. In the fields of earthquake engineering and engineering seismology, the operational earthquake loss forecasting (OELF) is the research frontier for the assessment of short-term seismic risk. It combines seismicity models, continuously updated based on ground-motion monitoring (i.e., operational earthquake forecasting), with large-scale vulnerability models for the built environment and exposure data. With the aim of contributing to the discussion about capabilities and limitations of OELF, the study presented aims at comparing the OELF results and the fatality risk (based on fatality data) related to coronavirus 2019 (COVID-19) that, at the time of writing, is perceived as very relevant and required unprecedented risk reduction measures in several countries, most notably Italy. Results show that, at a national scale in Italy, the COVID-19 risk has been higher than the seismic risk during the two pandemic waves even if, at the end of the so-called lockdown, the evolution of the pandemic suggested the possibility (not realized) of reaching a situation of comparable seismic and COVID-19 risks in a few weeks. Because the two risks vary at a local scale, risks comparison was also carried out on a regional basis, showing that, before the beginning of the second wave, in some cases, the seismic risk, as assessed by means of OELF, was larger than the pandemic one.

Two global ensemble seismicity models obtained from the combination of interseismic strain measurements and earthquake-catalogue information

SUMMARY Global seismicity models provide scientific hypotheses about the rate, location and magnitude of future earthquakes to occur worldwide. Given the aleatory variability of earthquake activity and epistemic uncertainties in seismicity forecasting, the veracity of these hypotheses can only be confirmed or rejected after prospective forecast evaluation. In this study, we present the construction of and test results for two updated global earthquake models, aimed at providing mean estimates of shallow (d ≤ 70 km) seismicity for seismic hazard assessment. These approaches, referred to as the Tectonic Earthquake Activity Model (TEAM) and the World Hybrid Earthquake Estimates based on Likelihood scores (WHEEL) model, use the Subduction Megathrust Earthquake Rate Forecast (SMERF2), an earthquake-rate model for subduction zones constrained by geodetic strain measurements and earthquake-catalogue information. Thus, these global ensemble seismicity models capture two independent components necessary for long-term earthquake forecasting, namely interseismic crustal strain accumulation and sudden lithospheric stress release. The calibration period for TEAM and WHEEL extends from 1977 January 1 to 2013 December 31. Accordingly, we use m ≥ 5.95 earthquakes recorded during the 2014–2019 period to pseudo-prospectively evaluate the forecasting skills of these earthquake models, and statistically compare their performances to that of the Global Earthquake Activity Rate (GEAR1) model. As a result, GEAR1 and WHEEL are the most informative global seismicity models during the pseudo-prospective test period, as both rank with the highest information scores among all participant earthquake-rate forecasts. Nonetheless, further prospective evaluations are required to more accurately assess the abilities of these global ensemble seismicity models to forecast long-term earthquake activity.

A physical constraint on smoothed-seismicity models and the stationary seismicity assumption in long-term forecasting

<p>In earthquake forecasting, smoothed-seismicity models (SSM) are based on the assumption that previous earthquakes serve as a guideline for future events. Different kernels are used to spatially extrapolate each moment tensor from a seismic catalog into a moment-rate density field. Nevertheless, governing mechanical principles remain absent through the model conception, even though crustal stress is responsible for moment release mainly in pre-existent faults. Furthermore, a lately developed SSM by Hiemer et al., 2013 (SEIFA) incorporates active-fault characterization and deformation rates stochastically, so that a geological estimate of moment release could also be taken into account. Motivated by this innovative approach, we address the question: How representative is the stochastic temporal/spatial averaging of SEIFA, of the long-term crustal deformation and stress? In this context, physics-based modeling provides insights about the energy, stress, and strain-rate fields within the crust due to discontinuities found therein. In this work, we aim to understand the required temporal window of SEIFA to satisfy mechanically its underlying assumption of stationarity. We build various SEIFA models within different spatio-temporal subsets of a catalog and confront them with a physics-based model of long-term seismic energy/moment rate. Following, we develop a method based on the moment-balance principle and information theory to compare the spatial similarity between these two types of models. These models are built from two spatially conforming layers of information: a complete seismic catalog and a computerized 3-D geometry of mapped faults along with their long-term slip rate. SEIFA uses both datasets to produce a moment-density rate field, from which later a forecast could be derived. A simple physics-based model is used as proof of concept, such as the steady-state Boundary Element Method (BEM). It uses the fault 3D geometry and slip rates to calculate the long-term interseismic energy rate and elastic stress and strain tensors, accumulated both along the faults and within the crust. The SHARE European Earthquake Catalog and the European Database of Seismogenic Faults are used as a case study, constrained to crustal faults and different spatio-temporal subsets of the Italy region in the 1000-2006 time window. The moment-balance principle is analyzed in terms of its spatial distribution calculating the spatial mutual information (SMI) between both models as a similarity measure. Finally, by using the SMI as a minimization function, we determine the catalog optimal time window for which the predicted moment rate by the SSM is closer to the geomechanical prediction. We emphasize that regardless of the stationarity assumption usefulness in seismicity forecasting, we determine a simple method that provides a physical boundary to data-driven seismicity models. This framework may be used in the future to combine seismicity data and geophysical modeling for earthquake forecasting.</p>