Solving three major biases of the ETAS model to improve forecasts of the 2019 Ridgecrest sequence

Strong earthquakes cause aftershock sequences that are clustered in time according to a power decay law, and in space along their extended rupture, shaping a typically elongate pattern of aftershock locations. A widely used approach to model earthquake clustering, the Epidemic Type Aftershock Sequence (ETAS) model, shows three major biases. First, the conventional ETAS approach assumes isotropic spatial triggering, which stands in conflict with observations and geophysical arguments for strong earthquakes. Second, the spatial kernel has unlimited extent, allowing smaller events to exert disproportionate trigger potential over an unrealistically large area. Third, the ETAS model assumes complete event records and neglects inevitable short-term aftershock incompleteness as a consequence of overlapping coda waves. These three aspects can substantially bias the parameter estimation and lead to underestimated cluster sizes. In this article, we combine the approach of Grimm et al. (Bulletin of the Seismological Society of America, 2021), who introduced a generalized anisotropic and locally restricted spatial kernel, with the ETAS-Incomplete (ETASI) time model of Hainzl (Bulletin of the Seismological Society of America, 2021), to define an ETASI space-time model with flexible spatial kernel that solves the abovementioned shortcomings. We apply different model versions to a triad of forecasting experiments of the 2019 Ridgecrest sequence, and evaluate the prediction quality with respect to cluster size, largest aftershock magnitude and spatial distribution. The new model provides the potential of more realistic simulations of on-going aftershock activity, e.g. allowing better predictions of the probability and location of a strong, damaging aftershock, which might be beneficial for short term risk assessment and disaster response

[I] Localised, ephemeral grounding of the Fimbul Ice Shelf, East Antarctica, as revealed from tidally modulated cryoseismicity

The Tenth Symposium on Polar Science/Ordinary sessions: [OG] Polar Geosciences, Wed. 4 Dec. / 3F Seminar room, National Institute of Polar Researc

Comment on “Potential short‐term earthquake forecasting by farm animal monitoring” by Wikelski, Mueller, Scocco, Catorci, Desinov, Belyaev, Keim, Pohlmeier, Fechteler, and Mai

Based on an analysis of continuous monitoring of farm animal behavior in the region of the 2016 M6.6 Norcia earthquake in Italy, Wikelski et al., 2020; (Seismol Res Lett, 89, 2020, 1238) conclude that animal activity can be anticipated with subsequent seismic activity and that this finding might help to design a "short-term earthquake forecasting method." We show that this result is based on an incomplete analysis and misleading interpretations. Applying state-of-the-art methods of statistics, we demonstrate that the proposed anticipatory patterns cannot be distinguished from random patterns, and consequently, the observed anomalies in animal activity do not have any forecasting power

Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model

The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017-2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively

Cyclical geothermal unrest as a precursor to Iceland’s 2021 Fagradalsfjall eruption

Understanding and constraining the source of geodetic deformation in volcanic areas is an important component of hazard assessment. Here, we analyse deformation and seismicity for one year before the March 2021 Fagradalsfjall eruption in Iceland. We generate a high-resolution catalogue of 39,500 earthquakes using optical cable recordings and develop a poroelastic model to describe three pre-eruptional uplift and subsidence cycles at the Svartsengi geothermal field, 8 km west of the eruption site. We find the observed deformation is best explained by cyclic intrusions into a permeable aquifer by a fluid injected at 4 km depth below the geothermal field, with a total volume of 0.11 ± 0.05 km3 and a density of 850 ± 350 kg m–3. We therefore suggest that ingression of magmatic CO2 can explain the geodetic, gravity and seismic data, although some contribution of magma cannot be excluded

A Coulomb Stress Response Model for Time‐Dependent Earthquake Forecasts

Seismicity models are probabilistic forecasts of earthquake rates to support seismic hazard assessment. Physics‐based models allow extrapolating previously unsampled parameter ranges and enable conclusions on underlying tectonic or human‐induced processes. The Coulomb Failure (CF) and the rate‐and‐state (RS) models are two widely used physics‐based seismicity models both assuming pre‐existing populations of faults responding to Coulomb stress changes. The CF model depends on the absolute Coulomb stress and assumes instantaneous triggering if stress exceeds a threshold, while the RS model only depends on stress changes. Both models can predict background earthquake rates and time‐dependent stress effects, but the RS model with its three independent parameters can additionally explain delayed aftershock triggering. This study introduces a modified CF model where the instantaneous triggering is replaced by a mean time‐to‐failure depending on the absolute stress value. For the specific choice of an exponential dependence on stress and a stationary initial seismicity rate, we show that the model leads to identical results as the RS model and reproduces the Omori‐Utsu relation for aftershock decays as well stress‐shadowing effects. Thus, both CF and RS models can be seen as special cases of the new model. However, the new stress response model can also account for subcritical initial stress conditions and alternative functions of the mean time‐to‐failure depending on the problem and fracture mode.Plain Language Summary: One of the most pressing questions in earthquake physics is understanding where and when earthquakes occur and how seismicity is related to stress changes in the Earth's crust. This question is even more important today because humans are increasingly influencing stresses in the Earth by exploiting the subsurface. So far, two classes of physics‐based seismicity models have been used primarily. One assumes instantaneous earthquake occurrence when stress exceeds a threshold, and the other is based on the nucleation of earthquakes according to friction laws determined in the laboratory. Both models are very different in their approaches, have advantages and disadvantages, and are limited in their applicability. In this paper, we introduce a new concept of seismicity models, which is very simple and short to derive and combines the strengths of both previous models, as shown in various applications to human‐related seismicity. The forecasts of both traditional models turn out to be special cases of the new model.Key Points: We introduce a modified Coulomb Failure seismicity model in which a mean time‐to‐failure replaces instantaneous triggering. The model explains the main features of time‐dependent seismicity, including aftershock activity and stress shadow effects. As a special case, it includes the rate‐state model solutions but can also handle subcritical stresses and other fracture types.European Unions 2020 research and innovation programmehttps://github.com/torstendahm/tds

Self-Organization of Earthquake Swarms

The temporal clustering of swarm activity differs significantly from characteristics of aftershock sequences accompanying mainshocks. This is often assumed to be caused by crustal structure complexities and fluid migration. However, the underlying mechanism is not yet fully understood, especially, the processes and conditions which lead to the apparent differences between the swarm patterns and typical mainshock-aftershock sequences. In previous works, we have shown that the most conspicuous characteristics of tectonic earthquakes can be reproduced by stick-slip block models incorporating visco-elastic interactions. Now, the same model is shown to reproduce an almost periodical occurrence of earthquake swarms in the case of an enlarged postseismic response. The simulated swarms respect not only the Gutenberg-Richter law for the event sizes, they also reproduce several observations regarding their spatiotemporal patterns. In particular, the comparison with the January 1997 and the year 2000 swarm in Vogtland/NW-Bohemia shows a good agreement in the interevent-time distributions and the spatio-temporal spreading of the swarm activity. The simulated seismicity patterns result from self-organization within the swarm due to local stress transfers and viscous coupling. Consequently, the agreement with the Vogtland swarm activity do not allow any decision about the preparatory process of the swarms; in particular, the question whether the swarms are initially triggered by fluid intrusion or tectonic motion cannot be answered. However, the model investigations suggest that the process of self-organization is very important for understanding the activity patterns of earthquake swarms