Refine
Year of publication
Document Type
- Article (47)
- Other (2)
- Conference Proceeding (1)
- Doctoral Thesis (1)
- Habilitation Thesis (1)
- Postprint (1)
- Review (1)
Keywords
- Statistical seismology (5)
- Seismicity and tectonics (3)
- seismic hazard (3)
- Bayesian (2)
- Dynamics: seismotectonics (2)
- earthquake interaction (2)
- forecasting (2)
- induced seismicity (2)
- seismicity (2)
- statistical seismology (2)
The Seismic Hazard Inferred from Tectonics based on the Global Strain Rate Map (SHIFT_GSRM) earthquake forecast was designed to provide high-resolution estimates of global shallow seismicity to be used in seismic hazard assessment. This model combines geodetic strain rates with global earthquake parameters to characterize long-term rates of seismic moment and earthquake activity. Although SHIFT_GSRM properly computes seismicity rates in seismically active continental regions, it underestimates earthquake rates in subduction zones by an average factor of approximately 3. We present a complementary method to SHIFT_GSRM to more accurately forecast earthquake rates in 37 subduction segments, based on the conservation of moment principle and the use of regional interface seismicity parameters, such as subduction dip angles, corner magnitudes, and coupled seismogenic thicknesses. In seven progressive steps, we find that SHIFT_GSRM earthquake-rate underpredictions are mainly due to the utilization of a global probability function of seismic moment release that poorly captures the great variability among subduction megathrust interfaces. Retrospective test results show that the forecast is consistent with the observations during the 1 January 1977 to 31 December 2014 period. Moreover, successful pseudoprospective evaluations for the 1 January 2015 to 31 December 2018 period demonstrate the power of the regionalized earthquake model to properly estimate subduction-zone seismicity.
The spatio-temporal epidemic type aftershock sequence (ETAS) model is widely used to describe the self-exciting nature of earthquake occurrences. While traditional inference methods provide only point estimates of the model parameters, we aim at a fully Bayesian treatment of model inference, allowing naturally to incorporate prior knowledge and uncertainty quantification of the resulting estimates. Therefore, we introduce a highly flexible, non-parametric representation for the spatially varying ETAS background intensity through a Gaussian process (GP) prior. Combined with classical triggering functions this results in a new model formulation, namely the GP-ETAS model. We enable tractable and efficient Gibbs sampling by deriving an augmented form of the GP-ETAS inference problem. This novel sampling approach allows us to assess the posterior model variables conditioned on observed earthquake catalogues, i.e., the spatial background intensity and the parameters of the triggering function. Empirical results on two synthetic data sets indicate that GP-ETAS outperforms standard models and thus demonstrate the predictive power for observed earthquake catalogues including uncertainty quantification for the estimated parameters. Finally, a case study for the l'Aquila region, Italy, with the devastating event on 6 April 2009, is presented.
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.
Reservoir-triggered seismicity has been observed near dams during construction, impoundment, and cyclic filling in many parts of the earth. In Turkey, the number of dams has increased substantially over the last decade, with Ataturk Dam being the largest dam in Turkey with a total water capacity of 48.7 billion m(3). After the construction of the dam, the monitoring network has improved. Considering earthquakes above the long-term completeness magnitude of M-C = 3.5, the local seismicity rate has substantially increased after the filling of the reservoir. Recently, two damaging earthquakes of M-w 5.5 and M-w 5.1 occurred in the town of Samsat near the Ataturk Reservoir in 2017 and 2018, respectively. In this study, we analyze the spatio-temporal evolution of seismicity and its source properties in relation to the temporal water-level variations and the stresses resulting from surface loading and pore-pressure diffusion. We find that water-level and seismicity rate are anti-correlated, which is explained by the stabilization effect of the gravitational induced stress imposed by water loading on the local faults. On the other hand, we find that the overall effective stress in the seismogenic zone increased over decades due to pore-pressure diffusion, explaining the enhanced background seismicity during recent years. Additionally, we observe a progressive decrease of the Gutenberg-Richter b-value. Our results indicate that the stressing rate finally focused on the region where the two damaging earthquakes occurred in 2017 and 2018.
The Gutenberg-Richter relation for earthquake magnitudes is the most famous empirical law in seismology. It states that the frequency of earthquake magnitudes follows an exponential distribution; this has been found to be a robust feature of seismicity above the completeness magnitude, and it is independent of whether global, regional, or local seismicity is analyzed. However, the exponent b of the distribution varies significantly in space and time, which is important for process understanding and seismic hazard assessment; this is particularly true because of the fact that the Gutenberg-Richter b-value acts as a proxy for the stress state and quantifies the ratio of large-to-small earthquakes. In our work, we focus on the automatic detection of statistically significant temporal changes of the b-value in seismicity data. In our approach, we use Bayes factors for model selection and estimate multiple change-points of the frequency-magnitude distribution in time. The method is first applied to synthetic data, showing its capability to detect change-points as function of the size of the sample and the b-value contrast. Finally, we apply this approach to examples of observational data sets for which b-value changes have previously been stated. Our analysis of foreshock and after-shock sequences related to mainshocks, as well as earthquake swarms, shows that only a portion of the b-value changes is statistically significant.
Introduction to special issue: Dynamics of seismicity patterns and earthquake triggering - Preface
(2006)
In low-seismicity regions, such as France or Germany, the estimation of probabilistic seismic hazard must cope with the difficult identification of active faults and with the low amount of seismic data available. Since the probabilistic hazard method was initiated, most studies assume a Poissonian occurrence of earthquakes. Here we propose a method that enables the inclusion of time and space dependences between earthquakes into the probabilistic estimation of hazard. Combining the seismicity model Epidemic Type Aftershocks-Sequence (ETAS) with a Monte Carlo technique, aftershocks are naturally accounted for in the hazard determination. The method is applied to the Pyrenees region in Southern France. The impact on hazard of declustering and of the usual assumption that earthquakes occur according to a Poisson process is quantified, showing that aftershocks contribute on average less than 5 per cent to the probabilistic hazard, with an upper bound around 18 per cent
[1] According to the well-known Coulomb failure criterion the variation of either stress or pore pressure can result in earthquake rupture. Aftershock sequences characterized by the Omori law are often assumed to be the consequence of varying stress, whereas earthquake swarms are thought to be triggered by fluid intrusions. The role of stress triggering can be analyzed by modeling solely three-dimensional (3-D) elastic stress changes in the crust, but fluid flows which initiate seismicity cannot be investigated without considering complex seismicity patterns resulting from both pore pressure variations and earthquake-connected stress field changes. We show that the epidemic-type aftershock sequence (ETAS) model is an appropriate tool to extract the primary fluid signal from such complex seismicity patterns. We analyze a large earthquake swarm that occurred in 2000 in Vogtland/NW Bohemia, central Europe. By fitting the stochastic ETAS model, we find that stress triggering is dominant in creating the observed seismicity patterns and explains the observed fractal interevent time distribution. External forcing, identified with pore pressure changes due to fluid intrusion, is found to directly trigger only a few percent of the total activity. However, temporal deconvolution indicates that a pronounced fluid signal initiated the swarm. These results are confirmed by our analogous investigation of model simulations in which earthquakes are triggered by fluid intrusion as well as stress transfers on a fault plane embedded in a 3-D elastic half-space. The deconvolution procedure based on the ETAS model is able to reveal the underlying pore pressure variations
An important task of seismic hazard assessment consists of estimating the rate of seismic moment release which is correlated to the rate of tectonic deformation and the seismic coupling. However, the estimations of deformation depend on the type of information utilized (e.g. geodetic, geological, seismic) and include large uncertainties. We therefore estimate the deformation rate in the Lower Rhine Embayment (LRE), Germany, using an integrated approach where the uncertainties have been systematically incorporated. On the basis of a new homogeneous earthquake catalogue we initially determine the frequency-magnitude distribution by statistical methods. In particular, we focus on an adequate estimation of the upper bound of the Gutenberg-Richter relation and demonstrate the importance of additional palaeoseis- mological information. The integration of seismological and geological information yields a probability distribution of the upper bound magnitude. Using this distribution together with the distribution of Gutenberg-Richter a and b values, we perform Monte Carlo simulations to derive the seismic moment release as a function of the observation time. The seismic moment release estimated from synthetic earthquake catalogues with short catalogue length is found to systematically underestimate the long-term moment rate which can be analytically determined. The moment release recorded in the LRE over the last 250 yr is found to be in good agreement with the probability distribution resulting from the Monte Carlo simulations. Furthermore, the long-term distribution is within its uncertainties consistent with the moment rate derived by geological measurements, indicating an almost complete seismic coupling in this region. By means of Kostrov's formula, we additionally calculate the full deformation rate tensor using the distribution of known focal mechanisms in LRE. Finally, we use the same approach to calculate the seismic moment and the deformation rate for two subsets of the catalogue corresponding to the east- and west-dipping faults, respectively
The statistics of time delays between successive earthquakes has recently been claimed to be universal and to show the existence of clustering beyond the duration of aftershock bursts. We demonstrate that these claims are unjustified. Stochastic simulations with Poissonian background activity and triggered Omori-type aftershock sequences are shown to reproduce the interevent-time distributions observed on different spatial and magnitude scales in California. Thus the empirical distribution can be explained without any additional long-term clustering. Furthermore, we find that the shape of the interevent-time distribution, which can be approximated by the gamma distribution, is determined by the percentage of main-shocks in the catalog. This percentage can be calculated by the mean and variance of the interevent times and varies between 5% and 90% for different regions in California. Our investigation of stochastic simulations indicates that the interevent-time distribution provides a nonparametric reconstruction of the mainshock magnitude-frequency distribution that is superior to standard declustering algorithm