Refine
Year of publication
Document Type
- Article (44)
- Other (2)
- Conference Proceeding (1)
- Doctoral Thesis (1)
- Habilitation Thesis (1)
- Postprint (1)
- Review (1)
Keywords
- Statistical seismology (5)
- Seismicity and tectonics (3)
- Bayesian (2)
- Dynamics: seismotectonics (2)
- earthquake interaction (2)
- forecasting (2)
- induced seismicity (2)
- seismic hazard (2)
- statistical seismology (2)
- Bayesian inference (1)
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.
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
We show that realistic aftershock sequences with space-time characteristics compatible with observations are generated by a model consisting of brittle fault segments separated by creeping zones. The dynamics of the brittle regions is governed by static/kinetic friction, 3D elastic stress transfer and small creep deformation. The creeping parts are characterized by high ongoing creep velocities. These regions store stress during earthquake failures and then release it in the interseismic periods. The resulting postseismic deformation leads to aftershock sequences following the modified Omori law. The ratio of creep coefficients in the brittle and creeping sections determines the duration of the postseismic transients and the exponent p of the modified Omori law
Earthquake swarms are often assumed to result from an intrusion of fluids into the seismogenic zone, causing seismicity patterns which significantly differ from aftershock sequences. But neither the temporal evolution nor the energy release of earthquake swarms is generally well understood. Because of the lack of descriptive empirical laws, the comparison with model simulations is typically restricted to aspects of the overall behaviour such as the frequency- magnitude distribution. However, previous investigations into a large earthquake swarm which occurred in the year 2000 in Vogtland/northwest Bohemia, Central Europe, revealed some well-defined characteristics which allow a rigorous test of model assumptions. In this study, simulations are performed of a discretized fault plane embedded in a 3-D elastic half- space. Earthquakes are triggered by fluid intrusion as well as by co-seismic and post-seismic stress changes. The model is able to reproduce the main observations, such as the fractal temporal occurrence of earthquakes, embedded aftershock sequences, and a power-law increase of the average seismic moment release. All these characteristics are found to result from stress triggering, whereas fluid diffusion is manifested in the spatiotemporal spreading of the hypocentres
Interdisziplinäres Zentrum für Musterdynamik und Angewandte Fernerkundung Workshop vom 9. - 10. Februar 2006
Diese Arbeit beschäftigt sich mit der Annahme, dass den Erdbeben ein selbstorganisiert kritischer Zustand der Erdkruste zugrunde liegt. Mit Hilfe einer Erweiterung bisheriger Modelle wird gezeigt, dass ein solcher Zustand nicht nur für die Grössenverteilung der Erdbeben (Gutenberg-Richter Gesetz), sondern auch für das beobachtete raumzeitliche Auftreten, z.B. für das Omori-Gesetz für Nachbebenserien, verantwortlich sein kann. Desweiteren wird die Frage nach der Vorhersagbarkeit grosser Erdbeben in solchen Modellsimulationen untersucht.
Earthquake rates are driven by tectonic stress buildup, earthquake-induced stress changes, and transient aseismic processes. Although the origin of the first two sources is known, transient aseismic processes are more difficult to detect. However, the knowledge of the associated changes of the earthquake activity is of great interest, because it might help identify natural aseismic deformation patterns such as slow-slip events, as well as the occurrence of induced seismicity related to human activities. For this goal, we develop a Bayesian approach to identify change-points in seismicity data automatically. Using the Bayes factor, we select a suitable model, estimate possible change-points, and we additionally use a likelihood ratio test to calculate the significance of the change of the intensity. The approach is extended to spatiotemporal data to detect the area in which the changes occur. The method is first applied to synthetic data showing its capability to detect real change-points. Finally, we apply this approach to observational data from Oklahoma and observe statistical significant changes of seismicity in space and time.
Earthquake faults interact with each other in many different ways and hence earthquakes cannot be treated as individual independent events. Although earthquake interactions generally lead to a complex evolution of the crustal stress field, it does not necessarily mean that the earthquake occurrence becomes random and completely unpredictable. In particular, the interplay between earthquakes can rather explain the occurrence of pronounced characteristics such as periods of accelerated and depressed seismicity (seismic quiescence) as well as spatiotemporal earthquake clustering (swarms and aftershock sequences). Ignoring the time-dependence of the process by looking at time-averaged values – as largely done in standard procedures of seismic hazard assessment – can thus lead to erroneous estimations not only of the activity level of future earthquakes but also of their spatial distribution. Therefore, it exists an urgent need for applicable time-dependent models. In my work, I aimed at better understanding and characterization of the earthquake interactions in order to improve seismic hazard estimations. For this purpose, I studied seismicity patterns on spatial scales ranging from hydraulic fracture experiments (meter to kilometer) to fault system size (hundreds of kilometers), while the temporal scale of interest varied from the immediate aftershock activity (minutes to months) to seismic cycles (tens to thousands of years). My studies revealed a number of new characteristics of fluid-induced and stress-triggered earthquake clustering as well as precursory phenomena in earthquake cycles. Data analysis of earthquake and deformation data were accompanied by statistical and physics-based model simulations which allow a better understanding of the role of structural heterogeneities, stress changes, afterslip and fluid flow. Finally, new strategies and methods have been developed and tested which help to improve seismic hazard estimations by taking the time-dependence of the earthquake process appropriately into account.
Stress drop is a key factor in earthquake mechanics and engineering seismology. However, stress drop calculations based on fault slip can be significantly biased, particularly due to subjectively determined smoothing conditions in the traditional least-square slip inversion. In this study, we introduce a mechanically constrained Bayesian approach to simultaneously invert for fault slip and stress drop based on geodetic measurements. A Gaussian distribution for stress drop is implemented in the inversion as a prior. We have done several synthetic tests to evaluate the stability and reliability of the inversion approach, considering different fault discretization, fault geometries, utilized datasets, and variability of the slip direction, respectively. We finally apply the approach to the 2010 M8.8 Maule earthquake and invert for the coseismic slip and stress drop simultaneously. Two fault geometries from the literature are tested. Our results indicate that the derived slip models based on both fault geometries are similar, showing major slip north of the hypocenter and relatively weak slip in the south, as indicated in the slip models of other studies. The derived mean stress drop is 5-6 MPa, which is close to the stress drop of similar to 7 MPa that was independently determined according to force balance in this region Luttrell et al. (J Geophys Res, 2011). These findings indicate that stress drop values can be consistently extracted from geodetic data.
A volcanic eruption is usually preceded by seismic precursors, but their interpretation and use for forecasting the eruption onset time remain a challenge. A part of the eruptive processes in open conduits of volcanoes may be similar to those encountered in geysers. Since geysers erupt more often, they are useful sites for testing new forecasting methods. We tested the application of Permutation Entropy (PE) as a robust method to assess the complexity in seismic recordings of the Strokkur geyser, Iceland. Strokkur features several minute-long eruptive cycles, enabling us to verify in 63 recorded cycles whether PE behaves consistently from one eruption to the next one. We performed synthetic tests to understand the effect of different parameter settings in the PE calculation. Our application to Strokkur shows a distinct, repeating PE pattern consistent with previously identified phases in the eruptive cycle. We find a systematic increase in PE within the last 15 s before the eruption, indicating that an eruption will occur. We quantified the predictive power of PE, showing that PE performs better than seismic signal strength or quiescence when it comes to forecasting eruptions.
The aim of this paper is to characterize the spatio-temporal distribution of Central-Europe seismicity. Specifically, by using a non-parametric statistical approach, the proportional hazard model, leading to an empirical estimation of the hazard function, we provide some constrains on the time behavior of earthquake generation mechanisms. The results indicate that the most conspicuous characteristics of M-w 4.0+ earthquakes is a temporal clustering lasting a couple of years. This suggests that the probability of occurrence increases immediately after a previous event. After a few years, the process becomes almost time independent. Furthermore, we investigate the cluster properties of the seismicity of Central-Europe, by comparing the obtained result with the one of synthetic catalogs generated by the epidemic type aftershock sequences (ETAS) model, which previously have been successfully applied for short term clustering. Our results indicate that the ETAS is not well suited to describe the seismicity as a whole, while it is able to capture the features of the short- term behaviour. Remarkably, similar results have been previously found for Italy using a higher magnitude threshold.
We show how the maximum magnitude within a predefined future time horizon may be estimated from an earthquake catalog within the context of Gutenberg-Richter statistics. The aim is to carry out a rigorous uncertainty assessment, and calculate precise confidence intervals based on an imposed level of confidence a. In detail, we present a model for the estimation of the maximum magnitude to occur in a time interval T-f in the future, given a complete earthquake catalog for a time period T in the past and, if available, paleoseismic events. For this goal, we solely assume that earthquakes follow a stationary Poisson process in time with unknown productivity Lambda and obey the Gutenberg-Richter law in magnitude domain with unknown b-value. The random variables. and b are estimated by means of Bayes theorem with noninformative prior distributions. Results based on synthetic catalogs and on retrospective calculations of historic catalogs from the highly active area of Japan and the low-seismicity, but high-risk region lower Rhine embayment (LRE) in Germany indicate that the estimated magnitudes are close to the true values. Finally, we discuss whether the techniques can be extended to meet the safety requirements for critical facilities such as nuclear power plants. For this aim, the maximum magnitude for all times has to be considered. In agreement with earlier work, we find that this parameter is not a useful quantity from the viewpoint of statistical inference.