Refine
Has Fulltext
- no (4)
Year of publication
- 2013 (4) (remove)
Language
- English (4)
Is part of the Bibliography
- yes (4)
Keywords
- Statistical seismology (2)
- Earthquake interaction (1)
- Induced seismicity (1)
- Seismic attenuation (1)
- Seismicity and tectonics (1)
- Triggered seismicity (1)
- and prediction (1)
- forecasting (1)
Institute
Time-dependent probabilistic seismic hazard assessment requires a stochastic description of earthquake occurrences. While short-term seismicity models are well-constrained by observations, the recurrences of characteristic on-fault earthquakes are only derived from theoretical considerations, uncertain palaeo-events or proxy data. Despite the involved uncertainties and complexity, simple statistical models for a quasi-period recurrence of on-fault events are implemented in seismic hazard assessments. To test the applicability of statistical models, such as the Brownian relaxation oscillator or the stress release model, we perform a systematic comparison with deterministic simulations based on rate- and state-dependent friction, high-resolution representations of fault systems and quasi-dynamic rupture propagation. For the specific fault network of the Lower Rhine Embayment, Germany, we run both stochastic and deterministic model simulations based on the same fault geometries and stress interactions. Our results indicate that the stochastic simulators are able to reproduce the first-order characteristics of the major earthquakes on isolated faults as well as for coupled faults with moderate stress interactions. However, we find that all tested statistical models fail to reproduce the characteristics of strongly coupled faults, because multisegment rupturing resulting from a spatiotemporally correlated stress field is underestimated in the stochastic simulators. Our results suggest that stochastic models have to be extended by multirupture probability distributions to provide more reliable results.
Reliable estimations of magnitude of completeness (M-c) are essential for a correct interpretation of seismic catalogues. The spatial distribution of M-c may be strongly variable and difficult to assess in mining environments, owing to the presence of galleries, cavities, fractured regions, porous media and different mineralogical bodies, as well as in consequence of inhomogeneous spatial distribution of the seismicity. We apply a 3-D modification of the probabilistic magnitude of completeness (PMC) method, which relies on the analysis of network detection capabilities. In our approach, the probability to detect an event depends on its magnitude, source receiver Euclidian distance and source receiver direction. The suggested method is proposed for study of the spatial distribution of the magnitude of completeness in a mining environment and here is applied to a 2-months acoustic emission (AE) data set recorded at the Morsleben salt mine, Germany. The dense seismic network and the large data set, which includes more than one million events, enable a detailed testing of the method. This method is proposed specifically for strongly heterogeneous media. Besides, it can also be used for specific network installations, with sensors with a sensitivity, dependent on the direction of the incoming wave (e.g. some piezoelectric sensors). In absence of strong heterogeneities, the standards PMC approach should be used. We show that the PMC estimations in mines strongly depend on the source receiver direction, and cannot be correctly accounted using a standard PMC approach. However, results can be improved, when adopting the proposed 3-D modification of the PMC method. Our analysis of one central horizontal and vertical section yields a magnitude of completeness of about M-c approximate to 1 (AE magnitude) at the centre of the network, which increases up to M-c approximate to 4 at further distances outside the network; the best detection performance is estimated for a NNE-SSE elongated region, which corresponds to the strike direction of the low-attenuating salt body. Our approach provides us with small-scale details about the capability of sensors to detect an earthquake, which can be linked to the presence of heterogeneities in specific directions. Reduced detection performance in presence of strong structural heterogeneities (cavities) is confirmed by synthetic waveform modelling in heterogeneous media.
Various techniques are utilized by the seismological community, extractive industries, energy and geoengineering companies to identify earthquake nucleation processes in close proximity to engineering operation points. These operations may comprise fluid extraction or injections, artificial water reservoir impoundments, open pit and deep mining, deep geothermal power generations or carbon sequestration. In this letter to the editor, we outline several lines of investigation that we suggest to follow to address the discrimination problem between natural seismicity and seismic events induced or triggered by geoengineering activities. These suggestions have been developed by a group of experts during several meetings and workshops, and we feel that their publication as a summary report is helpful for the geoscientific community. Specific investigation procedures and discrimination approaches, on which our recommendations are based, are also published in this Special Issue (SI) of Journal of Seismology.
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.