Error propagation in time-dependent probability of occurrence for characteristic earthquakes in Italy (original) (raw)
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Journal of Seismology, 2010
Using the characteristic earthquake model, we calculate the probability of occurrence of earthquakes M w > 5.5 for individual fault sources in the Central Apennines for the 30-year period (2007-2037). We show the effect of timedependent and time-independent occurrence (Brownian passage time (BPT) and Poisson) models together with uncertain slip rates and uncertain maximum magnitudes and, hence, uncertain recurrence times. In order to reduce the large prior geological slip rate uncertainty distribution for most faults, we obtain a posterior slip rate uncertainty distribution using a likelihood function obtained from regional historical seismicity. We assess the uncertainty of maximum magnitude by assuming that the uncertainty in fault width and length are described by a normal distribution with standard deviation equal to ±20% of the mean values. We then estimate the uncertainties of the 30-year probability of occurrence of a Elapsed time ratio (Tlapse/Tbar) Ratio of the Probability of ocurrences Prob( 0.3) / Prob(0.7) Prob( 0.3) / Prob(0.5) Prob( 0.3) / Prob(Poisson)
Renewal models for earthquake predictability
Journal of Seismology, 2010
The new Database of Italy's Seismogenic Sources (Basili et al. 2008) identifies areas with a degree of homogeneity in earthquake generation mechanism judged sufficiently high. Nevertheless, their seismic sequences show rather long and regular interoccurrence times mixed with irregularly distributed short interoccurrence times. Accordingly, the following question could naturally arise: do sequences consist of nearly periodic events perturbed by a kind of noise; are they Poissonian; or short interoccurrence times predominate like in a cluster model? The relative reliability of these hypotheses is at present
Probabilistic approach to earthquake prediction
The evaluation of any earthquake forecast hypothesis requires the application of rigorous statistical methods. It implies a univocal definition of the model characterising the concerned anomaly or precursor, so as it can be objectively recognised in any circumstance and by any observer. A valid forecast hypothesis is expected to maximise successes and minimise false alarms. The probability gain associated to a precursor is also a popular way to estimate the quality of the predictions based on such precursor. Some scientists make use of a statistical approach based on the computation of the likelihood of an observed realisation of seismic events, and on the comparison of the likelihood obtained under different hypotheses. This method can be extended to algorithms that allow the computation of the density distribution of the conditional probability of earthquake occurrence in space, time and magnitude. Whatever method is chosen for building up a new hypothesis, the final assessment of its validity should be carried out by a test on a new and independent set of observations. The implementation of this test could, however, be problematic for seismicity characterised by long-term recurrence intervals. Even using the historical record, that may span time windows extremely variable between a few centuries to a few millennia, we have a low probability to catch more than one or two events on the same fault. Extending the record of earthquakes of the past back in time up to several millennia, paleoseismology represents a great opportunity to study how earthquakes recur through time and thus provide innovative contributions to time-dependent seismic hazard assessment. Sets of paleoseimologically dated earthquakes have been established for some faults in the Mediterranean area: the Irpinia fault in Southern Italy, the Fucino fault in Central Italy, the El Asnam fault in Algeria and the Skinos fault in Central Greece. By using the age of the paleoearthquakes with their associated uncertainty we have computed, through a Montecarlo procedure, the probability that the observed inter-event times come from a uniform random distribution (null hypothesis). This probability is estimated approximately equal to 8.4% for the Irpinia fault, 0.5% for the Fucino fault, 49% for the El Asnam fault and 42% for the Skinos fault. So, the null Poisson hypothesis can be rejected with a confidence level of 99.5% for the Fucino fault, but it can be rejected only with a confidence level between 90% and 95% for the Irpinia fault, while it cannot be rejected for the other two cases. As discussed in the last section of this paper, whatever the scientific value of any prediction hypothesis, it should be considered effective only after evaluation of the balance between the costs and benefits introduced by its practical implementation.
In this study, we show the effect of time-independent and time-dependent occurrence models on the seismic hazard estimations. The time-dependency is introduced by 1) the Brownian Passage Time (BPT) probability model that is based on a simple physical model of the earthquake cycle 2) the fusion of the BPT renewal model (BPT+∆CFF) with a physical model that considers the earthquake probability perturbation for interacting faults by static Coulomb stress changes. To do so, we calculate the probability of occurrence of earthquakes M w > 6.5 for individual fault sources in the Marmara region for the 5-10-30 and 50-year periods (starting from January 1, 2013). We treat the uncertainties in the fault parameters (e.g. slip rate, characteristic magnitude and aperiodicity) of the statistical distribution associated to each examined fault source by a Monte Carlo technique. Then the probabilities of occurrence for the next characteristic earthquake are calculated from three different models ...
Bulletin of The Seismological Society of America, 2009
We produce probabilistic seismic hazard assessments for the Central Apennines, Italy, using time-dependent models that are characterized using a Brownian Passage Time (BPT) recurrence model. Using aperiodicity parameters of 0.3, 0.5, and 0.7, we examine the sensitivity of the probabilistic ground motion and its deaggregation to these parameters. For the seismic source model we incorporate both smoothed historical seismicity and geological information on faults. We use the maximum magnitude model for the fault sources together with a uniform probability of rupture along the fault (floating fault model) for faults where earthquakes cannot be correlated with known geologic structural segmentation.
Probabilistic Assessment of Earthquake Recurrence in Northeast India and Adjoining Regions
2010
Northeast India and adjoining regions (20°-32°N and 87°-100°E) are highly vulnerable to earthquake hazard in the Indian sub-continent, which fall under seismic zones V, IV and III in the seismic zoning map of India with magnitudes M exceeding 8, 7 and 6, respectively. It has experienced two devastating earthquakes, namely, the Shillong Plateau earthquake of June 12, 1897 (M w 8.1) and the Assam earthquake of August 15, 1950 (M w 8.5) that caused huge loss of lives and property in the Indian sub-continent. In the present study, the probabilities of the occurrences of earthquakes with magnitude M C 7.0 during a specified interval of time has been estimated on the basis of three probabilistic models, namely, Weibull, Gamma and Lognormal, with the help of the earthquake catalogue spanning the period 1846 to 1995. The method of maximum likelihood has been used to estimate the earthquake hazard parameters. The logarithmic probability of likelihood function (ln L) is estimated and used to compare the suitability of models and it was found that the Gamma model fits best with the actual data. The sample mean interval of occurrence of such earthquakes is estimated as 7.82 years in the northeast India region and the expected mean values for Weibull, Gamma and Lognormal distributions are estimated as 7.837, 7.820 and 8.269 years, respectively. The estimated cumulative probability for an earthquake M C 7.0 reaches 0.8 after about 15-16 (2010-2011) years and 0.9 after about 18-20 (2013-2015) years from the occurrence of the last earthquake (1995) in the region. The estimated conditional probability also reaches 0.8 to 0.9 after about 13-17 (2008-2012) years in the considered region for an earthquake M C 7.0 when the elapsed time is zero years. However, the conditional probability reaches 0.8 to 0.9 after about 9-13 (2018-2022) years for earthquake M C 7.0 when the elapsed time is 14 years (i.e. 2009).
Long-term Earthquake Occurrence Models
2017
This study describes three earthquake occurrence models as applied to the whole Italian territory, to assess the occurrence probabilities of future (M ≥ 5.0) earthquakes: two as short-term (24 hour) models, and one as long-term (5 and 10 years). The first model for short-term forecasts is a purely stochastic epidemic type earthquake sequence (ETES) model. The second short-term model is an epidemic rate-state (ERS) forecast based on a model that is physically constrained by the application to the earthquake clustering of the Dieterich rate-state constitutive law. The third forecast is based on a long-term stress transfer (LTST) model that considers the perturbations of earthquake probability for interacting faults by static Coulomb stress changes. These models have been submitted to the Collaboratory for the Study of Earthquake Predictability (CSEP) for forecast testing for Italy (ETH Zurich), and they were locked down to test their validity on real data in a future setting starting from August 1, 2009.
A Time\Dependent Probabilistic Seismic Hazard Model For The Central Apennines (Italy)
2004
Earthquake hazard in the Central Apennines, Italy has been investigated using time-independent probabilistic (simple Poissonian) and time-dependent probabilistic (renewal) models. We developed a hazard model that defines the sources for potential earthquakes and earthquake recurrence relations. Both characteristic and floating earthquake hypothesismodel is used for the Central Apennines faults (M>5.9). The models for each fault segment are developed based on recent geological and geophysical studies, as well as historical earthquakes. Historical seismicity, active faulting framework and inferred seismogenic behavior (expressed in terms of slip rates, recurrence intervals, elapsed times) constitute the main quantitative information used in the model assignment. We calculate the background hazard from Mw 4.6-5.9 earthquakes using the historical catalogs of CPTI04 (Working Group, 2004) and obtain a-value distribution over the study area. This is because the earthquakes occur in areas where they cannot be assigned to a particular fault. Therefore, their recurrence is considered by the historic occurrence of earthquakes, calculating the magnitude-frequency distributions. We found good agreement between expected earthquake rates from historical earthquake catalog and earthquake source model. The probabilities are obtained from time-dependent models characterized by a Brownian Passage Time function on recurrence interval with aperiodicity of 0.5. Earthquake hazard is quantified in terms of peak ground acceleration and spectral accelerations for natural periods of 0.2 and 1.0 seconds. The ground motions are determined for rock conditions. We have used the attenuation relationships obtained for the Apennines by Malagnini et al. (2000) together with the relationships predicted from Sabetta and Pugliese (1996) and Ambraseys et al. (1996) for the Italian and European regions, respectively. Generally, time dependent hazard is increased and the peaks appear to shift to the ESE of the central Apennines with respect to the results of the Possionian source model. In order to present the most likely earthquake magnitude or the most likely source-site distance and determine predominant sources of seismic hazard we deaggreagted the seismic hazard for 1.0 Hz PSA and PGA in some cities across Central Apennines.