A probabilistic seismic hazard assessment for the Turkish territory: part II—fault source and background seismicity model

Demircioğlu, M. B.; Şeşetyan, Karin; Duman, Tamer Y.; Çan, Tolga; Tekin, Senem; Ergintav, Semih

doi:10.1007/s10518-017-0130-x

Cited by 37 publications

(15 citation statements)

References 77 publications

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“…Seismic risk assessments have been made in previous studies (e.g. Demircioğlu et al, 2017;Akkar et al 2018) and the region between the NEAF and the NAF in the south of the Eastern Pontides was assessed in the medium to high risk group.…”

Section: Evaluation Of the Seismic Activity Of The Eastern Pontidesmentioning

confidence: 99%

Geomorphic evidence for active tectonic deformation in the coastal part of Eastern Black Sea, Eastern Pontides, Turkey

et al. 2018

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The Eastern Pontides (EP), which is the under transpressional deformation zone, is an active mountain belt that has been rising rapidly since the Cenozoic era because of the Arabian-Eurasian convergence. Morphometric studies have been performed to investigate the tectonic activity of this region and better understand the characteristics of the faults geomorphologically; the faults control the mountain fronts in the drainage basin of the EP. The results show the Hypsometric Curve (HC)-Hypsometric Integral (0.37-HI-0.67), Basin-Shaped Analysis (1.2-B s-7), Valley-Floor-Width to Height-Ratio (0.4-V f-1.2) and Asymmetry Factor (35-AF-81) applied to 46 drainage basins together with 9 tectonically controlled geomorphic indices (1.2-S mf-1.5) and a Stream Length Gradient (30-S L-120) indicate that the EP is tectonically active, and when the areas are evaluated according to S mf and V f analyses, the tectonic level is relatively high. According to our conceptual model for the uplifting of the EP, with respect to field studies and morphometric analysis, (i) the EP is the active deformation zone and has a "push-up" geometry in conjunction with the North Anatolian Fault; (ii) the EP is progressively uplifting at a rate of more than 0.5 mm/yr in along with the thrust faults of the Black Sea Fault (BSF) and Borjomi-Kazbegi Fault (BKF).

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Section: Evaluation Of the Seismic Activity Of The Eastern Pontidesmentioning

confidence: 99%

Geomorphic evidence for active tectonic deformation in the coastal part of Eastern Black Sea, Eastern Pontides, Turkey

et al. 2018

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“…The updated seismic hazard maps of Turkey, developed in connection with the new seismic design code, offer another source of comparison for the results of different modelling approaches. In the mentioned study, the design basis ground motions are obtained via a 50/50 combination of an area source model [72] and a fault source and smoothed seismicity model [73]. As the area source modelling tends to spread the hazard over the entire source area, our results remain higher around the main traces of the faults segments and generally lower over the background zones.…”

Section: Discussionmentioning

confidence: 93%

“…In green: negative amplifications. Figure 28 presents a comparison of the 475-year PGA distribution of our Poisson model with the mean 475-year PGA from the updated PSHA model of Turkey [71][72][73]. It would be difficult to provide a full description of the source modelling in the latter; however, we may say that the new Turkish seismic hazard model is composed of two independent source models, an area source model (assigned 50% weight in the final logic tree) and fault source and smoothed seismicity model (again weighted as 50% in the final logic tree).…”

Section: Discussion Of the Results And Comparisons With Other Studiesmentioning

confidence: 99%

“…Based on the evaluation of regional earthquake ground motion recordings and statistical ground motion prediction equation (GMPE) selection methods, [70] proposed an up-to-date logic tree for the characterisation of the ground motion in Turkey, which was later used in the update of the Turkish national seismic hazard maps [71][72][73]. As the main focus of the present study is an update of the source characterization in the Marmara region, we adopt for all source models (fault and background) the GMPE logic tree proposed by [70] for active shallow crustal regions, which consists of four models and associated weights as presented in Table 5.…”

Section: Ground Motion Characterisation and Logic Treementioning

confidence: 99%

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Evaluation of The Seismic Hazard in The Marmara Region (Turkey) Based on Updated Databases

Şeşetyan

Tümsa²,

Akinci

2019

Geosciences

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The increase in the wealth of information on the seismotectonic structure of the Marmara region after two devastating earthquakes (M7.6 Izmit and M7.2 Duzce events) in the year 1999 opened the way for the reassessment of the probabilistic seismic hazard in the light of new datasets. In this connection, the most recent findings and outputs of different national and international projects concerning seismicity and fault characterization in terms of geometric and kinematic properties are exploited in the present study to build an updated seismic hazard model. A revised fault segmentation model, alternative earthquake rupture models under a Poisson and renewal assumptions, as well as recently derived global and regional ground motion prediction equations (GMPEs) are put together in the present model to assess the seismic hazard in the region. Probabilistic seismic hazard assessment (PSHA) is conducted based on characteristic earthquake modelling for the fault segments capable of producing large earthquakes and smoothed seismicity modelling for the background smaller magnitude earthquake activity. The time-independent and time-dependent seismic hazard results in terms of spatial distributions of three ground-shaking intensity measures (peak ground acceleration, PGA, and 0.2 s and 1.0 s spectral accelerations (SA) on rock having 10% and 2% probabilities of exceedance in 50 years) as well as the corresponding hazard curves for selected cities are shown and compared with previous studies.

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“…The exceedance probability of 2 % in 50 years was determined for each branch of a logic tree for the given properties. Reference [13] used the art technologies and knowledge of the active fault, earthquake database, and ground motion prediction equations to develop a seismic source zonation model for PSHA. The model is called spatial smoothing seismic source model (FSBCK).…”

Section: Figurementioning

confidence: 99%

Active Probability Backpropagation Neural Network Model for Monthly Prediction of Probabilistic Seismic Hazard Analysis in Taiwan

Lin

Chiou

2019

IEEE Access

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In this study, an active probability backpropagation neural network model (PBNNM) was built by training a backpropagation neural network (BPNN) to predict the probability distribution of the probabilistic seismic hazard analysis (PSHA) monthly. The four-layered BPNN framework was determined using training data that were obtained from an earthquake catalogue for the time period of 1990-2015 (Taiwan Standard Time, TST). The studied region was divided into 500 small grids, each 0.2 • × 0.2 • in size, which is approximately 20 × 20 km 2. Each grid was assigned a predicted earthquake occurrence probability for a month between 2015 and 2018 by the PBNNM. The PBNNM successfully predicted the Tainan earthquake (2 February 2016 TST) and Hualien earthquake (2 February 2018 TST) with probabilities of 94% and 95%, respectively. A quantitative analysis of the reliability of the PBNNM, the standard error of the mean (SEM), and the normalised mean square error (NMSE) were used as statistical approaches to evaluate predicted probability errors of the PBNNM. The SEM (1.79) and NMSE (1.45) of 2015 were the inside test and within the time period of the training data. The SEM (2.11) and NMSE (2.03) of 2016, the low SEM (2.17) of 2017, the NMSE (2.13) and SEM (2.17) of 2017, and the SEM (1.71) and NMSE (1.32) of 2018 were the outside tests and not within the time period of the training data. These low SEM and NMSE values confirmed the accuracy of the PBNNM. In addition, the PBNNM does not consider the prerequisite conditions of past studies (e.g., the return period and assumed probability density model). Therefore, the PBNNM can be commercialised with relatively low cost and minimal resources and equipment compared with the methods presented in previous studies; only earthquake catalogues would be necessary. INDEX TERMS Active probability backpropagation neural network model (PBNNM), probabilistic seismic hazard analysis (PSHA), standard error of the mean (SEM), normalized mean square error (NMSE).

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A probabilistic seismic hazard assessment for the Turkish territory: part II—fault source and background seismicity model

Cited by 37 publications

References 77 publications

Geomorphic evidence for active tectonic deformation in the coastal part of Eastern Black Sea, Eastern Pontides, Turkey

Geomorphic evidence for active tectonic deformation in the coastal part of Eastern Black Sea, Eastern Pontides, Turkey

Evaluation of The Seismic Hazard in The Marmara Region (Turkey) Based on Updated Databases

Active Probability Backpropagation Neural Network Model for Monthly Prediction of Probabilistic Seismic Hazard Analysis in Taiwan

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