Considering potential seismic sources in earthquake hazard assessment for Northern Iran

Abdollahzadeh, Gholamreza; Sazjini, Mohammad; Shahaky, Mohsen; Tajrishi, Fatemeh Zahedi; Khanmohammadi, Leila

doi:10.1007/s10950-013-9412-1

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…The veryhigh-to-high hazard levels may be due to seismic amplification in loose sedimentary rocks, active faults, and complicated geo-tectonics. Abdollahzadeh et al [51] conducted a PSHA by considering potential seismic sources in northern Iran and estimated PGA values of 0.3 g for 475-and 0.5 g to 0.75 g for 2475-year return periods. Grunthal [52] conducted a seismic-hazard assessment in Europe and estimated a PGA of 0.6 g for a 10% exceedance rate over 50 years.…”

Section: Discussionmentioning

confidence: 99%

An Integration of Deep Learning and Transfer Learning for Earthquake-Risk Assessment in the Eurasian Region

et al. 2023

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The problem of estimating earthquake risk is one of the primary themes for researchers and investigators in the field of geosciences. The combined assessment of spatial probability and the determination of earthquake risk at large scales is challenging. To the best of the authors’ knowledge, there no updated earthquake-hazard-and-risk assessments for the Eurasia region have been published since 1999. Considering that Eurasia is characterized by a seismically active Alpine–Himalayan fault zone and the Pacific Ring of Fire, which are frequently affected by devastating events, a continental-scale risk assessment for Eurasia is necessary to check the global applicability of developed methods and to update the earthquake-hazard, -vulnerability, and -risk maps. The current study proposes an integrated deep-transfer-learning approach called the gated recurrent unit–simple recurrent unit (GRU–SRU) to estimate earthquake risk in Eurasia. In this regard, the GRU model estimates the spatial probability, while the SRU model evaluates the vulnerability. To this end, spatial probability assessment (SPA), and earthquake-vulnerability assessment (EVA) results were integrated to generate risk A, while the earthquake-hazard assessment (EHA) and EVA were considered to generate risk B. This research concludes that in the case of earthquake-risk assessment (ERA), the results obtained for Risk B were better than those for risk A. Using this approach, we also evaluated the stability of the factors and interpreted the interaction values to form a spatial prediction. The accuracy of our proposed integrated approach was examined by means of a comparison between the obtained deep learning (DL)-based results and the maps generated by the Global Earthquake Model (GEM). The accuracy of the SPA was 93.17%, while that of the EVA was 89.33%.

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Section: Discussionmentioning

confidence: 99%

An Integration of Deep Learning and Transfer Learning for Earthquake-Risk Assessment in the Eurasian Region

et al. 2023

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“…As stated before Tavakoli and Ghafory (1999) divided Iran into several seismotectonic subdivision and reported the value of b parameters around 0.6±0.04 to 0.7±0.07 for subdivision number 15 that include Guilan region and the maximum expected magnitude of earthquake around 7.9±0.3 Richter in 1999 for the area of Guilan province [2]. [4]. They reported 505 declustered events, including 344 events in Alborz-Azarbayejan and 161 events in Kope Dagh seismotectonic.…”

Section: Figure 10 Probability Of Occurrence Of An Earthquake -Ms Fomentioning

confidence: 99%

“…During the last several decades, thousands of the people lost their lives, and severe economic impacts and social damages were experienced in this province. For instance, at 21 PM on June 1990 a destructive earthquake occurred in Guilan province in Rudbar city with moment magnitude of 7.4 Richter scale which induced more than 40,000 people lost their lives, more than 500,000 became homeless, nearly 100,000 buildings were destroyed and 700 villages were demolished [3,4]. Each earthquake event can highlight the poor performance of structures, lack of knowledge about earthquake hazards and sometimes inefficiency of building codes.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Earthquake Catalogs and Seismicity Parameters from Incomplete Earthquake Catalogs of Guilan Region, Iran

Ghorbani

Izadi

2017

Civ Eng J

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Statistics of human losses and financial casualties in Guilan province as one of the most populated and strategic areas in the north of Iran have doubled the importance of having knowledge about earthquake and strategies to reduce its effect. In order to investigate seismic hazard analysis, earthquake records along with selecting the proper distance of intended locations were gathered to make Poissonian catalogs. The earthquake catalogs cover the geographical area limited to 35.0°-39.3°N, 47.1-52.2°E and include around 4,000 earthquake events between the years of 855 to 2016. An extensive amount of efforts and times are required to eliminate duplicated events, to unify the magnitude scales and to cluster the earthquake sequences with variable windows in time and location domains to remove aftershocks and foreshocks. The Final homogenous catalog consists of around 110 events for each region. Magnitude of completeness in different time intervals is reported for Guilan region. Seismicity parameters were achieved using Gutenberg-Richter method by Zmap and Kijko-Sellovell approaches for important cities of Guilan including Rasht, Anzali, Rudbar, and Lahijan. Comparative analysis of the results from Zmap and Kijko-Sellovell approaches shows good consistency in the estimation of seismic parameters with the result of literature.

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“…Up to now, several researchers have built PSHA models for the Iranian Plateau with different assumptions (Shabani and Mirzaei 2007, Abdollahzadeh et al 2014b, Khodaverdian et al 2016b, Khoshnevis et al 2017. Khodaverdian et al (2016b) built a catalog-based seismic hazard model for Iran using the smoothed seismicity approach.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic deformation-based seismic hazard model for Iran

Lotfi

Zafarani²,

Khodaverdian³

2022

Bull Earthquake Eng

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Probabilistic seismic hazard analysis, as the most prevalent approach to evaluate earthquake hazard, is commonly based on earthquake catalogs. Although previous studies show that the recurrence time of large-magnitude (Mw > 7.0) events in Iran is more than ~1000-2000 years, the available instrumentally recorded earthquakes are limited to less than 100 years. With the idea of having another proxy for seismicity rates, we propose a methodology to evaluate activity rates from strain rates in a combination of regional estimates of  and max m .Considering the comprehensive deformation model of the Iranian Plateau, we found that the deformation-based occurrence rates are more than the catalog-based occurrence rates in all seismotectonic provinces in the Iranian Plateau. Except for Central Iran, the ratios of the deformation-based to the catalog-based occurrence rate are between 1.21-3.95. For the first time, a probabilistic deformation-based seismic hazard model for the Iranian plateau is also developed and Peak Ground Acceleration (PGA) values for 10% and 2% POEs in 50 years are estimated. The highest levels of PGA are found in the Azerbaijan and Alborz seismotectonic provinces, where the highest value of strain rates is located. The spatial averages of PGA with 10% POE in 50 years from the deformation-based model are up to 30% higher than the results of a traditional probabilistic seismic hazard for almost all provinces except the Zagros region.

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Considering potential seismic sources in earthquake hazard assessment for Northern Iran

Cited by 11 publications

References 19 publications

An Integration of Deep Learning and Transfer Learning for Earthquake-Risk Assessment in the Eurasian Region

An Integration of Deep Learning and Transfer Learning for Earthquake-Risk Assessment in the Eurasian Region

Comprehensive Earthquake Catalogs and Seismicity Parameters from Incomplete Earthquake Catalogs of Guilan Region, Iran

A probabilistic deformation-based seismic hazard model for Iran

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