scholarly journals Capturing the Uncertainty of Seismic Activity Rates in Probabilistic Seismic‐Hazard Assessments

2015 ◽  
Vol 105 (2A) ◽  
pp. 580-589 ◽  
Author(s):  
D. Stromeyer ◽  
G. Grünthal
Keyword(s):  
Seismic Hazard ◽  
Seismic Activity ◽  
Probabilistic Seismic Hazard ◽  
Hazard Assessments

scholarly journals Integrating faults and past earthquakes into a probabilistic seismic hazard model for peninsular Italy

2017 ◽  
Vol 17 (11) ◽  
pp. 2017-2039 ◽  
Author(s):  
Alessandro Valentini ◽  
Francesco Visini ◽  
Bruno Pace
Keyword(s):  
Seismic Hazard ◽  
Seismic Activity ◽  
Grid Point ◽  
Active Faults ◽  
Gaussian Model ◽  
Probabilistic Seismic Hazard ◽  
Maximum Magnitude ◽  
Slip Rates ◽  
Earthquake Sources ◽  
The Impact

Abstract. Italy is one of the most seismically active countries in Europe. Moderate to strong earthquakes, with magnitudes of up to ∼ 7, have been historically recorded for many active faults. Currently, probabilistic seismic hazard assessments in Italy are mainly based on area source models, in which seismicity is modelled using a number of seismotectonic zones and the occurrence of earthquakes is assumed uniform. However, in the past decade, efforts have increasingly been directed towards using fault sources in seismic hazard models to obtain more detailed and potentially more realistic patterns of ground motion. In our model, we used two categories of earthquake sources. The first involves active faults, and using geological slip rates to quantify the seismic activity rate. We produced an inventory of all fault sources with details of their geometric, kinematic, and energetic properties. The associated parameters were used to compute the total seismic moment rate of each fault. We evaluated the magnitude–frequency distribution (MFD) of each fault source using two models: a characteristic Gaussian model centred at the maximum magnitude and a truncated Gutenberg–Richter model. The second earthquake source category involves grid-point seismicity, with a fixed-radius smoothed approach and a historical catalogue were used to evaluate seismic activity. Under the assumption that deformation is concentrated along faults, we combined the MFD derived from the geometry and slip rates of active faults with the MFD from the spatially smoothed earthquake sources and assumed that the smoothed seismic activity in the vicinity of an active fault gradually decreases by a fault-size-driven factor. Additionally, we computed horizontal peak ground acceleration (PGA) maps for return periods of 475 and 2475 years. Although the ranges and gross spatial distributions of the expected accelerations obtained here are comparable to those obtained through methods involving seismic catalogues and classical zonation models, the spatial pattern of the hazard maps obtained with our model is far more detailed. Our model is characterized by areas that are more hazardous and that correspond to mapped active faults, while previous models yield expected accelerations that are almost uniformly distributed across large regions. In addition, we conducted sensitivity tests to determine the impact on the hazard results of the earthquake rates derived from two MFD models for faults and to determine the relative contributions of faults versus distributed seismic activity. We believe that our model represents advancements in terms of the input data (quantity and quality) and methodology used in the field of fault-based regional seismic hazard modelling in Italy.

Determination of the Spatial Distribution Function of Bohai Seismic Tectonic Area

2014 ◽  
Vol 1065-1069 ◽  
pp. 1530-1534
Author(s):  
Zhuo Juan Xie ◽  
Yue Jun Lu
Keyword(s):  
Distribution Function ◽  
Spatial Distribution ◽  
Seismic Hazard ◽  
Seismic Activity ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Spatial Distribution Function

The spatial distribution function assigns seismicity parameters of seismic zone and belt to the potential earthquake source area seismic by the magnitude interval, determination of the spatial distribution function is one of the key technologies to comprehensive probabilistic seismic hazard analysis methods, and the results will directly influence seismic hazard analysis results of the calculated sites. However, the spatial distribution functions are hard to get by statistics due to serious lack of seismic samples, and evaluation factors used in the actual work are too complex and not independent, the spatial distribution function is lack of reliability. In this paper, Bohai seismic tectonic area is chosen as an example; geological data are obtained from the newly built offshore oil platform engineering in recent decades, while seismic activity data are obtained by checking from historical difficult seismic parameters. Five factors are adopted in this area, including potential seismic focus area, seismic tectonic, seismic activity level, long-term forecasts, and strong earthquake recurrence interval and construct empty segment. Spatial distribution function of Bohai Sea earthquake structure area is obtained by weighted comprehension; it reflects the spatial and temporal heterogeneity of seismic activities in the area, and provides the calculation parameters for the probabilistic seismic hazard analysis.

scholarly journals Integrating faults and past earthquakes into a probabilistic seismic hazard model for peninsular Italy

2017 ◽  
Author(s):  
Alessandro Valentini ◽  
Francesco Visini ◽  
Bruno Pace
Keyword(s):  
Seismic Hazard ◽  
Seismic Activity ◽  
Active Faults ◽  
Gaussian Model ◽  
Probabilistic Seismic Hazard ◽  
Maximum Magnitude ◽  
Ground Acceleration ◽  
Slip Rates ◽  
Earthquake Sources ◽  
The Impact

Abstract. Italy is one of the most seismically active countries in Europe. Moderate to strong earthquakes, with magnitudes of up to ~ 7, have been recorded on many of active faults in historical times. Currently, probabilistic seismic hazard assessments in Italy are mainly based on area source models, in which the seismicity is modelled on a number of seismotectonic zones and the occurrence of earthquakes is assumed to be uniform. However, in the last decade, efforts have increasingly been directed towards using fault sources in seismic hazard models to obtain more detailed and possibly more realistic patterns of ground motion. In our model, we used two categories of earthquake sources. The first involves active faults, and fault slip rates were used to quantify the seismic activity rate. We produced an inventory of all fault sources, with details on their geometric, kinematic and energetic properties. The parameters are used to compute the total seismic moment rate for each fault. We evaluated the magnitude-frequency distributions of each fault source using two models, a characteristic Gaussian model centred on the maximum magnitude and a Truncated Gutenberg-Richter model. The second earthquake source category involves distributed seismicity, and a fixed-radius smoothed approach and a historical catalogue were used to evaluate seismic activity. Under the assumption that deformation is concentrated along faults, we combined the earthquakes derived from the geometry and slip rates of active faults with the earthquakes from the spatially smoothed earthquake sources and assumed that the smoothed seismic activity in the vicinity of an active fault gradually decreases by a fault-size driven factor. We computed horizontal peak ground acceleration maps for return periods of 475 and 2,475 yr. Although the range and gross spatial distribution of the expected accelerations obtained here are comparable to those obtained through methods involving seismic catalogues and classical zonation models, the spatial pattern of our model is far more detailed. Our model is characterized by areas that are more hazardous and that correspond to mapped active faults, while the previous models yield expected accelerations that are almost uniformly distributed across large regions. In addition, we conducted sensitivity tests to determine the impact on the hazard results of the earthquake rates derived from two magnitude-frequency distribution models for faults and to determine the relative contributions of faults versus distributed seismic activity. We think our model represents an advance for Italy in terms of input data (quantity and quality) and methodology in the field of the fault-based regional seismic hazard modelling.

scholarly journals Probabilistic seismic hazard assessments of Sabah, east Malaysia: accounting for local earthquake activity near Ranau

2017 ◽  
Vol 15 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Amin E Khalil ◽  
Ismail A Abir ◽  
Hanteh Ginsos ◽  
Hesham E Abdel Hafiez ◽  
Sohail Khan
Keyword(s):  
Seismic Hazard ◽  
Probabilistic Seismic Hazard ◽  
Earthquake Activity ◽  
East Malaysia ◽  
Local Earthquake ◽  
Hazard Assessments

PEMETAAN DAERAH RENTAN GEMPA BUMI SEBAGAI DASAR PERENCANAAN TATA RUANG DAN WILAYAH DI PROVINSI SULAWESI BARAT

KURVATEK ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 41-47
Author(s):  
Marinda noor Eva
Keyword(s):  
Seismic Hazard ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard

Penelitian mengenai daerah rawan gempa bumi ini menggunakan Metode Probabilistic Seismic Hazard Analysis (PSHA) di Provinsi Sulawesi Barat, dengan tujuan untuk memetakan tingkat kerawanan bahaya gempa bumi di Kabupaten Mamasa. Penelitian ini menggunakan data kejadian gempa bumi di Pulau Sulawesi dan sekitarnya dari tahun 1900 – 2015. Hasil pengolahan PSHA menggunakan Software Ez-Frisk 7.52 yang menghasilkan nilai hazard di batuan dasar pada kondisi PGA (T = 0,0 sekon), dengan periode ulang 500 tahun dan 2500 tahun berkisar antara (149,54 – 439,45) gal dan (287,18 – 762,81) gal. Nilai hazard di batuan dasar dengan kondisi spektra T = 0,2 sekon untuk periode ulang 500 tahun dan 2500 tahun adalah (307,04 – 1010,90) gal dan (569,48 – 1849,78) gal. Nilai hazard di batuan dasar dengan kondisi spektra T = 1,0 sekon untuk periode ulang 500 tahun dan 2500 tahun diperoleh nilai (118,01 – 265,75) gal dan (223,74 – 510,92) gal. Berdasarkan analisis PSHA, nilai PGA di Provinsi Sulawesi Barat dominan dipengaruhi oleh sumber gempa sesar.

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