scholarly journals Time-Dependent Seismic Hazard Analysis for Induced Seismicity: The Case of St Gallen (Switzerland), Geothermal Field

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2747
Author(s):  
Vincenzo Convertito ◽  
Hossein Ebrahimian ◽  
Ortensia Amoroso ◽  
Fatemeh Jalayer ◽  
Raffaella De Matteis ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Induced Seismicity ◽  
Hazard Analysis ◽  
Geothermal Field ◽  
Seismic Hazard Analysis ◽  
Time Dependent ◽  
Recurrence Time ◽  
Maximum Magnitude ◽  
Small Magnitude

Reliable seismic hazard analyses are crucial to mitigate seismic risk. When dealing with induced seismicity the standard Probabilistic Seismic Hazard Analysis (PSHA) has to be modified because of the peculiar characteristics of the induced events. In particular, the relative shallow depths, small magnitude, a correlation with field operations, and eventually non-Poisson recurrence time. In addition to the well-known problem of estimating the maximum expected magnitude, it is important to take into account how the industrial field operations affect the temporal and spatial distribution of the earthquakes. In fact, during specific stages of the project the seismicity may be hard to be modelled as a Poisson process—as usually done in the standard PSHA—and can cluster near the well or migrate toward hazardous known or—even worse—not known faults. Here we present a technique in which we modify the standard PSHA to compute time-dependent seismic hazard. The technique allows using non-Poisson models (BPT, Weibull, gamma and ETAS) whose parameters are fitted using the seismicity record during distinct stages of the field operations. As a test case, the procedure has been implemented by using data recorded at St. Gallen deep geothermal field, Switzerland, during fluid injection. The results suggest that seismic hazard analyses, using appropriate recurrence model, ground motion predictive equations, and maximum magnitude allow the expected ground-motion to be reliably predicted in the study area. The predictions can support site managers to decide how to proceed with the project avoiding adverse consequences.

scholarly journals Seismic hazard for the Trans Adriatic Pipeline (TAP). Part 1: probabilistic seismic hazard analysis along the pipeline

2021 ◽  
Author(s):  
D. Slejko ◽  
A. Rebez ◽  
M. Santulin ◽  
J. Garcia-Pelaez ◽  
D. Sandron ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Test Site ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Maximum Magnitude ◽  
Low Frequencies ◽  
The North

AbstractThe design of critical facilities needs a targeted computation of the expected ground motion levels. The Trans Adriatic Pipeline (TAP) is the pipeline that transports natural gas from the Greek-Turkish border, through Greece and Albania, to Italy. We present here the probabilistic seismic hazard analysis (PSHA) that we performed for this facility, and the deaggregation of the results, aiming to identify the dominant seismic sources for a selected site along the Albanian coast, where one of the two main compressor stations is located. PSHA is based on an articulated logic tree of twenty branches, consisting of two models for source, seismicity, estimation of the maximum magnitude, and ground motion. The area with the highest hazard occurs along the Adriatic coast of Albania (PGA between 0.8 and 0.9 g on rock for a return period of 2475 years), while strong ground motions are also expected to the north of Thessaloniki, Kavala, in the southern Alexandroupolis area, as well as at the border between Greece and Turkey. The earthquakes contributing most to the hazard of the test site at high and low frequencies (1 and 5 Hz) and the corresponding design events for the TAP infrastructure have been identified as local quakes with MW 6.6 and 6.0, respectively.

scholarly journals A ground motion logic tree for seismic hazard analysis in the stable cratonic region of Europe: regionalisation, model selection and development of a scaled backbone approach

2020 ◽  
Vol 18 (14) ◽  
pp. 6119-6148
Author(s):  
Graeme Weatherill ◽  
Fabrice Cotton
Keyword(s):  
United States ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Eastern United States ◽  
Logic Tree ◽  
Short Period ◽  
Northeastern Europe

Abstract Regions of low seismicity present a particular challenge for probabilistic seismic hazard analysis when identifying suitable ground motion models (GMMs) and quantifying their epistemic uncertainty. The 2020 European Seismic Hazard Model adopts a scaled backbone approach to characterise this uncertainty for shallow seismicity in Europe, incorporating region-to-region source and attenuation variability based on European strong motion data. This approach, however, may not be suited to stable cratonic region of northeastern Europe (encompassing Finland, Sweden and the Baltic countries), where exploration of various global geophysical datasets reveals that its crustal properties are distinctly different from the rest of Europe, and are instead more closely represented by those of the Central and Eastern United States. Building upon the suite of models developed by the recent NGA East project, we construct a new scaled backbone ground motion model and calibrate its corresponding epistemic uncertainties. The resulting logic tree is shown to provide comparable hazard outcomes to the epistemic uncertainty modelling strategy adopted for the Eastern United States, despite the different approaches taken. Comparison with previous GMM selections for northeastern Europe, however, highlights key differences in short period accelerations resulting from new assumptions regarding the characteristics of the reference rock and its influence on site amplification.

Ranking of Ground-Motion Models (GMMs) for Use in Probabilistic Seismic Hazard Analysis for Iran Based on an Independent Data Set

2020 ◽  
Author(s):  
Zoya Farajpour ◽  
Milad Kowsari ◽  
Shahram Pezeshk ◽  
Benedikt Halldorsson
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Selection Process ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Data Set ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT We apply three data-driven selection methods, log-likelihood (LLH), Euclidean distance-based ranking (EDR), and deviance information criterion (DIC), to objectively evaluate the predictive capability of 10 ground-motion models (GMMs) developed from Iranian and worldwide data sets against a new and independent Iranian strong-motion data set. The data set includes, for example, the 12 November 2017 Mw 7.3 Ezgaleh earthquake and the 25 November 2018 Mw 6.3 Sarpol-e Zahab earthquake and includes a total of 201 records from 29 recent events with moment magnitudes 4.5≤Mw≤7.3 with distances up to 275 km. The results of this study show that the prior sigma of the GMMs acts as the key measure used by the LLH and EDR methods in the ranking against the data set. In some cases, this leads to the resulting model bias being ignored. In contrast, the DIC method is free from such ambiguity as it uses the posterior sigma as the basis for the ranking. Thus, the DIC method offers a clear advantage of partially removing the ergodic assumption from the GMM selection process and allows a more objective representation of the expected ground motion at a specific site when the ground-motion recordings are homogeneously distributed in terms of magnitudes and distances. The ranking results thus show that the local models that were exclusively developed from Iranian strong motions perform better than GMMs from other regions for use in probabilistic seismic hazard analysis in Iran. Among the Next Generation Attenuation-West2 models, the GMMs by Boore et al. (2014) and Abrahamson et al. (2014) perform better. The GMMs proposed by Darzi et al. (2019) and Farajpour et al. (2019) fit the recorded data well at short periods (peak ground acceleration and pseudoacceleration spectra at T=0.2  s). However, at long periods, the models developed by Zafarani et al. (2018), Sedaghati and Pezeshk (2017), and Kale et al. (2015) are preferable.

Sign in / Sign up

Export Citation Format

Share Document