Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Summary Earthquake hypocenters are routinely obtained by a common inversion problem of P- and S-phase arrivals observed on a seismological network. Improving our understanding of the uncertainties associated with the hypocentral parameters is crucial for reliable seismological analysis, understanding of tectonic processes, and assessing seismic hazards. However, current methods often overlook uncertainties in velocity models and variable trade-offs during inversion. Here, we propose to unravel the effects of the main sources of uncertainty in the location process using techniques derived from the framework of global sensitivity analysis. These techniques provide a quantification of the effects of selected variables on the variance of the earthquake location using an iterative model that challenges the inversion scheme. Specifically, we consider the main and combined effects of (1) variable network geometry, (2) the presence of errors in analyst observations and (3) errors in velocity parameters from a 1D velocity model. These multiple sources of uncertainty are described by a dozen of random variables in our model. Using a Monte Carlo sampling approach, we explore the model configurations and analyze the differences between the initial reference location and 100,000 resulting hypocentral locations. The GSA approach using Sobol's variance decomposition allows us to quantify the relative importance of our choice of variables. It highlights the critical importance of the velocity model approximation and provides a new, objective and quantitative insight into understanding the sources of uncertainty in the inversion process.
Summary Earthquake hypocenters are routinely obtained by a common inversion problem of P- and S-phase arrivals observed on a seismological network. Improving our understanding of the uncertainties associated with the hypocentral parameters is crucial for reliable seismological analysis, understanding of tectonic processes, and assessing seismic hazards. However, current methods often overlook uncertainties in velocity models and variable trade-offs during inversion. Here, we propose to unravel the effects of the main sources of uncertainty in the location process using techniques derived from the framework of global sensitivity analysis. These techniques provide a quantification of the effects of selected variables on the variance of the earthquake location using an iterative model that challenges the inversion scheme. Specifically, we consider the main and combined effects of (1) variable network geometry, (2) the presence of errors in analyst observations and (3) errors in velocity parameters from a 1D velocity model. These multiple sources of uncertainty are described by a dozen of random variables in our model. Using a Monte Carlo sampling approach, we explore the model configurations and analyze the differences between the initial reference location and 100,000 resulting hypocentral locations. The GSA approach using Sobol's variance decomposition allows us to quantify the relative importance of our choice of variables. It highlights the critical importance of the velocity model approximation and provides a new, objective and quantitative insight into understanding the sources of uncertainty in the inversion process.
Probabilistic seismic hazard analysis (PSHA) is a methodology with a long history and has been widely implemented. However, in the conventional PSHA and sequence-based probabilistic seismic hazard analysis (SPSHA) approaches, the occurrence of mainshocks is modeled as the homogeneous Poisson process, which is unsuitable for large earthquakes. To account for the stationary occurrence of small-to-moderate (STM) mainshocks and the nonstationary behavior of large mainshocks, we propose a time-dependent sequence-based probabilistic seismic hazard analysis (TD-SPSHA) approach by combining the time-dependent mainshock probabilistic seismic hazard analysis (TD-PSHA) and aftershock probabilistic seismic hazard analysis, consisting of four components: (1) STM mainshocks, (2) aftershocks associated with STM mainshocks, (3) large mainshocks, and (4) aftershocks associated with large mainshocks. The approach incorporates an exponential-magnitude, exponential-time model for STM mainshocks, and a renewal-time, characteristic-magnitude model for large mainshocks to assess the time-dependent hazard for mainshocks. Then nonhomogeneous Poisson process is used to model the occurrence of associated aftershocks, in which the aftershock sequences can be modeled using the Reasenberg and Jones (RJ) model or the epidemic-type aftershock sequence (ETAS) model. To demonstrate the proposed TD-SPSHA approach, a representative site of the San Andreas fault is selected as a benchmark case, for which five time-dependent recurrence models, including normal, lognormal, gamma, Weibull, and Brownian passage time (BPT) distributions, are chosen to determine the occurrence of large mainshocks. Then sensitivity tests are presented to show the effects on TD-SPSHA, including (1) time-dependent recurrence models, (2) mainshock magnitude, (3) rupture distance, (4) aftershock duration, (5) escaped time since the last event, and (6) future time interval. Furthermore, the bimodal hybrid renewal model is utilized by TD-SPSHA for another case site. The comparison results illustrate that the sequence hazard analysis approach ignoring time-varying properties of large earthquakes for long periods and the effects of associated aftershocks will result in a significantly underestimated hazard. The TD-SPSHA-based hazard curves using the ETAS model are larger than those of the RJ model. The proposed TD-SPSHA approach may be of significant interest to the field of earthquake engineering, particularly in the context of structural design or seismic risk analysis for the long term.
Bayesian network (BN) has important applications in disaster risk analysis due to its unique causal structure and probabilistic characteristics. This research begins with a detailed introduction to probabilistic seismic hazard analysis (PSHA) for China, and the utilization of BN-based modeling for seismic hazard and risk assessment. Subsequently, a comprehensive theoretical exposition of PSHA for China based on BN is presented. This includes a clear explanation of the three-level subdivision of seismic sources and the employment of the elliptical ground-motion model (GMM) in China. Regarding BN modeling, the values, conditional probabilities, and the impact of subdivisions of the nodes are carefully discussed with the assistance of a specific example from China. The advantages of BN in terms of both holistic and probabilistic computation are then demonstrated through the disaggregation of seismic hazard and various sensitivity analyses. Finally, the article concludes by summarizing its content, highlighting the advantages of BN, and outlining future work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.