RETRACTED: PSHRisk-Tool: A Python-Based Computational Tool for Developing Site Seismic Hazard Analysis and Failure Risk Assessment of Infrastructure

A better seismic design for building and other structures is the most effective way to reduce seismic risk and avoid earthquake disaster. Adoption and implementation of new seismic safety regulations and design standards have caused serious problems in many communities in the New Madrid region, including western Kentucky, however. The main reasons for these problems are (1) misunderstanding of the national seismic hazard maps and (2) confusion between seismic hazard and seismic risk. Both are caused by probabilistic seismic hazard analysis (PSHA). PSHA is a mathematical formulation derived from a probability analysis on the distribution of earthquake magnitudes, locations, and ground-motion attenuation. Some assumptions and distributions associated with PSHA have been found to be invalid in earth science, however. In addition, PSHA contains a mathematical error: equating a dimensionless quantity (the annual probability of exceedance – exceedance probability in one year) to a dimensional quantity (the annual frequency of exceedance with the unit of per year [1/yr]). Thus, PSHA is scientifically flawed and the resulting seismic hazard and seismic risk estimates are artifacts. The national seismic hazard curves and maps are artifacts because they were produced from PSHA, even though the inputs are scientifically sound. Although seismic hazard and seismic risk have often been used interchangeably, they are two fundamentally different concepts. Seismic hazard describes the natural phenomenon or property of an earthquake, whereas seismic risk describes the probability of loss or damage that could be caused by a seismic hazard. Seismic hazard and seismic risk play different roles in engineering design and other policy considerations. Furthermore, measures for seismic hazard mitigation are different from those for seismic risk reduction. The difficulties in the development of design ground motion for NEHRP provisions are caused by the use of the national seismic hazard maps which are neither seismic hazard nor seismic risk. The resulting design ground motions for building codes and other policy considerations are therefore problematic. California’s experience proves that deterministic/scenario seismic hazard analysis is an appropriate approach for seismic hazard assessment, seismic risk assessment, as well as *Kentucky Geological Survey, University of Kentucky, 504 Rose Street, Lexington, KY 40506; phone (859) 323-0564; fax (859)257-1147; e-mail: zmwang@uky.edu. 112 Seismic Hazard Design Issues in the Central United States engineering design and other policy considerations. Deterministic/scenario seismic hazard analysis is also appropriate for engineering design and other policy considerations in the New Madrid region, as well as other regions.

Over the past two decades, seismic hazard modeling has advanced along two complementary frontiers: empirical probabilistic frameworks, which systematically capture uncertainty through statistical inference, and physics-based simulation platforms, which directly compute ground motions from the governing equations of wave propagation. This project seeks to unify these two worlds by developing an end-to-end integration between OpenQuake and CyberShake, thereby creating a new generation of seismic hazard models that are globally extensible, probabilistically complete, and physically consistent. CyberShake has been under active development for more than a decade, demonstrating its robustness and scientific maturity through extensive implementations in California. It performs a physics-based probabilistic seismic hazard analysis (PSHA), replacing traditional empirical Ground Motion Prediction Equations (GMPEs) with full 3D numerical simulations of seismic wave propagation. Built upon the UCERF2/3 Earthquake Rupture Forecasts, CyberShake computes hazard curves directly from synthetic seismograms generated via Strain Green’s Tensors and thousands of stochastic rupture variations. This approach enables non-ergodic, site-specific hazard estimation and has set a global benchmark for high-fidelity hazard computation. However, its application has remained geographically limited: both the ERF and 3D velocity models were designed specifically for California, requiring extensive datasets that are rarely available elsewhere. Conversely, OpenQuake, developed by the Global Earthquake Model (GEM) Foundation, provides a fully open-source, Python-based framework for probabilistic seismic hazard and risk analysis. It serves as the computational backbone of large-scale hazard models such as the European Seismic Hazard Model 2020 (ESHM20), which integrates decades of regional expertise into a unified and statistical representation. OpenQuake provides a complete probabilistic framework to build Earthquake Rupture Forecasts (ERFs) that combine declustered catalogs, background seismicity, and multi-branch logic trees, ensuring a balanced and uncertainty-aware representation of regional tectonics. Furthermore, its ecosystem extends seamlessly to vulnerability and exposure modules, enabling the translation of hazard into actionable risk assessments and resilience planning.This project will establish a direct pipeline from OpenQuake’s event-based results to the generation of an ERF compatible with CyberShake’s simulation framework, ensuring moment–rate consistency. By doing so, it will enable CyberShake simulations to be performed for regions beyond California, extending its use to Europe based on the knowledge contained in the ESHM20. The first pilot region is Istanbul, Turkey, a densely populated metropolis located near the western termination of the North Anatolian Fault. Our initial results show that the workflow is already functioning at the prototype level: we have developed a unified 3D velocity model for the Istanbul region by combining available tomographic models with local datasets; generated preliminary event-based rupture catalogs from ESHM20 using OpenQuake; and demonstrated early convergence behavior in hazard curves, indicating that the rupture sampling strategy is statistically robust. These initial results demonstrate the feasibility of the integration approach and indicate that the essential elements needed for a CyberShake-ready ERF are already in place.

Ground‐motion prediction equations (GMPEs) describe the amplitude of shaking as a function of earthquake magnitude, distance from the source, site conditions, and sometimes other variables. Amplitudes are predicted for peak ground acceleration and velocity and for such measures of engineering interest as the response of a damped oscillator of a specified natural period. Selection of appropriate GMPEs is a critical step in a probabilistic seismic‐hazard analysis (PSHA), and therefore their selection is an issue of major practical significance. For PSHA applications, we are interested in more than just a best estimate of the GMPE; equally important is the range of alternative models used to characterize the uncertainty in the GMPEs and thereby to express the limitations of our current knowledge. Explicitly, we wish to capture the center, the body, and the range of defensible technical judgments regarding the GMPEs (Budnitz et al. , 1997; U.S. Nuclear Regulatory Commission, 2012). Two types of uncertainty are recognized in GMPEs and modeled in a PSHA: (1) aleatory uncertainty, expressing random variability of amplitudes about a median prediction equation, and (2) epistemic uncertainty, expressing uncertainty concerning the correct value of the median. Aleatory variability is handled relatively easily in a PSHA by integrating over the distribution of ground‐motion amplitudes about the median (Bommer and Abrahamson, 2006). Epistemic uncertainty is usually handled by considering alternative GMPEs in a logic‐tree format (e.g., Bommer and Scherbaum, 2008; Bommer, 2012). Construction of a logic tree involves populating the branches with alternative models and then assigning weights to the branches; together, the branches and their weights are intended to represent the underlying continuous distribution of possible ground motions. The logic tree must capture both the best estimates of what is known and the potential range of alternatives in light of what is currently not known. There has been considerable …

Proper selection and ranking of Ground Motion Prediction Equations (GMPEs) is critical for successful logic-tree implementation in probabilistic seismic hazard analysis. The present study explores this issue in predicting peak ground accelerations at the rock sites in India. Macroseismic intensity data complemented with limited strong ground-motion recordings are used for the purpose. The findings corroborate the possible conformity between the GMPEs developed for tectonically active shallow crust across the globe. On the other hand, the relevant GMPEs in the intraplate regions cluster into two different groups with the equations of lower ranks catering to higher ground motions. The earthquakes in the subduction zones have significant regional implications. However, affinity in the ground-motion attenuations between the major interface events (MW > 7.4) in Andaman-Nicobar, Japan and Cascadia, respectively, is noted. This can be also observed for the intraslab events in the Hindukush and Taiwan respectively. Overall, we do not observe any significant advantage with the equations developed using the regional data. These findings are expected to be useful in probabilistic seismic hazard analysis across the study region.

The fuzzy logic method has been used widely in civil and earthquake engineering, but there is no comprehensive point of view for utilizing fuzzy approach in order to obtain ground motion prediction equations (GMPEs) for probabilistic seismic hazard analysis (PSHA). Hence, fuzzy magnitude–distance method as a new approach for choosing GMPEs in the process of PSHA, is developed in this research through the selection of the ruling peak ground acceleration (PGA) of each common cell (the combined cell of earthquake intensity and site to source distance). The presented method reduces the need for engineering judgments in seismic analysis based on a newly developed benchmark. It enables designers to not only determine the range of acceptable fuzzy results but also introduces a concept which ensures the selection of initial well-suited GMPEs for the analysis.

<p>Traditional Probabilistic Seismic Hazard Analysis (PSHA) estimates the level of earthquake ground shaking that is expected to be exceeded with a given recurrence time on the basis of  historical earthquake catalogues and empirical and time-independent Ground Motion Prediction Equations (GMPEs). The smooth nature of GMPEs usually disregards some well known drivers of ground motion characteristics associated with fault rupture processes, in particular in the near-fault region, complex source-site propagation of seismic waves, and sedimentary basin response. Modern physics-based earthquake simulations can consider all these effects, but require a large set of input parameters for which constraints may often be scarce. However, with the aid of high-performance computing (HPC) infrastructures  the parameter space may be sampled in an efficient and scalable manner allowing for a large suite of site-specific ground motion simulations that approach the center, body and range of expected ground motions. </p><p>CyberShake is a HPC platform designed to undertake physics-based PSHA from a large suite of earthquake simulations. These simulations are based on seismic reciprocity, rendering PSHA computationally tractable for hundreds of thousands potential earthquakes. For each site of interest, multiple kinematic rupture scenarios, derived by varying slip distributions and hypocenter location across the pre-defined fault system, are generated from an input Earthquake Forecast Model (EFM). Each event is simulated to determine ground motion intensities, which are synthesized into hazard results. CyberShake has been developed by the Southern California Earthquake Center, and used so far to assess seismic hazard in California. This work focuses on the CyberShake migration to the seismic region of South Iceland (63.5°- 64.5°N, 20°-22°W) where the largely sinistral East-West transform motion across the tectonic margin is taken up by a complex array of near-vertical and parallel North-South oriented dextral transform faults in the South Iceland Seismic Zone (SISZ) and the Reykjanes Peninsula Oblique Rift (RPOR). Here, we describe the main steps of migrating CyberShake to the SISZ and RPOR, starting by setting up a relational input database describing potential causative faults and rupture characteristics, and key sites of interest. To simulate our EFM, we use the open source code SHERIFS, a logic-tree method that converts the slip rates of complex fault systems to the corresponding annual seismicity rate. The fault slip rates are taken from a new 3D physics-based fault model for the SISZ-RPOR transform fault system. To validate model and simulation parameters, two validation steps using key CyberShake modeling tools have been carried out. First, we perform simulations of historical earthquakes and compare the synthetics with recorded ground motions and results from other forward simulations. Second, we adjust the rupture kinematics to make slip distributions more representative of SISZ-type earthquakes by comparing with static slip distributions of past significant earthquakes. Finally, we run CyberShake and compare key parameters of the synthetic ground motions with new GMPEs available for the study region. The successful migration and use of CyberShake in South Iceland is the first step of a full-scale physics-based PSHA in the region, and showcases the implementation of CyberShake in new regions.</p>

A method is proposed to identify within seismic catalogs those earthquakes that are most relevant to the seismic hazard. The approach contrasts with the classical approach to decluster the seismic catalog with the expectation that the remaining main shocks will be the relevant events for the seismic hazard analysis. We apply a time window like in the window declustering approach of Gardner and Knopoff, but the time window is motivated by relevance to engineering. A ground motion criterion replaces the spatial window. An event in the time window is included in the “Maximum Shaking Earthquake Catalog (MSEQ catalog)” if the median ground motion at its epicenter exceeds the predicted median ground motion there from the main shock, using a locally appropriate ground motion prediction equation. Ground motion can be measured by any parameter that is estimated by a ground motion prediction equation. We consider peak acceleration and spectral amplitude (SA) at periods of 0.2, 1.0, and 3.0 s. The longer period parameters systematically remove more small events. The purpose is not to produce a declustered catalog, in which each group of physically related earthquakes is represented by its largest event. Statistical properties of the MSEQ catalog somewhat resemble the corresponding declustered catalog in three tested regions, but the MSEQ catalogs all retain more large-magnitude earthquakes. The MSEQ catalog may better represent the potential hazard in a region, and thus might be considered as an alternative to a declustered catalog in developing the seismicity model for probabilistic seismic hazard analysis.

A probabilistic seismic hazard analysis of Pakistan is carried out to produce seismic hazard maps showing peak ground acceleration for the 2% and 10% of probability of exceedance in 50 years and spectral acceleration for the 5% and 2% probabilities of exceedance in 50 years. The main objective of this work is to define seismic hazard definition for Pakistan based on the classical approach using updated seismic hazards analysis inputs. A composite earthquake catalogue consisting of 32,700 events was compiled and used in the analysis with a magnitude range of Mw 4.0–8.3. The computations were made at a rectangular grid of 5 km in the OpenQuake engine. Ground motion parameter values have been obtained for flat rock reference seismic site conditions having a shear wave velocity of 760 m/s. Through the logic tree, epistemic uncertainties inherent in ground motion prediction equations and the maximum magnitude potential of seismic sources were estimated. Ground motion prediction equations were assigned equal weights in the logic tree while different weights were assigned to the maximum magnitude potential models. Results of the study were expressed as ground motion contour maps and median uniform hazard spectra for important cities in Pakistan. The results showed that peak ground acceleration values in Pakistan range from 0.16 to 0.54 g for the 10% of probability of exceedance, 0.23–0.72 g for the 5% probability of exceedance and 0.32–1.02 g for the 2% of probability of exceedance in 50 years. Spectral acceleration values at 0.2 s range from 0.67 to 2.19 g for the 2% chance of exceedance in 50 years, respectively, whereas spectral acceleration at 1.0 s ranges from 0.09 to 0.52 g for the 2% chance of exceedance in 50 years. The centre of Pakistan and the southern and the northern region are exposed to high hazard compared to the central-eastern part of the country. Quetta, Chitral, Peshawar, Islamabad Ziarat and Gwadar are exposed to seismic hazard in the PGA range of 0.32–0.45 g. Comparison of the results of this study with those of similar studies suggests that the seismic estimates proposed are reliable. The study provides a demonstration that the classical seismic hazard approach applied here is rational and applicable in Pakistan.

An area located near an active volcano, subduction zone, and/or active fault has a risk of an earthquake. Earthquakes can cause damage to buildings with a large number of losses. One of the earthquake-related parameters required in planning an earthquake-resistant building is the Peak Ground Acceleration (PGA). The objective of this study is to compare the PGA value at the coastal area of Loh Buaya, Rinca Island, Indonesia, using Probabilistic Seismic Hazard Analysis (PSHA) and Deterministic Seismic Hazard Analysis (DSHA). The DSHA is calculated based on earthquake data series from the United States Geological Survey (USGS) in the period between 1922 to 2022. Also, it is calculated using 10 data of Ground Motion Prediction Equation (GMPE). Data from the USGS is constrained with magnitude > 5.0 MW and earthquake distance < 200 km from the study site. The PSHA is based on the Indonesian earthquake hazard map and the de-aggregation map from the National Earthquake Study Centre of Indonesia with a probability exceeding 2% in 50 years. Based on the parameters of PGA maximum, distance, and magnitude, the DSHA by Kanno method has the closest result to the PSHA. It is concluded that this area has a high earthquake risk with a PGA maximum of more than 0.5 g.

This paper presents deterministic and probabilistic predictions of inelastic response spectra based on a comprehensive ground motion prediction equation (GMPE). Our analysis reveals that over a wide structural period range, the magnitude scaling for an inelastic system is higher than that for an elastic system, especially for ductility levels greater than 2 and magnitude greater than 6.5. Both deterministic and probabilistic hazard analyses show that the “equal displacement rule,” to estimate inelastic displacement, is valid for small to moderate magnitudes and/or for low ductility levels. However, it underestimates inelastic deformation even for long period structures if the earthquake magnitude is large and the structure needs to sustain a large ductility. Our study shows that an inelastic GMPE can easily be implemented as part of standard probabilistic seismic hazard analysis (PSHA) packages to directly generate probabilistic hazard for inelastic response, avoiding possible over‐ or under‐conservatism in approximating inelastic deformation from an elastic system.

Evaluation of the predictive performance of regional and global ground motion predictive equations against Greek strong motion data

Modelling the spatial correlation of ground motion intensity measures (IMs) has become a keystone in seismic hazard and risk analysis of portfolios of buildings, spatially distributed infrastructures and earthquake-induced phenomena. The quantification of the seismic performance of such systems over a region requires knowledge of the joint probability of occurrence of different ground motion IMs at multiple locations. Therefore, the classical Probabilistic Seismic Hazard Assessment (PSHA) tools, which are based on the hypothesis of independency between IMs at closely spaced sites, are not appropriate. Over the past decade, the spatial correlation of peak ground acceleration (PGA) and spectral acceleration (SA) has been widely studied. Although common findings suggest that the correlation of intra-event residuals decreases quite rapidly with increasing separation distances, these models feature different rates of decay. Among the causes that may lead to inconsistencies between models, with significant impact on hazard and loss estimates, are the multiple techniques used to estimate the correlation structure, the region and local site conditions, as well as the choice of the databases. Furthermore, little effort has been directed towards other IMs suitable to characterize the resulting damage to structures and predict ground failure: peak ground velocity (PGV), peak ground displacement (PGD) and spectral displacement (SD) as well as Arias intensity (퐼") and cumulative absolute velocity (CAV), to name but a few. A proper definition of the seismic action in terms of spectral displacement ordinates has progressively gained importance in performance-based seismic design, and 퐼" and CAV have been found to be adequate for many other earthquake engineering applications, such as evaluating the susceptibility to liquefaction and earthquake-induced landslides. In this study, we use geostatistical tools in order to compute the spatial correlations of such ground motion parameters. We perform comparisons with other existing models with the aim of: (1) identifying factors that most affect the correlation structure, and (2) quantifying the variability of correlation lengths between different events and regions. Moreover, spatial correlation models are usually calibrated on the within-event component of residuals, obtained based on ergodic ground motion prediction equations (GMPEs). Therefore, we also analyse the spatial correlation of event- and site- corrected residuals, retrieved relaxing the ergodic assumption, to further investigate the factors that determine the spatial dependency of IMs. In order to address these issues, we use the 2016-2017 Central Italy seismic sequence database, which includes nine Mw ≥ 5.0 earthquakes that occurred over a time period of five months. These data allow some uncertainties to be removed and an evaluation of the event-to-event variability of the spatial correlation because the same seismic region is considered. Our preliminary results will provide a more accurate picture of ground motions, and thus improve the modelling of earthquake losses for risk model development.

Multivariate normality of logarithmic intensity measures (IMs) is conventionally assumed in earthquake engineering applications. This article introduces a vine copula approach as a useful tool for multivariate modeling of IMs. This approach provides a flexible way to decompose a joint distribution into individual marginal distributions and multiple dependences characterized by a cascade of bivariate copulas (pair-copulas), whereas the conventional multivariate normality can be considered as a special case of the vine copula model. Based on the Next Generation Attenuation-West1 database and various combinations of ground-motion prediction equations (GMPEs), the optimal dependence structures among peak ground acceleration, peak ground velocity, and Arias intensity, as well as that for spectral accelerations at four periods, are identified. The joint normality assumption for the two vector sets of logarithmic IMs is examined from the perspective of copula theory. The results illustrate that the normality assumption is generally adequate for bivariate IMs but may not be optimal for multivariate IMs. Using the same set of GMPEs (developed by the same researchers) may improve the joint normality for logarithmic IMs. Furthermore, the impact of dependence structures among IMs on probabilistic seismic slope displacement hazard analysis is explored. The results indicate that using the same Pearson correlation coefficients but different dependence structures for IMs produces different hazard results and this difference is generally enlarged with increasing hazard levels. As hazard difference from different dependence structures is generally not significant, the multivariate normality distribution for logarithmic IMs is judged to be an acceptable assumption in engineering practice. Alternatively, engineers may make a choice between the joint normal distribution and the vine copula tool depending on the specific situation because of the better generality of the latter.

Fragility curves are useful tools for presenting the risk of structural damage from earthquakes as a function of a ground-motion parameter. Seismic hazard analysis for an area precedes the fragility analysis for structures built in that area. Many studies are available in the literature regarding probabilistic seismic hazard analysis (PSHA) and fragility estimation. However, a practical approach to integrate the hazard curve from PSHA and using the ground motion of that level to estimate fragility can be an area to explore further. In this study, an effort has been made to obtain peak ground acceleration (PGA) level at design-based earthquake (DBE) from PSHA. Based on this PGA value obtained, a fragility assessment has been performed. This study aims to obtain a fragility curve for a midrise RC frame building for DBE hazard level using PSHA. Three 6-story RC frame buildings are considered in this study for a seismically active zone in India. The buildings have been designed with the following performance objectives: (1) immediate occupancy (IO) with 1% drift, (2) life safety (LS) with 2% drift, and (3) collapse prevention (CP) with 3% drift. Incremental dynamic analysis (IDA) is performed using 22 numbers of far-field ground motions as prescribed by a strong ground-motion database to obtain the capacity of exceeding the respective drift limit states. Fragility curves for the three buildings are generated based on those capacities. The present study is a practical approach to capture the consequence of seismic hazard for an area and predicting the seismic risk.

Research Article| July 24, 2018 Decomposing Leftovers: Event, Path, and Site Residuals for a Small‐Magnitude Anza Region GMPE Valerie Sahakian; Valerie Sahakian aEarthquake Science Center, U.S. Geological Survey, 345 Middlefield Road, MS 977, Menlo Park, California 94025, vjs@uoregon.edu Search for other works by this author on: GSW Google Scholar Annemarie Baltay; Annemarie Baltay aEarthquake Science Center, U.S. Geological Survey, 345 Middlefield Road, MS 977, Menlo Park, California 94025, vjs@uoregon.edu Search for other works by this author on: GSW Google Scholar Tom Hanks; Tom Hanks aEarthquake Science Center, U.S. Geological Survey, 345 Middlefield Road, MS 977, Menlo Park, California 94025, vjs@uoregon.edu Search for other works by this author on: GSW Google Scholar Janine Buehler; Janine Buehler bScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92039 Search for other works by this author on: GSW Google Scholar Frank Vernon; Frank Vernon bScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92039 Search for other works by this author on: GSW Google Scholar Debi Kilb; Debi Kilb bScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92039 Search for other works by this author on: GSW Google Scholar Norman Abrahamson Norman Abrahamson cCivil and Environmental Engineering, UC Berkeley, 447 Davis Hall, Berkeley, California 94720 Search for other works by this author on: GSW Google Scholar Author and Article Information Valerie Sahakian aEarthquake Science Center, U.S. Geological Survey, 345 Middlefield Road, MS 977, Menlo Park, California 94025, vjs@uoregon.edu Annemarie Baltay aEarthquake Science Center, U.S. Geological Survey, 345 Middlefield Road, MS 977, Menlo Park, California 94025, vjs@uoregon.edu Tom Hanks aEarthquake Science Center, U.S. Geological Survey, 345 Middlefield Road, MS 977, Menlo Park, California 94025, vjs@uoregon.edu Janine Buehler bScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92039 Frank Vernon bScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92039 Debi Kilb bScripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92039 Norman Abrahamson cCivil and Environmental Engineering, UC Berkeley, 447 Davis Hall, Berkeley, California 94720 Publisher: Seismological Society of America First Online: 24 Jul 2018 Online Issn: 1943-3573 Print Issn: 0037-1106 © Seismological Society of America Bulletin of the Seismological Society of America (2018) 108 (5A): 2478–2492. https://doi.org/10.1785/0120170376 Article history First Online: 24 Jul 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Valerie Sahakian, Annemarie Baltay, Tom Hanks, Janine Buehler, Frank Vernon, Debi Kilb, Norman Abrahamson; Decomposing Leftovers: Event, Path, and Site Residuals for a Small‐Magnitude Anza Region GMPE. Bulletin of the Seismological Society of America 2018;; 108 (5A): 2478–2492. doi: https://doi.org/10.1785/0120170376 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search Abstract Ground‐motion prediction equations (GMPEs) are critical elements of probabilistic seismic hazard analysis (PSHA), as well as for other applications of ground motions. To isolate the path component for the purpose of building nonergodic GMPEs, we compute a regional GMPE using a large dataset of peak ground accelerations (PGAs) from small‐magnitude earthquakes (⁠0.5≤M≤4.5 with >10,000 events, yielding ∼120,000 recordings) that occurred in 2013 centered around the ANZA seismic network (hypocentral distances ≤180 km⁠) in southern California. We examine two separate methods of obtaining residuals from the observed and predicted ground motions: a pooled ordinary least‐squares model and a mixed‐effects maximum‐likelihood model. Whereas the former is often used by the broader seismological community, the latter is widely used by the ground‐motion and engineering seismology community. We confirm that mixed‐effects models are the preferred and most statistically robust method to obtain event, path, and site residuals and discuss the reasoning behind this. Our results show that these methods yield different consequences for the uncertainty of the residuals, particularly for the event residuals. Finally, our results show no correlation (correlation coefficient [CC] <0.03⁠) between site residuals and the classic site‐characterization term VS30⁠, the time‐averaged shear‐wave velocity in the top 30 m at a site. We propose that this is due to the relative homogeneity of the site response in the region and perhaps due to shortcomings in the formulation of VS30 and suggest applying the provided PGA site correction terms to future ground‐motion studies for increased accuracy. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.