Ground Motion Prediction Models Research Articles

Vertical Ground Motion Prediction Equations for Interplate and Intermediate-Depth Intraslab Earthquakes at Mexico City’s Hill Zone

ABSTRACT We present ground motion prediction models for spectral pseudo acceleration (5% damping), peak ground velocity, and peak ground acceleration values from interplate and intermediate-depth intraslab earthquakes at the so-called hill zone of Mexico City for the vertical component and the vertical-to-horizontal ratio case. There are no previous models for the vertical component from these events in Mexico City. The prediction equations were developed as a function of magnitude and also as a function of distance to the fault surface of earthquakes by using 30 and 26 seismic recordings from interplate (1965–2022) and intermediate-depth intraslab (1964–2017) earthquakes at the hill zone of the city by using Bayesian regression analyses. The attenuation models are developed for interplate events for distances ranging from 280 km to over 500 km and Mw from 6 to 8.1, and for intraslab events for distances to the fault surface ranging from 100 km up to about 600 km, focal depths from 40 km to over 120 km and Mw from 5 to 8.2. Nevertheless, since at large distances ground motions are generally weak, we recommend the applicability of the proposed models to be restricted to up to 400 km. The models are adequate to estimating the seismic hazard at the hill zone of Mexico City, as proved by contrasting analytical versus empirical seismic exceedance rates for the vertical component.

Directionality of FIV3 ground-motion intensities during the 6 February 2023 Kahramanmaraş, Türkiye earthquake doublet

At present time, ground-motion prediction models neglect the directionality observed in horizontal components of earthquake ground motions, that is, the important changes in ground-motion intensity that occur with changes in azimuth. This study presents an investigation of the directionality of a recently proposed measure of ground-motion intensity during the 6 February 2023, Mw 7.8 Pazarcık and Mw 7.5 Elbistan earthquake doublet in the Kahramanmaraş region of Türkiye, which resulted in the collapse of more than 35,000 buildings and caused almost 60,000 fatalities. The studied intensity measure is referred to as FIV3, which has been shown to be better correlated with structural collapse than the spectral acceleration at the fundamental period of the structure. The improved intensity measure is period-dependent and is computed as the sum of the three largest incremental velocities with the same polarity obtained from the area under segments of a low-pass filtered ground acceleration time series. The following aspects are studied in this article: variation of FIV3 intensity with changes in the orientation; variation of FIV3 intensity with changes in the period of vibration; attenuation of FIV3 intensities with increasing distance; and spatial distribution of the orientation of maximum FIV3 intensity. This study is based on 231 pairs of records from the Mw 7.8 main event and 222 pairs of records from the Mw 7.5 event. Similarly to the directionality of spectral ordinates, it is found that the directionality of FIV3 intensity also increases with increasing period. Strong directionality occurred not only in the near field but up to distances as large as 400 km from the epicenter. The orientation of maximum FIV3 intensity is found to occur close to the transverse orientation, consistent with observations for the orientation of maximum spectral ordinates during strike-slip earthquakes.

Mean period prediction models for Mexican interplate and intermediate-depth intraslab earthquake ground motions

The aim of this paper is to predict the mean period, Tm, as a measure of the frequency content of interplate and intermediate-depth intraslab earthquake ground motions recorded at rock sites due to subduction earthquakes in Mexico. For this purpose, both Ground Motion Prediction Models (GMPMs), which use a parametric regression, and Support Vector Machine (SVM) regression, a type of machine learning algorithm used for regression analysis, are employed in this investigation. Therefore, two databases which include 484 and 300 earthquake ground motions recorded during interplate and intraslab historical Mexican earthquakes were assembled in this study. It is shown that Tm estimates depend on the type of earthquake (i.e., interplate or intraslab). While the SVM regression yields the lowest total standard deviation in predicting Tm, the GMPM still yields an acceptable total standard deviation similar to that reported in other studies. A comparison of the GMPM for intraslab earthquakes in Mexico with another GMPMs for predicting Tm recently introduced in the literature showed differences, which suggests that GMPMs for predicting Tm should be developed for specific regions.

Modeling and Prediction of Nonstationary Ground Motions as Time–Frequency Images

Proper modeling of nonstationary characteristics of ground motions is critical in estimating the seismic performance of structures. The importance of ground motion nonstationarity is being increasingly recognized in seismic design codes and provisions. This paper develops a new method for predicting seismic ground motions in the joint time–frequency domain based on recorded ground motions. We treat wavelet energy maps of acceleration records as images and extract essential patterns from them using principal component analysis. These patterns are then linked to the seismic source, path, and site variables using general regression neural network. The resulting model predicts an image representing the expected evolutionary power spectrum conditioned on the input variables. An example application is presented using records from the Next Generation Attenuation of Ground Motions Database of the Pacific Earthquake Engineering Center. As opposed to conventional ground motion prediction models, the proposed approach retains the time domain characteristics of ground motions. The results show that the proposed model can predict significant patterns in the seismic energy distribution, acceleration time series, and 5% damped elastic spectral accelerations.

Ground motion prediction model for Himalayan region: a comprehensive review

Ground motion prediction model for Himalayan region: a comprehensive review

Ground motion prediction model for shallow crustal earthquakes in Japan based on XGBoost with Bayesian optimization

Ground motion prediction is an important and complex research subject in earthquake engineering and traditional approaches based on statistical regression have much room for improvement in prediction accuracy. Utilizing the 67,164 ground motion records from KiK-net and K-Net for 777 shallow crustal earthquakes between 1997 and 2019 in Japan, this paper proposes a ground motion prediction model XGBoost-SC based on the machine learning algorithm of eXtreme Gradient Boosting (XGBoost) for Japan. Magnitude, focal depth, hypo-central distance, Vs30, site altitude, and focal mechanism were used as the feature parameters, and XGBoost, Random Forest, and Deep Neural Networks (DNN) algorithms were selected for model training while Bayesian optimization was used to search for optimized hyperparameters to improve the prediction accuracy. XGBoost algorithm was selected for further study based on the comparison of results from the three algorithms. Residual change with magnitude and hypo-central distance, the probability distribution of residuals, residual standard deviation (σ), residual mean squared error (MSE), and Pearson correlation coefficient (R) were used as the evaluation parameters, and a comparison study was performed against the ground motion prediction equation based on traditional approaches. Actual earthquake events were selected to compare the prediction results against the observation records. To further validate and explain the proposed model, SHapley Additive exPlanations (SHAP) analysis was performed to explain the impact of selected feature parameters on the proposed model. The results demonstrate that the proposed XGBoost-SC model has good prediction stability for all periods, and its residual errors are smaller than those of other models. Therefore, the proposed model can better reflect the ground motion attenuation for shallow crustal earthquakes in Japan and can serve as a better model for ground motion prediction in future aseismic design and earthquake disaster mitigation efforts.

Convolutional Neural Network for Earthquake Ground Motion Prediction Model in Earthquake Early Warning System in West Java

As urbanization continues, more people and infrastructure are concentrated in areas that are at risk from earthquakes. This can increase the potential damage and loss of life when earthquakes occur. Indonesia is a region that is near the boundary of three major tectonic plates which has a very high frequency of earthquake occurrences. Over the past two decades, a new approach to earthquake disaster risk mitigation has emerged. It is based on the advent of digital seismology and advances in data transmission and automatic processing that make it possible to send warnings before the largest ground motion that called the Earthquake Early Warning System (EEW). On-site EEW is a type of EEW that consists of limited seismic stations located at a specific destination/infrastructure (for early detection systems). On-site EEW estimates ground motion parameters directly from the characteristics of seismograms recorded by the system. An artificial intelligence approach to EEW is necessary to increase the speed and accuracy of information, which increases processing time, especially in areas very close to the epicenter

Development of shear-wave velocity profiles and kappa compatible with Ground Motion model for Taiwan

A new velocity model (GMM) compatible with a ground motion prediction model (GMM) for Taiwan is presented in this study. The model is developed using the data set extracted from the flatfile compiled for Taiwan Senior Seismic Hazard Analysis Committee (SSHAC) level 3 project (NCREE, 2015). The Phung et al. (2020) ergodic Sa GMM was used as a backbone model for estimating the site amplification in the frequency domain using inverse Random vibration theory (IRVT). The main motivation for developing a velocity model is that the estimation of the site scaling in terms of the time-averaged shear-wave velocity over the top 30 m (VS30), the method also provides the corresponding depth-dependent VS(z) profiles and the value for the selected VS30 value as a result of the inverse quarter-wave-length method (IQWL). Not all of the empirical amplification can be explained by the 1-D VS profiles andvalues. For soft sites (VS30 < 500 m/s), and intermediate periods (0.5-2.0 sec), there is additional amplification in the empirical data which is attributed to 3-D path and site effects. Using the proposed method, the resulting GMM provides a VS profile, k, and 3-D effect for each VS30 value, which provides a more informative handoff of ground-motion information for use in site-specific site response studies.

Construction of a Ground-Motion Logic Tree through Host-to-Target Region Adjustments Applied to an Adaptable Ground-Motion Prediction Model: An Addendum

ABSTRACT Boore et al. (2022; hereafter, Bea22) described adjustments to a host-region ground-motion prediction model (GMPM) for use in hazard calculations in a target region, using Chiou and Youngs (2014; hereafter, CY14) as the host-region model. This article contains two modifications to the Bea22 procedures for the host-to-target adjustments, one for the source and one for the anelastic attenuation function. The first modification is to compute logic-tree branches for the source adjustment variable ΔcM given in Bea22 assuming that the host- and target-region stress parameters are uncorrelated, instead of the implicit assumption in Bea22 that they are perfectly correlated. The assumption of uncorrelated stress parameters makes little difference for the example in Bea22 because the standard deviation of the host-region stress parameter is much less than that of the target-region stress parameter. However, this might not be the case in some future applications. The second modification is to the host-to-target anelastic attenuation path adjustment. The adjustment in Bea22 involves a distance-independent change in the γ variable that controls the rate of anelastic attenuation in the CY14 GMPM. This article proposes a method to account for a distance dependence in the adjustment. Such a dependence is needed for short-period ground-motion intensity measures (GMIMs) at distances greater than 100 km, with the importance increasing with distance. For the example in Bea22, the ratio of GMIMs computed with the revised and the previous adjustment to γ is less than about a factor of 1.05 at distances within about 100 km, but it can exceed a factor of 2 at 300 km for short-period GMIMs.

Variability of response spectra of single-path ground-motion record pairs

To improve the accuracy of seismic hazard analysis, understanding the uncertainty of the prediction of the ground-motion amplitude for a specific site owing to a specific source is important. In this study, the variability of the acceleration response spectra of single-path ground motion was evaluated using record pairs obtained from two earthquakes at the same site with the same magnitude, location, and focal mechanism, recorded by high-density strong-motion observation networks in Japan. The natural log standard deviations of the single-path ground motions varied with the period and were distributed in the range of approximately 0.3–0.5. Even if an empirical ground-motion prediction model that accurately models the characteristics of single-path ground motions could be developed, avoiding this variability in the prediction is difficult. The standard deviation of the within-event residuals was smaller at larger distances, and the period of the maximum standard deviation tended to increase as the earthquake magnitude increased. These features may be attributed to differences in the source characteristics of each earthquake. These findings will be useful for the development of advanced empirical ground-motion models and for improving the accuracy of seismic hazard analysis.

Regional offshore ground motion prediction model from a referenced empirical approach: A case study in the Japan Trench area

With increasing resource exploitation and engineering construction offshore, the seismic safety of offshore structures has received attention from researchers and designers, and an offshore ground motion prediction model (GMPM) can be used to estimate the possible earthquake response of structures and provide the foundation for seismic fortification of structures. Because there are few strong motion records recorded by offshore stations, there are insufficient empirical data in the magnitude-distance range to directly develop a GMPM using empirical regression techniques. To develop a reliable GMPM for data-poor offshore regions, we used a referenced empirical approach based on an onshore empirical prediction model. We collected 2605 records from 138 crustal earthquake events, 2669 records from 125 slab earthquake events and 5693 records from 267 interface earthquake events recorded by S-net stations. We used the existing Japanese onshore GMPM as a reference model. Then, we analyzed the residuals of the reference model for offshore data and calculated its adjustment factors. Finally, we developed a GMPM for the Japan Trench area and compared it with the existing onshore models. The proposed model fit the data subsets at hypocentral distances of 20–300 km reasonably well. There were different spectral accelerations (SAs) for different source types, with considerably larger SAs for short periods of offshore motions from crustal events than for the motions from the other two types, and the difference becomes more obvious with the increase of magnitude. At short periods, the SAs of the offshore motions from slab events were slightly larger than those of the motions from the interface events. The discrepancy of the offshore and onshore GMPM was significant, and the peak ground accelerations (PGAs) and SAs of the offshore ground motions were apparently greater than those of onshore ground motions of the same magnitude and distance. The offshore GMPM established by the reference empirical approach contribute to studying the characteristics of offshore strong ground motions, and this approach can be applied to data-poor regions.

Ground Motion Prediction of High-Energy Mining Seismic Events: A Bootstrap Approach

Induced seismicity has been a serious problem for many coal mines in the Upper Silesian Coal Basin in Poland for many decades. The occurring mining tremors of the rock mass generate seismic vibrations that cause concern to the local population and in some rare cases lead to partial damage to buildings on the surface. The estimation of peak ground acceleration values caused by high energy mining seismic tremors is an important part of seismic hazard assessment in mining areas. A specially designed bootstrapping procedure has been applied to estimate the ground motion prediction model and makes it possible to calculate the confidence intervals of these peak ground acceleration values with no assumptions about the statistical distribution of the recorded seismic data. Monte Carlo sampling with the replacement for 132 seismic records measured for mining seismic tremors exceeding 150 mm/s2 have been performed to estimate the mean peak ground acceleration values and the corresponding upper limits of 95% confidence intervals. The specially designed bootstrap procedure and obtained ground motion prediction model reflect much better the observed PGA values and therefore provide more accurate PGA estimators compared to the GMPE model from multiple regression analysis. The bootstrap analysis of recorded peak ground acceleration values of high-energy mining tremors provides significant information on the level of seismic hazard on the surface infrastructure. A new tool has been proposed that allows for more reliable determination of PGA estimators and identification in the areas in coal mines that are prone to high-energy seismic activity.

Investigation of Site Amplification and Attenuation Effects in the Changjiang Delta

Abstract Site response in the Changjiang Delta in eastern China was studied using Lg Fourier amplitude spectra. We used broadband seismograms recorded at 70 stations from 62 earthquakes with magnitude (Ms) varying from 3.5 to 5.0 during 2009–2021. The crustal quality factor Q and site response in the Changjiang Delta were obtained simultaneously from regression of Lg Fourier acceleration amplitude versus frequency. The κ0 of each individual station was subsequently calculated from a regression of the high-frequency site terms versus frequency. The site terms exhibit obvious dependence on sediment thickness in the Changjiang Delta. The site amplification factor reaches ∼7–10 for stations overlying sediments ∼8–9 km thick in the northern Jiangsu basin. The site terms were found to behave consistently as a function of sediment thickness over the frequencies of 0.56–24.86 Hz. Site amplification shows a positive correlation with sediment thickness at lower frequencies (<7.26 Hz) but transitions to a negative correlation as frequency increases to 12.86 Hz and higher. Linear functions versus sediment thickness were used to model the site response terms at individual frequencies. We also showed that site terms calculated using the κ0 model as a function of sediment thickness fit the site response terms well at frequencies higher than 9.66 Hz. Results of this study can be incorporated in ground-motion prediction models for the Changjiang Delta. In addition, the site response estimates determined here can be used to reduce bias due to site effects in studies of earthquake source parameters.

A far-field ground motion prediction model for interface earthquakes at the hill zone of Mexico City

This study presents an updated attenuation model to predict the peak ground acceleration (PGA), peak ground velocity (PGV), 5% damped pseudo-spectral acceleration (SA), and the average spectral acceleration (AvgSA) at the hill zone of Mexico City for interface earthquakes. The strong-motion dataset comprises 33 earthquakes recorded at CU station, covering a moment magnitude (Mw) range from 6.0 to 8.1 and a source-to-site distance (Rrup) range from 240 to 490 km. Given the small number of available observations, a Bayesian regression scheme is used to obtain the coefficients of the ground-motion prediction model (GMPM). In addition, the epistemic uncertainty in the estimation of the regression coefficients is evaluated, showing its impact on the framework of a probabilistic seismic hazard analysis (PSHA). The results are compared with models previously developed for the CU station, discussing the differences observed between the median predictions and their standard deviations. Likewise, seismic hazard curves are computed and compared with empirical curves obtained by counting the number of times per year that a given value of ground-motion intensity is exceeded. The results show that the dispersion of the GMPM proposed is lower than the previous models for PGA and SA, which means better predictability and more reliable estimates of the seismic hazard at the site.

A study of site response in the Longmen Shan and adjacent regions and site response models for the Sichuan Basin

We investigated the regional attenuation and site responses in the Sichuan Basin and adjacent Songpan-Ganze terrane of the Tibetan Plateau using seismic data recorded at 41 stations from regional earthquakes occurring between January 2009 and October 2020. Fourier amplitude spectra of Lg waves were computed and binned into 18 frequency bins with center frequencies ranging from 0.1 Hz to 20.4 Hz. The quality factor is estimated as Q(f)=313f0.74 for the Sichuan Basin and Q(f)=568f0.34 for the Songpan-Ganze terrane, reflecting significant differences in the crustal structure beneath these two regions. Relative to the Songpan-Ganze terrane, site responses in the Sichuan Basin are characterized by strong amplification effects at frequencies lower than 6 Hz and obvious attenuation at higher frequencies (>10 Hz). κ0 of stations in the Sichuan Basin show clearly geographical dependence with an average value of 0.045 s, whereas stations in the Songpan-Ganze terrane generally have smaller κ0 values with an average value of 0.028 s. In particular, site response and κ0 of stations in the Sichuan Basin are found to be dependent on the geographically variable thickness of the sedimentary deposits (sediment thickness). These units are comprised of sedimentary rock and semi-consolidated sediments, with a maximum thickness reaching approximately 10 km. Site response terms in the Sichuan Basin derived from the Lg Fourier spectra exhibit consistent patterns versus sediment thickness as frequency increases. We developed site response models as functions of sediment thickness for stations in the Sichuan Basin. The site response model derived from Lg site terms is consistent with that based on site response terms from coda amplitude spectra and horizontal to vertical (H/V) spectral ratios. The models were then incorporated in the stochastic method of ground motion predictions in the Sichuan Basin for six earthquakes occurring between October 2020 and June 2022. Residual analysis suggests that incorporating the site response models as functions of sediment thickness can improve the ground motion prediction model for the Sichuan Basin from moderate earthquakes.

A data-driven artificial neural network model for the prediction of ground motion from induced seismicity: The case of The Geysers geothermal field

Ground-motion models have gained foremost attention during recent years for being capable of predicting ground-motion intensity levels for future seismic scenarios. They are a key element for estimating seismic hazard and always demand timely refinement in order to improve the reliability of seismic hazard maps. In the present study, we propose a ground motion prediction model for induced earthquakes recorded in The Geysers geothermal area. We use a fully connected data-driven artificial neural network (ANN) model to fit ground motion parameters. Especially, we used data from 212 earthquakes recorded at 29 stations of the Berkeley–Geysers network between September 2009 and November 2010. The magnitude range is 1.3 and 3.3 moment magnitude (Mw), whereas the hypocentral distance range is between 0.5 and 20 km. The ground motions are predicted in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped spectral acceleration (SA) at T=0.2, 0.5, and 1 s. The predicted values from our deep learning model are compared with observed data and the predictions made by empirical ground motion prediction equations developed bySharma et al. (2013)for the same data set by using the nonlinear mixed-effect (NLME) regression technique. For validation of the approach, we compared the models on a separate data made of 25 earthquakes in the same region, with magnitudes ranging between 1.0 and 3.1 and hypocentral distances ranging between 1.2 and 15.5 km, with the ANN model providing a 3% improvement compared to the baseline GMM model. The results obtained in the present study show a moderate improvement in ground motion predictions and unravel modeling features that were not taken into account by the empirical model. The comparison is measured in terms of both theR2statistic and the total standard deviation, together with inter-event and intra-event components.

On the development of region and site-specific ground motion prediction model for the region of I-lan, Taiwan

this study presents the development of a region- and -site specific ground motion model (GMM) based on ground motion data obtained from the I-lan alluvial basin in northeastern Taiwan. The development was performed by calibrating the ergodic Phung et al. (2020) (Ph20) GMM developed for Taiwan. The source of strong ground motion data includes events from shallow crustal and subduction zones with magnitudes (MW) from 4.0 to 7.6, records from distances shorter than 200 km, and focal depths up to 150 km. The resulting regional model accounts for peculiarities that are not captured by the ergodic Ph20 GMMs, such as (1) the difference in source type effects, (2) VS30-scaling from the local site effects, and (3) enhancement of long period spectral ordinates due to the deep sedimentary basins. The standard deviations of the local model are much lower than those of the ergodic model, particularly the component of the standard deviation that corresponds to the site-to-site variability (ϕS2S), indicating that better site characterization can significantly reduce the overall uncertainty in ground motion estimation.

Ground-motion prediction models for induced earthquakes in the Groningen gas field, the Netherlands

Small-magnitude earthquakes induced by gas production in the Groningen field in the Netherlands have prompted the development of seismic risk models that serve both to estimate the impact of these events and to explore the efficacy of different risk mitigation strategies. A core element of the risk modelling is ground-motion prediction models (GMPM) derived from an extensive database of recordings obtained from a dense network of accelerographs installed in the field. For the verification of damage claims, an empirical GMPM for peak ground velocity (PGV) has been developed, which predicts horizontal PGV as a function of local magnitude, ML; hypocentral distance, Rhyp; and the time-averaged shear-wave velocity over the upper 30 m, VS30. For modelling the risk due to potential induced and triggered earthquakes of larger magnitude, a GMPM for response spectral accelerations has been developed from regressions on the outputs from finite-rupture simulations of motions at a deeply buried rock horizon. The GMPM for rock motions is coupled with a zonation map defining frequency-dependent non-linear amplification factors to obtain estimates of surface motions in the region of thick deposits of soft soils. The GMPM for spectral accelerations is formulated within a logic-tree framework to capture the epistemic uncertainty associated with extrapolation from recordings of events of ML ≤ 3.6 to much larger magnitudes.

Amplification and Attenuation Due to Geologic Conditions in the Sichuan Basin, Central China

Abstract The Sichuan basin is an intracontinental basin in central China containing extensive Paleozoic, Mesozoic, and Cenozoic sedimentary sequences of varying thicknesses. We used seismic recordings from 189 regional earthquakes with magnitude Ms 4.0–7.0 occurring from 2010 to 2020 recorded by 22 stations from the China Digital Seismological Observation Network to estimate the kappa values (κ0) in the Sichuan basin using Lg waves and coda waves. The Lg and coda κ0 values agree well with each other and show positive correlation with the thickness of sediments in the basin. The average κ0 for the Sichuan basin is estimated as 0.0475 s, similar to results reported in previous studies. The average estimate of κ0 is 0.06 s for stations in the western part of the basin that are underlain by sediments approximately 8 km thick. The quality factor Q of Lg waves in the crustal waveguide is estimated as Q(f)=335f0.72. We estimated site response in the Sichuan basin from Lg spectral inversion, coda spectral ratios, and Lg horizontal to vertical spectral ratios, respectively, and investigated correlation between the site response values and sediment thickness. Consistent patterns are observed in the three sets of site response, which are clearly dependent on the thickness of sediments. Maximum averaged amplification factors of the sediments occur around 2–4 Hz reaching ∼4.5 for sites overlying 8 km sediments. Stronger attenuation effects are observed at stations lying on thicker sediment columns at frequencies higher than 9.66 Hz. This spatial dependence of low-frequency amplification and high-frequency attenuation should be incorporated in ground-motion prediction models for the Sichuan basin. The results of this study can be applied to sites in the Sichuan basin where the thickness of the sedimentary section exceeds 2 km.

Construction of a Ground-Motion Logic Tree through Host-to-Target Region Adjustments Applied to an Adaptable Ground-Motion Prediction Model

ABSTRACT The purpose of a median ground-motion logic tree is to capture the center, body, and range of possible ground-motion amplitudes for each earthquake scenario considered in a seismic hazard analysis. For site-specific hazard analyses, the traditional approach of populating the logic tree branches with ground-motion prediction models (GMPMs) selected and weighted on the basis of vaguely defined applicability to the target region is rapidly being abandoned in favor of the backbone GMPM approach. In this approach, the selected backbone model is first adjusted to match the earthquake source and path characteristics of the target region, and then it is separately adjusted to account for the site-specific geotechnical profile. For a GMPM to be amenable to such host-to-target adjustments, the magnitude scaling of response spectral ordinates should be consistent with the theoretical scaling of Fourier amplitude spectra. In addition, the influence of individual source and path parameters should be clearly distinguished in the model to allow the adjustments to be applied individually, and reliable estimates of the source and path parameters from the host region of the GMPM should be available, as should a reference rock profile for the model. The NGA-West2 project GMPM of Chiou and Youngs (2014; hereafter, CY14) has been identified as a very suitable backbone model. Moreover, rather than adopting generic source and path parameters and a rock site profile from the host region for CY14, which is not easily defined because the data from which it was derived came from several geographical locations, recent studies have inverted the model to obtain a CY14-consistent reference rock profile and CY14-compatible source and path parameters. Using these host-region characteristics, this study illustrates the process of building a ground-motion logic tree through the sequential application of multiple host-to-target-region adjustments, each represented by a node on the logic tree to achieve a tractable model for the total epistemic uncertainty.