Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records

2021 ◽  
Author(s):  
Tomohisa Okazaki ◽  
Nobuyuki Morikawa ◽  
Asako Iwaki ◽  
Hiroyuki Fujiwara ◽  
Tomoharu Iwata ◽  
...  
Keyword(s):  
Ground Motion ◽  
Ground Acceleration ◽  
Site Specific ◽  
Motion Data ◽  
Motion Models ◽  
Proposed Model ◽  
Single Station ◽  
Input Variables ◽  
Ground Condition ◽  
Ground Motion Models

ABSTRACT Choosing the method for inputting site conditions is critical in reducing the uncertainty of empirical ground-motion models (GMMs). We apply a neural network (NN) to construct a GMM of peak ground acceleration that extracts site properties from ground-motion data instead of referring to ground condition variables given for each site. A key structure of the model is one-hot representations of the site ID, that is, specifying the collection site of each ground-motion record by preparing input variables corresponding to all observation sites. This representation makes the best use of the flexibility of NN to obtain site-specific properties while avoiding overfitting at sites where a small number of strong motions have been recorded. The proposed model exhibits accurate and robust estimations among several compared models in different aspects, including data-poor sites and strong motions from large earthquakes. This model is expected to derive a single-station sigma that evaluates the residual uncertainty under the specification of estimation sites. The proposed NN structure of one-hot representations would serve as a standard ingredient for constructing site-specific GMMs in general regions.

NGA-Subduction research program

2021 ◽  
pp. 875529302110560
Author(s):  
Yousef Bozorgnia ◽  
Norman A Abrahamson ◽  
Sean K Ahdi ◽  
Timothy D Ancheta ◽  
Linda Al Atik ◽  
...  
Keyword(s):  
Ground Motion ◽  
Research Program ◽  
Epistemic Uncertainty ◽  
Specific Model ◽  
Peak Ground Velocity ◽  
Ground Acceleration ◽  
Motion Data ◽  
Motion Models ◽  
Aleatory Variability ◽  
Adjustment Factors

This article summarizes the Next Generation Attenuation (NGA) Subduction (NGA-Sub) project, a major research program to develop a database and ground motion models (GMMs) for subduction regions. A comprehensive database of subduction earthquakes recorded worldwide was developed. The database includes a total of 214,020 individual records from 1,880 subduction events, which is by far the largest database of all the NGA programs. As part of the NGA-Sub program, four GMMs were developed. Three of them are global subduction GMMs with adjustment factors for up to seven worldwide regions: Alaska, Cascadia, Central America and Mexico, Japan, New Zealand, South America, and Taiwan. The fourth GMM is a new Japan-specific model. The GMMs provide median predictions, and the associated aleatory variability, of RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration (PSA) at oscillator periods ranging from 0.01 to 10 s. Three GMMs also quantified “within-model” epistemic uncertainty of the median prediction, which is important in regions with sparse ground motion data, such as Cascadia. In addition, a damping scaling model was developed to scale the predicted 5%-damped PSA of horizontal components to other damping ratios ranging from 0.5% to 30%. The NGA-Sub flatfile, which was used for the development of the NGA-Sub GMMs, and the NGA-Sub GMMs coded on various software platforms, have been posted for public use.

scholarly journals Selecting Ground-Motion Models for Site-Specific PSHA: Adaptability versus Applicability

2020 ◽  
Vol 110 (6) ◽  
pp. 2801-2815 ◽  
Author(s):  
Julian J. Bommer ◽  
Peter J. Stafford
Keyword(s):  
Ground Motion ◽  
The Body ◽  
Stochastic Simulations ◽  
Study Region ◽  
Site Specific ◽  
Target Region ◽  
Motion Models ◽  
Site Characteristics ◽  
Wide Range ◽  
Ground Motion Models

ABSTRACT Capturing the center, the body, and the range of ground-motion predictions is an indispensable element of site-specific probabilistic seismic hazard analyses (PSHAs), for which the logic tree is the ubiquitous tool in current practice. The criteria for selecting the ground-motion models (GMMs) used in such studies have generally been focused on their potential applicability to the region and site for which the PSHA is being conducted. However, except for applications within the few regions with abundant ground-motion databases, it will rarely be the case that GMMs can be identified, which are perfectly calibrated to the characteristics of the target study region in terms of source and path properties. A good match between the generic site amplification model within the GMM and the site-specific dynamic response characteristics is equally, if not more, unlikely. Consequently, adjustments are likely to be made to the selected GMMs to render them more applicable to the target region and site. Empirical adjustments for host-to-target-region source differences using local recordings are unlikely to be robust, unless these have been generated by earthquakes from a wide range of magnitudes. Empirical adjustments for site characteristics are impossible, unless there are recordings from the target site. Therefore, the preferred approach makes parametric adjustments to empirical GMMs, isolating each host-to-target difference to map the individual contributions to the epistemic uncertainty. For such an approach to be applied, the emphasis moves from selecting GMMs on the basis of their applicability to focusing on their amenability to being adjusted to the target region and site. An adaptable equation is characterized by well-constrained host-region source, path, and site characteristics and a functional form in which response spectral accelerations scale with source, path, and site characteristics in a manner similar to the scaling implicit in stochastic simulations based on Fourier amplitude spectra.

A Link between Machine Learning and Optimization in Ground-Motion Model Development: Weighted Mixed-Effects Regression with Data-Driven Probabilistic Earthquake Classification

2020 ◽  
Vol 110 (6) ◽  
pp. 2777-2800
Author(s):  
Sebastian von Specht ◽  
Fabrice Cotton
Keyword(s):  
Ground Motion ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Data Driven ◽  
Weighted Likelihood ◽  
Motion Data ◽  
Motion Models ◽  
Uncertainty Estimates ◽  
Data Weighting ◽  
Ground Motion Models

ABSTRACT The steady increase of ground-motion data not only allows new possibilities but also comes with new challenges in the development of ground-motion models (GMMs). Data classification techniques (e.g., cluster analysis) do not only produce deterministic classifications but also probabilistic classifications (e.g., probabilities for each datum to belong to a given class or cluster). One challenge is the integration of such continuous classification in regressions for GMM development such as the widely used mixed-effects model. We address this issue by introducing an extension of the mixed-effects model to incorporate data weighting. The parameter estimation of the mixed-effects model, that is, fixed-effects coefficients of the GMMs and the random-effects variances, are based on the weighted likelihood function, which also provides analytic uncertainty estimates. The data weighting permits for earthquake classification beyond the classical, expert-driven, binary classification based, for example, on event depth, distance to trench, style of faulting, and fault dip angle. We apply Angular Classification with Expectation–maximization, an algorithm to identify clusters of nodal planes from focal mechanisms to differentiate between, for example, interface- and intraslab-type events. Classification is continuous, that is, no event belongs completely to one class, which is taken into account in the ground-motion modeling. The theoretical framework described in this article allows for a fully automatic calibration of ground-motion models using large databases with automated classification and processing of earthquake and ground-motion data. As an example, we developed a GMM on the basis of the GMM by Montalva et al. (2017) with data from the strong-motion flat file of Bastías and Montalva (2016) with ∼2400 records from 319 events in the Chilean subduction zone. Our GMM with the data-driven classification is comparable to the expert-classification-based model. Furthermore, the model shows temporal variations of the between-event residuals before and after large earthquakes in the region.

Conditional Ground-Motion Models for Horizontal Peak Ground Displacement for Active Crustal Regions

2021 ◽  
Author(s):  
Chih-Hsuan Sung ◽  
Norman A. Abrahamson ◽  
Jyun-Yan Huang
Keyword(s):  
Ground Motion ◽  
Earthquake Engineering ◽  
Input Parameter ◽  
Strong Motion ◽  
Tectonic Deformation ◽  
Ground Displacement ◽  
Strong Motion Data ◽  
Motion Data ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT Ground-motion models (GMMs) are developed for peak ground displacement (PGD) and for bandlimited PGD based on strong-motion data that has been filtered as part of standard processing and the total PGD that includes the tectonic deformation as well as the vibratory ground motion. For the bandlimited PGD, we develop conditional ground-motion models (CGMMs) using subsets of the Pacific Earthquake Engineering Research Center Next Generation Attenuation-West2 Project (NGA-W2) database and the National Center for Research on Earthquake Engineering Taiwan Senior Seismic Hazard Analysis Committee level 3 project database. The CGMM approach includes the observed pseudospectral acceleration (PSA(T)) as an input parameter in addition to magnitude and distance. The period of the PSA(T) is used as an input parameter; it is magnitude dependent and is based on the period for which there is the highest correlation between the ln(PGD) and ln(PSA(T)). Two CGMMs are developed: a global model based on the NGA-W2 data and a region-specific model for Taiwan. The conditional PGD models are combined with traditional GMMs for PSA(T) values to develop GMMs for both the median and standard deviation of PGD without the dependence on PSA. A second set of PGD GMMs are developed to correct for two factors: the effect of the high-pass filtering from standard record processing and the stronger large magnitude (M>6.5) scaling due to tectonic deformation. For magnitudes greater than 7, the PGD values from the total PGD GMMs are 2–5 times larger than the bandlimited PGD values based on the strong-motion data sets, but the increase is at very long periods. The appropriate PGD model to use, bandlimited PGD or total PGD, depends on the period range of interest for the specific engineering application.

NGA-subduction global ground motion models with regional adjustment factors

2021 ◽  
pp. 875529302110348
Author(s):  
Grace A Parker ◽  
Jonathan P Stewart ◽  
David M Boore ◽  
Gail M Atkinson ◽  
Behzad Hassani
Keyword(s):  
Ground Motion ◽  
Site Response ◽  
Epistemic Uncertainty ◽  
Peak Ground Velocity ◽  
Earthquake Ground Motion ◽  
Motion Models ◽  
Magnitude Scaling ◽  
Aleatory Variability ◽  
Single Station ◽  
Ground Motion Models

We develop semi-empirical ground motion models (GMMs) for peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral accelerations for periods from 0.01 to 10 s, for the median orientation-independent horizontal component of subduction earthquake ground motion. The GMMs are applicable to interface and intraslab subduction earthquakes in Japan, Taiwan, Mexico, Central America, South America, Alaska, the Aleutian Islands, and Cascadia. The GMMs are developed using a combination of data inspection, data regression with respect to physics-informed functions, ground-motion simulations, and geometrical constraints for certain model components. The GMMs capture observed differences in source and path effects for interface and intraslab events, conditioned on moment magnitude, rupture distance, and hypocentral depth. Site effect and aleatory variability models are shared between event types. Regionalized GMM components include the model constant (that controls ground motion amplitude), anelastic attenuation, magnitude-scaling break point, linear site response, and sediment depth terms. We develop models for the aleatory between-event variability [Formula: see text], within-event variability [Formula: see text], single-station within-event variability [Formula: see text], and site-to-site variability [Formula: see text]. Ergodic analyses should use the median GMM and aleatory variability computed using the between-event and within-event variability models. An analysis incorporating non-ergodic site response should use the median GMM at the reference shear-wave velocity condition, a site-specific site response model, and aleatory variability computed using the between-event and single-station within-event variability models. Epistemic uncertainty in the median model is represented by standard deviations on the regional model constants, which facilitates scaled-backbone representations of model uncertainty in hazard analyses.

Localizing Ground-Motion Models in Volcanic Terranes: Shallow Events at Mt. Etna, Italy, Revisited

2020 ◽  
Vol 110 (6) ◽  
pp. 2843-2861
Author(s):  
Giuseppina Tusa ◽  
Horst Langer ◽  
Raffaele Azzaro
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Total Error ◽  
Scaling Law ◽  
Peak Ground Velocity ◽  
Ground Acceleration ◽  
Hypocentral Distance ◽  
Motion Models ◽  
Mt Etna ◽  
Ground Motion Models

ABSTRACT We present a set of revised ground-motion models (GMMs) for shallow events at Mt. Etna Volcano. The recent occurrence of damaging events, in particular two of the strongest earthquakes ever instrumentally recorded in the area, has required revising previous GMMs, as these failed to match the observations made for events with local magnitude ML>4.3, above all for sites situated close to the epicenter. The dataset now includes 49 seismic events, with a total of 1600 time histories recorded at distances of up to 100 km, and ML ranging from 3.0 to 4.8. The model gives estimates of peak ground acceleration (both horizontal and vertical), peak ground velocity (both horizontal and vertical), and 5% damped horizontal pseudoacceleration response spectral ordinates up to a period of 4 s. GMMs were developed using the functional form proposed by Boore and Atkinson (2008). Furthermore, with a slightly modified approach, we also considered a regression model using a pseudodepth (h) depending on magnitude according to the scaling law by Azzaro et al. (2017). Both models were applied to hypocentral distance ranges of up to 60 km and up to 100 km, respectively. From the statistical analysis, we found that reducing the maximum distance from the event up to 60 km and introducing a magnitude-dependent pseudodepth improved the model in terms of total error. We compared our results with those derived using the GMMs for shallow events at Mt. Etna found by Tusa and Langer (2016) and for volcanic areas by Lanzano and Luzi (2019). The main differences are observed at short epicentral distances and for higher magnitude events. The use of variable pseudodepth avoids sharp peaks of predicted ground-motion parameters around the epicenter, preventing instabilities when using a GMM in probabilistic seismic hazard analysis.

Arias Intensity Conditional Scaling Ground‐Motion Models for Subduction Zones

2019 ◽  
Vol 109 (4) ◽  
pp. 1343-1357 ◽  
Author(s):  
Jorge Macedo ◽  
Norman Abrahamson ◽  
Jonathan D. Bray
Keyword(s):  
Ground Motion ◽  
Subduction Zones ◽  
Spectral Acceleration ◽  
Scaling Effects ◽  
Arias Intensity ◽  
Ground Acceleration ◽  
Earthquake Scenario ◽  
Motion Models ◽  
Conditional Models ◽  
Ground Motion Models

Abstract Conditional ground‐motion models (CGMMs) for estimating Arias intensity (IA) for earthquakes in subduction zones are developed. The estimate of IA is conditioned in these models on the estimated peak ground acceleration (PGA), the spectral acceleration at T=1  s (SA1), time‐averaged shear‐wave velocity in the top 30 m (VS30), and magnitude (Mw). Random‐effects regressions are used to develop CGMMs for Japan, Taiwan, South America, and New Zealand. By combining the conditional models of IA with the ground‐motion models (GMMs) for PGA and SA1, the conditional models are converted to scenario‐based GMMs that can be used to estimate the median IA and its standard deviation directly for a given earthquake scenario and site conditions. The conditional scaling approach ensures the estimated IA values are consistent with a design spectrum that may correspond to above‐average spectral values for the controlling scenario. In addition, this approach captures the complex ground‐motion scaling effects found in GMMs for spectral acceleration, such as sediment‐depth effects, soil nonlinearity effects, and regionalization effects, in the developed scenario‐based models for IA. Estimates from the new scenario‐based IA models are compared to those from traditional GMMs for IA in subduction zones.

scholarly journals Regional broad-band ground-shaking modelling over extended and thick sedimentary basins: an example from the Lower Rhine Embayment (Germany)

2020 ◽  
Author(s):  
Marco Pilz ◽  
Fabrice Cotton ◽  
Hoby Njara Tendrisoa Razafindrakoto ◽  
Graeme Weatherill ◽  
Thomas Spies
Keyword(s):  
Ground Motion ◽  
Broad Band ◽  
Soft Rock ◽  
Sedimentary Basins ◽  
Response Spectra ◽  
Site Specific ◽  
Ground Shaking ◽  
Motion Models ◽  
Ground Motion Models ◽  
Lower Rhine Embayment

AbstractThe simulation of broad-band (0.1 to 10 + Hz) ground-shaking over deep and spatially extended sedimentary basins at regional scales is challenging. We evaluate the ground-shaking of a potential M 6.5 earthquake in the southern Lower Rhine Embayment, one of the most important areas of earthquake recurrence north of the Alps, close to the city of Cologne in Germany. In a first step, information from geological investigations, seismic experiments and boreholes is combined for deriving a harmonized 3D velocity and attenuation model of the sedimentary layers. Three alternative approaches are then applied and compared to evaluate the impact of the sedimentary cover on ground-motion amplification. The first approach builds on existing response spectra ground-motion models whose amplification factors empirically take into account the influence of the sedimentary layers through a standard parameterization. In the second approach, site-specific 1D amplification functions are computed from the 3D basin model. Using a random vibration theory approach, we adjust the empirical response spectra predicted for soft rock conditions by local site amplification factors: amplifications and associated ground-motions are predicted both in the Fourier and in the response spectra domain. In the third approach, hybrid physics-based ground-motion simulations are used to predict time histories for soft rock conditions which are subsequently modified using the 1D site-specific amplification functions computed in method 2. For large distances and at short periods, the differences between the three approaches become less notable due to the significant attenuation of the sedimentary layers. At intermediate and long periods, generic empirical ground-motion models provide lower levels of amplification from sedimentary soils compared to methods taking into account site-specific 1D amplification functions. In the near-source region, hybrid physics-based ground-motions models illustrate the potentially large variability of ground-motion due to finite source effects.

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