Ranking and Selection of Earthquake Ground-Motion Models Using the Stochastic Area Metric

2021 ◽  
Author(s):  
Jaleena Sunny ◽  
Marco De Angelis ◽  
Benjamin Edwards
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Cumulative Distribution ◽  
Earthquake Ground Motion ◽  
Motion Data ◽  
Motion Models ◽  
Area Metric ◽  
Few Data ◽  
Ground Motion Models ◽  
Selection Of

Abstract We introduce the cumulative-distribution-based area metric (AM)—also known as stochastic AM—as a scoring metric for earthquake ground-motion models (GMMs). The AM quantitatively informs the user of the degree to which observed or test data fit with a given model, providing a rankable absolute measure of misfit. The AM considers underlying data distributions and model uncertainties without any assumption of form. We apply this metric, along with existing testing methods, to four GMMs in order to test their performance using earthquake ground-motion data from the Preston New Road (United Kingdom) induced seismicity sequences in 2018 and 2019. An advantage of the proposed approach is its applicability to sparse datasets. We, therefore, focus on the ranking of models for discrete ranges of magnitude and distance, some of which have few data points. The variable performance of models in different ranges of the data reveals the importance of considering alternative models. We extend the ranking of GMMs through analysis of intermodel variations of the candidate models over different ranges of magnitude and distance using the AM. We find the intermodel AM can be a useful tool for selection of models for the logic-tree framework in seismic-hazard analysis. Overall, the AM is shown to be efficient and robust in the process of selection and ranking of GMMs for various applications, particularly for sparse and small-sized datasets.

Ranking and calibration of ground-motion models using the stochastic area metric. 

2021 ◽  
Author(s):  
Jaleena Sunny ◽  
Marco De Angelis ◽  
Ben Edwards
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Strong Motion ◽  
Statistical Testing ◽  
Testing Methods ◽  
Motion Models ◽  
Area Metric ◽  
Scenario Earthquakes ◽  
Ground Motion Variability ◽  
Ground Motion Models

<p>The selection and ranking of  Ground Motion Models (GMMs) for scenario earthquakes is a crucial element in seismic hazard analysis. Typically model testing and ranking do not appropriately account for uncertainties, thus leading to improper ranking. We introduce the stochastic area metric (AM) as a scoring metric for GMMs, which not only informs the analyst of the degree to which observed or test data fit the model but also considers the uncertainties without the assumption of how data are distributed. The AM can be used as a scoring metric or cost function, whose minimum value identifies the model that best fits a given dataset. We apply this metric along with existing testing methods to recent and commonly used European ground motion prediction equations: Bindi et al. (2014, B014), Akkar et al. (2014, A014) and Cauzzi et al. (2015, C015). The GMMs are ranked and their performance analysed against the European Engineering Strong Motion (ESM) dataset. We focus on the ranking of models for ranges of magnitude and distance with sparse data, which pose a specific problem with other statistical testing methods. The performance of models over different ranges of magnitude and distance were analysed using AM, revealing the importance of considering different models for specific applications (e.g., tectonic, induced). We find the A014 model displays good performance with complete dataset while B014 appears to be best for small magnitudes and distances. In addition, we calibrated GMMs derived from a compendium of data and generated a suite of models for the given region through an optimisation technique utilising the concept of AM and ground motion variability. This novel framework for ranking and calibration guides the informed selection of models and helps develop regionally adjusted and application-specific GMMs for better prediction. </p><p> </p>

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.

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

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

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

Exploring the Proximity of Ground-Motion Models Using High-Dimensional Visualization Techniques

2010 ◽  
Vol 26 (4) ◽  
pp. 1117-1138 ◽  
Author(s):  
Frank Scherbaum ◽  
Nicolas M. Kuehn ◽  
Matthias Ohrnberger ◽  
Andreas Koehler
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Ground Motions ◽  
High Dimensional ◽  
Logic Tree ◽  
Motion Models ◽  
Metric Set ◽  
Motion Characteristics ◽  
Visualization Techniques ◽  
Ground Motion Models

Logic trees have become a popular tool to capture epistemic uncertainties in seismic hazard analysis. They are commonly used by assigning weights to models on a purely descriptive basis (nominal scale). This invites the creation of unintended inconsistencies regarding the weights on the corresponding hazard curves. On the other hand, for human experts it is difficult to confidently express degrees-of-beliefs in particular numerical values. Here we demonstrate for ground-motion models how the model and the value-based perspectives can be partially reconciled by using high-dimensional information-visualization techniques. For this purpose we use Sammon's (1969) mapping and self-organizing mapping to project ground-motion models onto a two-dimensional map (an ordered metric set). Here they can be evaluated jointly according to their proximity in predicting similar ground motions, potentially making the assignment of logic tree weights consistent with their ground motion characteristics without having to abandon the model-based perspective.

BSHAP project strong ground motion database and selection of suitable ground motion models for the Western Balkan Region

2016 ◽  
Vol 15 (4) ◽  
pp. 1319-1343 ◽  
Author(s):  
Radmila Salic ◽  
M. Abdullah Sandikkaya ◽  
Zoran Milutinovic ◽  
Zeynep Gulerce ◽  
Llambro Duni ◽  
...  
Keyword(s):  
Ground Motion ◽  
Strong Ground Motion ◽  
Motion Models ◽  
Western Balkan Region ◽  
Balkan Region ◽  
Ground Motion Models ◽  
Strong Ground ◽  
Selection Of

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.

Comparison of Near-Fault Velocity Pulse-Like Ground Motions from the 2018 Mw 6.4 Hualien, Taiwan, Earthquake with the Next Generation Attenuation (NGA)-West2 Ground-Motion Models and Directivity Models

2021 ◽  
Author(s):  
Xiaofen Zhao ◽  
Zengping Wen ◽  
Junju Xie ◽  
Quancai Xie ◽  
Kuo-En Ching
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Ground Motions ◽  
Spectral Shape ◽  
Next Generation ◽  
Motion Models ◽  
Near Fault ◽  
Short Period ◽  
Shape Curve ◽  
Ground Motion Models

ABSTRACT Pulse-like ground motions cause severe damage in structures at certain periods. Hence, pulse effects need to be considered during probabilistic seismic hazard analysis and seismic design in the near-fault region. Traditional ground-motion models used to quantify the hazard posed by pulse-like ground motions may underestimate them, but they are relatively suitable for describing the residual ground motions after extracting pulses. Nevertheless, the applicability of Next Generation Attenuation-West2 Project (NGA-West2) models to pulse and residual ground motions has not been evaluated. Moreover, the applicability of recently developed directivity models, including the Shahi and Baker (2011; hereafter, SB2011), Chang et al. (2018; hereafter, Chang2018), and Rupakhety et al. (2011; hereafter, Rupakhety2011) models, has not been investigated for this event. Here, based on the abundance of pulse-like ground motions recorded during the Mw 6.4 Hualien earthquake, the applicability of NGA-West2 models and directivity models was quantitatively evaluated. In summary, (1) The applicability of NGA-West2 models to the observed original and residual ground motions varies significantly at different periods. The suggests that NGA-West2 models overestimate the original and residual ground motions for short periods (T<1.0  s), but are suitable for describing the residual ground motions yet underestimate the original ground motions for long periods (T≥1.0  s). (2) Pulse periods and amplification bands due to pulses are unusually larger than previous events. Similar to the Chang2018 model, the plateau of this event starts and ends at the periods of 0.70 and 1.1 times the pulse period. However, the Chang2018 and SB2011 models underestimate the constant ordinate of this plateau. Spectral ordinates of the spectral shape curve due to pulses for the short period (∼Tn<1.3  s) are smaller than the predictions from the Rupakhety2011 model. The trend was reversed for long periods (∼Tn>3.0  s). Compared with the Rupakhety2011 model, the peak location of the spectral shape curve is shifted to the long period. These results will be helpful for updating these models in the near future.

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.

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