Considering Spatial Correlation in Mixed-Effects Regression and the Impact on Ground-Motion Models

2010 ◽  
Vol 100 (6) ◽  
pp. 3295-3303 ◽  
Author(s):  
N. Jayaram ◽  
J. W. Baker
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Mixed Effects ◽  
Motion Models ◽  
Ground Motion Models ◽  
The Impact

scholarly journals An Advanced Estimation Algorithm for Ground‐Motion Models with Spatial Correlation

2019 ◽  
Vol 109 (2) ◽  
pp. 541-566 ◽  
Author(s):  
Deyu Ming ◽  
Chen Huang ◽  
Gareth W. Peters ◽  
Carmine Galasso
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Estimation Algorithm ◽  
Motion Models ◽  
Ground Motion Models

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.

A Suite of Alternative Ground-Motion Models (GMMs) for Israel

2021 ◽  
Author(s):  
Soumya Kanti Maiti ◽  
Gony Yagoda-Biran ◽  
Ronnie Kamai
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Empirical Data ◽  
Epistemic Uncertainty ◽  
Ground Motions ◽  
Plate Boundary ◽  
Strong Motion ◽  
Engineering Applications ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT Models for estimating earthquake ground motions are a key component in seismic hazard analysis. In data-rich regions, these models are mostly empirical, relying on the ever-increasing ground-motion databases. However, in areas in which strong-motion data are scarce, other approaches for ground-motion estimates are sought, including, but not limited to, the use of simulations to replace empirical data. In Israel, despite a clear seismic hazard posed by the active plate boundary on its eastern border, the instrumental record is sparse and poor, leading to the use of global models for hazard estimation in the building code and all other engineering applications. In this study, we develop a suite of alternative ground-motion models for Israel, based on an empirical database from Israel as well as on four data-calibrated synthetic databases. Two host models are used to constrain model behavior, such that the epistemic uncertainty is captured and characterized. Despite the lack of empirical data at large magnitudes and short distances, constraints based on the host models or on the physical grounds provided by simulations ensure these models are appropriate for engineering applications. The models presented herein are cast in terms of the Fourier amplitude spectra, which is a linear, physical representation of ground motions. The models are suitable for shallow crustal earthquakes; they include an estimate of the median and the aleatory variability, and are applicable in the magnitude range of 3–8 and distance range of 1–300 km.

Testing non-linear amplification factors used in ground motion models

2021 ◽  
Author(s):  
Karina Loviknes ◽  
Danijel Schorlemmer ◽  
Fabrice Cotton ◽  
Sreeram Reddy Kotha
Keyword(s):  
Ground Motion ◽  
Site Effects ◽  
Ground Motions ◽  
Site Amplification ◽  
Building Codes ◽  
Linear Amplification ◽  
Motion Models ◽  
Earthquake Predictability ◽  
Non Linear ◽  
Ground Motion Models

<p>Non-linear site effects are mainly expected for strong ground motions and sites with soft soils and more recent ground-motion models (GMM) have started to include such effects. Observations in this range are, however, sparse, and most non-linear site amplification models are therefore partly or fully based on numerical simulations. We develop a framework for testing of non-linear site amplification models using data from the comprehensive Kiban-Kyoshin network in Japan. The test is reproducible, following the vision of the Collaboratory for the Study of Earthquake Predictability (CSEP), and takes advantage of new large datasets to evaluate <span>whether or not</span> non-linear site effects predicted by site-amplification models are supported by empirical data. The site amplification models are tested using residuals between the observations and predictions from a GMM based only on magnitude and distance. When the GMM is derived without any site term, the site-specific variability extracted from the residuals is expected to capture the site response of a site. The non-linear site amplification models are tested against a linear amplification model on individual well-record<span>ing</span> stations. Finally, the result is compared to building codes where non-linearity is included. The test shows that for most of the sites selected as having sufficient records, the non-linear site-amplification models do not score better than the linear amplification model. This suggests that including non-linear site amplification in GMMs and building codes may not yet be justified, at least not in the range of ground motions considered in the test (peak ground acceleration < 0.2 g).</p>

Relational database used for ground-motion model development in the NGA-sub project

2021 ◽  
pp. 875529302110552
Author(s):  
Silvia Mazzoni ◽  
Tadahiro Kishida ◽  
Jonathan P Stewart ◽  
Victor Contreras ◽  
Robert B Darragh ◽  
...  
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Data Storage ◽  
Relational Database ◽  
Model Development ◽  
Intensity Measures ◽  
Motion Models ◽  
Ground Motion Model ◽  
Ground Motion Models ◽  
Pseudo Spectral

The Next-Generation Attenuation for subduction zone regions project (NGA-Sub) has developed data resources and ground motion models for global subduction zone regions. Here we describe the NGA-Sub database. To optimize the efficiency of data storage, access, and updating, data resources for the NGA-Sub project are organized into a relational database consisting of 20 tables containing data, metadata, and computed quantities (e.g. intensity measures, distances). A database schema relates fields in tables to each other through a series of primary and foreign keys. Model developers and other users mostly interact with the data through a flatfile generated as a time-stamped output of the database. We describe the structure of the relational database, the ground motions compiled for the project, and the means by which the data can be accessed. The database contains 71,340 three-component records from 1880 earthquakes from seven global subduction zone regions: Alaska, Central America and Mexico, Cascadia, Japan, New Zealand, South America, and Taiwan. These data were processed on a component-specific basis to minimize noise effects in the data and remove baseline drifts. Provided ground motion intensity measures include peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations for a range of oscillator periods.

Are Near-Fault Fling Effects Captured in the New NGA West2 Ground Motion Models?

2015 ◽  
Vol 31 (3) ◽  
pp. 1629-1645 ◽  
Author(s):  
Ronnie Kamai ◽  
Norman Abrahamson
Keyword(s):  
Ground Motion ◽  
Vertical Component ◽  
Strong Motion ◽  
Motion Processing ◽  
Large Set ◽  
Pass Filter ◽  
Motion Models ◽  
Finite Fault ◽  
Maximum Period ◽  
Ground Motion Models

We evaluate how much of the fling effect is removed from the NGA database and accompanying GMPEs due to standard strong motion processing. The analysis uses a large set of finite-fault simulations, processed with four different high-pass filter corners, representing the distribution within the PEER ground motion database. The effects of processing on the average horizontal component, the vertical component, and peak ground motion values are evaluated by taking the ratio between unprocessed and processed values. The results show that PGA, PGV, and other spectral values are not significantly affected by processing, partly thanks to the maximum period constraint used when developing the NGA GMPEs, but that the bias in peak ground displacement should not be ignored.

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