scholarly journals Full-Wave Ground Motion Forecast for Southern California

10.5772/50114 ◽  
2012 ◽  
Author(s):  
En-Jui Lee ◽  
Po Che
Keyword(s):  
Ground Motion ◽  
Southern California ◽  
Full Wave

Ground Motion Residuals, Path Effects, and Crustal Properties: A Pilot Study in Southern California

2019 ◽  
Vol 124 (6) ◽  
pp. 5738-5753 ◽  
Author(s):  
V. J. Sahakian ◽  
A. Baltay ◽  
T. C. Hanks ◽  
J. Buehler ◽  
F. L. Vernon ◽  
...  
Keyword(s):  
Pilot Study ◽  
Ground Motion ◽  
Southern California

Deterministic ground motion simulations with shallow crust nonlinearity at Garner Valley in Southern California

2020 ◽  
Vol 50 (1) ◽  
pp. 43-59
Author(s):  
Elnaz Seylabi ◽  
Doriam Restrepo ◽  
Ricardo Taborda ◽  
Domniki Asimaki
Keyword(s):  
Ground Motion ◽  
Southern California ◽  
Ground Motion Simulations ◽  
Shallow Crust

Event Detection Performance of the PLUM Earthquake Early Warning Algorithm in Southern California

2019 ◽  
Vol 109 (4) ◽  
pp. 1524-1541 ◽  
Author(s):  
Elizabeth S. Cochran ◽  
Julian Bunn ◽  
Sarah E. Minson ◽  
Annemarie S. Baltay ◽  
Deborah L. Kilb ◽  
...  
Keyword(s):  
Los Angeles ◽  
Ground Motion ◽  
Early Warning ◽  
Southern California ◽  
Ground Motions ◽  
Tohoku Earthquake ◽  
Prediction Equation ◽  
Earthquake Early Warning ◽  
Japan Meteorological Agency ◽  
Origin Time

Abstract We test the Japanese ground‐motion‐based earthquake early warning (EEW) algorithm, propagation of local undamped motion (PLUM), in southern California with application to the U.S. ShakeAlert system. In late 2018, ShakeAlert began limited public alerting in Los Angeles to areas of expected modified Mercalli intensity (IMMI) 4.0+ for magnitude 5.0+ earthquakes. Most EEW systems, including ShakeAlert, use source‐based methods: they estimate the location, magnitude, and origin time of an earthquake from P waves and use a ground‐motion prediction equation to identify regions of expected strong shaking. The PLUM algorithm uses observed ground motions directly to define alert areas and was developed to address deficiencies in the Japan Meteorological Agency source‐based EEW system during the 2011 Mw 9.0 Tohoku earthquake sequence. We assess PLUM using (a) a dataset of 193 magnitude 3.5+ earthquakes that occurred in southern California between 2012 and 2017 and (b) the ShakeAlert testing and certification suite of 49 earthquakes and other seismic signals. The latter suite includes events that challenge the current ShakeAlert algorithms. We provide a first‐order performance assessment using event‐based metrics similar to those used by ShakeAlert. We find that PLUM can be configured to successfully issue alerts using IMMI trigger thresholds that are lower than those implemented in Japan. Using two stations, a trigger threshold of IMMI 4.0 for the first station and a threshold of IMMI 2.5 for the second station PLUM successfully detect 12 of 13 magnitude 5.0+ earthquakes and issue no false alerts. PLUM alert latencies were similar to and in some cases faster than source‐based algorithms, reducing area that receives no warning near the source that generally have the highest ground motions. PLUM is a simple, independent seismic method that may complement existing source‐based algorithms in EEW systems, including the ShakeAlert system, even when alerting to light (IMMI 4.0) or higher ground‐motion levels.

Precariously balanced rocks and ground-motion maps for Southern California

1996 ◽  
Vol 86 (1A) ◽  
pp. 43-54 ◽  
Author(s):  
James N. Brune
Keyword(s):  
Ground Motion ◽  
Power Plants ◽  
Southern California ◽  
Time History ◽  
High Energy ◽  
Fault Creep ◽  
Rock Outcrops ◽  
Ground Shaking ◽  
Slow Earthquakes ◽  
Large Earthquakes

Abstract Groups of precariously balanced rocks are effectively low-resolution strong-motion seismoscopes that have been operating on solid rock outcrops for thousands of years and, once the methodology has been developed, can provide important information about seismic risk. In one zone, near Victorville, only 30 km from the nearest point on the San Andreas fault, more than 50 precarious rocks have been documented. Widespread rock varnish suggests that many of these rocks have been in their current unstable positions for thousands of years. We have established the mechanical basis for rough estimates of the horizontal accelerations necessary to topple these rocks, using field observations and numerical and physical modeling. To verify that zones of precarious rocks do not occur near historic earthquakes, searches using binoculars were made along roads, with occasional foot surveys, near large earthquakes. Based on these reconnaissance searches, we conclude that no precarious rock zones are found within 15 km of zones of high-energy release of historic large earthquakes. To document the occurrence of precarious rocks in southern California, road surveys were carried out along major roads. Four zones of precarious rocks and seven other zones of somewhat less precarious rocks have been documented. Published probabilistic ground-motion maps for southern California are compared with the occurrence of zones of precarious and semi-precarious rocks. The results are encouraging and suggest that eventually, studies of precarious rocks will provide important constraints on the assumptions on which the maps are based. Results from studies of precarious rocks may eventually provide important information for siting and design of sensitive structures such as hospitals and power plants. Precarious rocks give a direct indication of past ground shaking, in contrast to the indirect inference provided by fault-trenching studies, which may be subject to uncertainties in the actual time history of slip due to the fault (e.g., fault creep, “slow” earthquakes, or unknown dynamic stress drop). It is concluded that precarious rocks warrant further study and quantitative analysis.

Evaluation of Ground-Motion Models for USGS Seismic Hazard Models Using Near-Source Instrumental Ground-Motion Recordings of the Ridgecrest, California, Earthquake Sequence

2020 ◽  
Vol 110 (4) ◽  
pp. 1517-1529
Author(s):  
Daniel E. McNamara ◽  
Emily L. G. Wolin ◽  
Morgan P. Moschetti ◽  
Eric M. Thompson ◽  
Peter M. Powers ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Southern California ◽  
Epistemic Uncertainty ◽  
Site Amplification ◽  
Earthquake Sequence ◽  
Scoring Method ◽  
Motion Models ◽  
Tectonically Active ◽  
Ground Motion Models

ABSTRACT We evaluated the performance of 12 ground-motion models (GMMs) for earthquakes in the tectonically active shallow crustal region of southern California using instrumental ground-motion observations from the 2019 Ridgecrest, California, earthquake sequence (Mw 4.0–7.1). The sequence was well recorded by the Southern California Seismic Network and rapid response portable aftershock monitoring stations. Ground-motion recordings of this size and proximity are rare, valuable, and independent of GMM development, allowing us to evaluate the predictive powers of GMMs. We first compute total residuals and compare the probability density functions, means, and standard deviations of the observed and predicted ground motions. Next we use the total residuals as inputs to the probabilistic scoring method (log-likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) logic trees. We also explore GMM performance for a range of earthquake magnitudes, wave propagation distances, and site characteristics. We find that the Next Generation Attenuation West-2 (NGAW2) active crust GMMs perform well for the 2019 Ridgecrest, California, earthquake sequence and thus validate their use in the 2018 USGS NSHM. However, significant ground-motion residual scatter remains unmodeled by NGAW2 GMMs due to complexities such as local site amplification and source directivity. Results from this study will inform logic-tree weights for updates to the USGS National NSHM. Results from this study support the use of nonergodic GMMs that can account for regional attenuation and site variations to minimize epistemic uncertainty in USGS NSHMs.

scholarly journals Comparing Artificial Neural Networks with Traditional Ground-Motion Models for Small-Magnitude Earthquakes in Southern California

2021 ◽  
Author(s):  
Alexis Klimasewski ◽  
Valerie Sahakian ◽  
Amanda Thomas
Keyword(s):  
Machine Learning ◽  
Ground Motion ◽  
Southern California ◽  
Small Magnitude ◽  
Motion Models ◽  
Input Parameters ◽  
Ann Models ◽  
Artificial Neural ◽  
A Site ◽  
Ground Motion Models

ABSTRACT Traditional, empirical ground-motion models (GMMs) are developed by prescribing a functional form between predictive parameters and ground-motion intensity measures. Machine-learning techniques may serve as a fully data-driven alternative to widely used regression techniques, as they do not require explicitly defining these relationships. Although, machine-learning methods offer a nonparametric alternative to regression methods, there are few studies that develop and assess performance of traditional versus machine-learning GMMs side by side. We compare the performance and behavior of these two approaches: a mixed-effects maximum-likelihood (MEML) model and a feed-forward artificial neural network (ANN). We develop and train both models on the same dataset from southern California. We subsequently test both models on a dataset from the 2019 Ridgecrest sequence, in a new region and on magnitudes outside the range of the training dataset, to examine model portability. Our models estimate horizontal peak ground acceleration, and the input parameters include moment magnitude (M) and hypocentral distance (Rhyp), and some include a site parameter, either VS30 or κ0. We find that, with our small set of input parameters, the ANN generally shows more site-specific predictions than the MEML model with more variation between sites, and, performs better than their corresponding MEML model, when applied “blind” to our testing dataset (in which the MEML random effects cannot be considered). Although, previous studies have found that κ0 may be a better predictor of site effects than VS30, we found similar performance, suggesting that including a site parameter may be more important than the physical meaning of the parameter. Finally, when applying our models to our Ridgecrest dataset, we find that both methods perform well; however, the MEML models perform better with the new dataset than the ANN models, suggesting that future applications of ANN models may need to consider how to accommodate model portability.

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