Validation of Fault Displacements from Dynamic Rupture Simulations against the Observations from the 1992 Landers Earthquake

2021 ◽  
Vol 111 (5) ◽  
pp. 2574-2594 ◽  
Author(s):  
Yongfei Wang ◽  
Christine Goulet

ABSTRACT Coseismic fault displacements in large earthquakes have caused significant damage to structures and lifelines on and near fault lines. Coseismic displacements represent a real threat, especially to distributed infrastructure systems. For infrastructure systems that can not avoid active faults, engineering displacement demands are defined using probabilistic fault-displacement hazard analyses (PFDHA). However, PFDHA models are sparse and poorly constrained partly due to the scarcity of detailed fault-displacement observations. Advancements in dynamic rupture simulation methods make them an attractive approach to address this important issue. Because fault displacements can be simulated for various geologic conditions as constrained by current knowledge about earthquake processes, they can be used to supplement the observation datasets. In addition to providing on-fault displacements, when used with appropriate constitutive models for the bulk medium, they can capture off-fault distributed inelastic deformations as well. For viable extrapolation, simulations must first be validated against data. In this article, we summarize the calibration and validation of the dynamic rupture model against the observations of the well-documented 1992 Landers earthquake. We defined a preferred model that reproduces several first-order fault-displacement metrics such as the on-fault partition of the total displacement, the mean fault-zone width, and the location of the peak displacement. Simulated ground motions consistent with the observations ensure that all physics important to modeling have been properly parameterized. For the extrapolation, we generated a suite of dynamic rupture models to quantify expected fault-displacement metrics, their intercorrelations, and magnitude dependencies, which are in part supported by the Landers and other recent earthquakes. Our validation and extrapolation exercise paves the way for using dynamic rupture modeling to quantitatively address fault-displacement hazard on a broader scale. The results are promising and are expected to be useful to inform PFDHA model development.

1995 ◽  
Vol 85 (6) ◽  
pp. 1873-1878
Author(s):  
Rachel E. Abercrombie ◽  
Duncan C. Agnew ◽  
Frank K. Wyatt

Abstract Some laboratory models of slip find that a critical amount (or velocity) of slow slip is required over a nucleation patch before dynamic failure begins. Typically, such patch sizes, when extrapolated to earthquakes, have been thought to be very small and the precursory slip undetectable. Ohnaka (1992, 1993) has proposed a model in which foreshocks delineate a growing zone of quasi-static slip that nucleates the dynamic rupture and suggests that it could be large enough (∼10 km across) to be detectable and thus useful for short-term earthquake prediction. The 1992 Landers earthquake (M 7.3) had a distinctive foreshock sequence and initiated only 70 km from the strain meters at the Piñon Flat Observatory (PFO). We use this earthquake to investigate the validity and usefulness of Ohnaka's model. The accurate relocations of Dodge et al. (1995) show that the foreshock zone can be interpreted as expanding from an area of 800 m (along strike) by 900 m (in depth), to 2000 by 3200 m in the 6.5 hr before the mainshock. We have calculated the deformation signals expected both at PFO and 20 km from the foreshock zone, assuming either constant slip or constant stress drop on a circular patch expanding at 5 cm/sec over 6.5 hr. We find the slips or stress drops would have to have been implausibly high (meters or kilobars) to have been detectable on the strain meters at PFO. Slightly better limits are possible only 20 km from the source. Even though the distance from Landers to PFO is small compared with the average spacing of strain meters in California, we are unable to prove or disprove Ohnaka's model of earthquake nucleation. This suggests that even if the model is valid, it will not be useful for short-term prediction.


2021 ◽  
Vol 9 ◽  
Author(s):  
Fu-Sheng Chou ◽  
Laxmi V. Ghimire

Background: Pediatric myocarditis is a rare disease. The etiologies are multiple. Mortality associated with the disease is 5–8%. Prognostic factors were identified with the use of national hospitalization databases. Applying these identified risk factors for mortality prediction has not been reported.Methods: We used the Kids' Inpatient Database for this project. We manually curated fourteen variables as predictors of mortality based on the current knowledge of the disease, and compared performance of mortality prediction between linear regression models and a machine learning (ML) model. For ML, the random forest algorithm was chosen because of the categorical nature of the variables. Based on variable importance scores, a reduced model was also developed for comparison.Results: We identified 4,144 patients from the database for randomization into the primary (for model development) and testing (for external validation) datasets. We found that the conventional logistic regression model had low sensitivity (~50%) despite high specificity (>95%) or overall accuracy. On the other hand, the ML model struck a good balance between sensitivity (89.9%) and specificity (85.8%). The reduced ML model with top five variables (mechanical ventilation, cardiac arrest, ECMO, acute kidney injury, ventricular fibrillation) were sufficient to approximate the prediction performance of the full model.Conclusions: The ML algorithm performs superiorly when compared to the linear regression model for mortality prediction in pediatric myocarditis in this retrospective dataset. Prospective studies are warranted to further validate the applicability of our model in clinical settings.


2019 ◽  
Vol 177 (5) ◽  
pp. 2163-2179 ◽  
Author(s):  
Percy Galvez ◽  
Paul Somerville ◽  
Anatoly Petukhin ◽  
Jean-Paul Ampuero ◽  
Daniel Peter

Nature ◽  
2000 ◽  
Vol 406 (6795) ◽  
pp. 500-504 ◽  
Author(s):  
Stephen S. Gao ◽  
Paul G. Silver ◽  
Alan T. Linde ◽  
I. Selwyn Sacks

1994 ◽  
Vol 84 (3) ◽  
pp. 625-645 ◽  
Author(s):  
K. W. Hudnut ◽  
Y. Bock ◽  
M. Cline ◽  
P. Fang ◽  
Y. Feng ◽  
...  

Abstract We present co-seismic displacement vectors derived from Global Positioning System (GPS) measurements of 92 stations in southern California. These GPS results are combined with five well-determined GPS displacement vectors from continuously tracking stations of the Permanent GPS Geodetic Array, as well as line-length changes from USGS Geodolite and two-color laser trilateration observations, to determine a self-consistent set of geodetic data for the earthquake. These combined displacements are modeled by an elastic dislocation representation of the primary fault rupture planes. On average, the model residuals are about twice the estimated measurement errors.


1996 ◽  
Vol 86 (1A) ◽  
pp. 255-258 ◽  
Author(s):  
Sharon Kedar ◽  
Hiroo Kanamori

Abstract We have developed a method to detect long-period precursors for large earthquakes observed in southern California, if they occur. The method allows us to continuously monitor seismic energy radiation over a wide frequency band to investigate slow deformation in the crust (e.g., slow earthquakes), especially before large earthquakes. We used the long-period records (1 sample/sec) from TERRAscope, a broadband seismic network in southern California. The method consists of dividing the record into a series of overlapping 30-min-long windows, computing the spectra over a frequency band of 0.00055 to 0.1 Hz, and plotting them in the form of a time-frequency diagram called spectrogram. This procedure is repeated daily over a day-long record. We have analyzed the 17 January 1994 Northridge earthquake (Mw = 6.7), and the 28 June 1992 Landers earthquake (Mw = 7.3). No slow precursor with spectral amplitude measured over a duration of 30 min larger than that of a magnitude 3.7 was detected prior to either event. In other words, there was no precursor whose moment was larger than ∼0.003% of the mainshock.


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