Testing a model of earthquake nucleation

1995 ◽  
Vol 85 (6) ◽  
pp. 1873-1878
Author(s):  
Rachel E. Abercrombie ◽  
Duncan C. Agnew ◽  
Frank K. Wyatt
Keyword(s):  
Slow Slip ◽  
Dynamic Rupture ◽  
Short Term ◽  
Landers Earthquake ◽  
Earthquake Nucleation ◽  
Short Term Prediction ◽  
Stress Drops ◽  
1992 Landers Earthquake ◽  
Precursory Slip ◽  
Laboratory Models

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.

scholarly journals Do slow slip events trigger large and great megathrust earthquakes?

2018 ◽  
Vol 4 (10) ◽  
pp. eaat8472 ◽  
Author(s):  
N. Voss ◽  
T. H. Dixon ◽  
Z. Liu ◽  
R. Malservisi ◽  
M. Protti ◽  
...  
Keyword(s):  
Costa Rica ◽  
Power Law ◽  
Slip Rate ◽  
Failure Stress ◽  
Slow Slip ◽  
Short Term ◽  
Slow Slip Events ◽  
Megathrust Earthquakes ◽  
Coulomb Failure ◽  
Earthquake Nucleation

Slow slip events have been suggested to trigger subduction earthquakes. However, examples to date have been poorly recorded, occurring offshore, where data are sparse. Better understanding of slow slip events and their influence on subsequent earthquakes is critical for hazard forecasts. We analyze a well-recorded event beginning 6 months before the 2012 Mw (moment magnitude) 7.6 earthquake in Costa Rica. The event migrates to the eventual megathrust rupture. Peak slip rate reached a maximum of 5 mm/day, 43 days before the earthquake, remaining high until the earthquake. However, changes in Mohr-Coulomb failure stress at the hypocenter were small (0.1 bar). Our data contradict models of earthquake nucleation that involve power law acceleration of slip and foreshocks. Slow slip events may prove useful for short-term earthquake forecasts.

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
Keyword(s):  
Current Knowledge ◽  
Model Development ◽  
Active Faults ◽  
Constitutive Models ◽  
Dynamic Rupture ◽  
Peak Displacement ◽  
Infrastructure Systems ◽  
Landers Earthquake ◽  
Fault Displacement ◽  
1992 Landers Earthquake

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.

scholarly journals Migratory earthquake precursors are dominant on an ice stream fault

2021 ◽  
Vol 7 (6) ◽  
pp. eabd0105 ◽  
Author(s):  
G. Barcheck ◽  
E. E. Brodsky ◽  
P. M. Fulton ◽  
M. A. King ◽  
M. R. Siegfried ◽  
...  
Keyword(s):  
Large Scale ◽  
Slow Slip ◽  
West Antarctica ◽  
Stick Slip ◽  
Ice Stream ◽  
Earthquake Nucleation ◽  
Global Positioning ◽  
Passive Seismic ◽  
Seismic Records ◽  
Precursory Slip

Simple fault models predict earthquake nucleation near the eventual hypocenter (self-nucleation). However, some earthquakes have migratory foreshocks and possibly slow slip that travel large distances toward the eventual mainshock hypocenter (migratory nucleation). Scarce observations of migratory nucleation may result from real differences between faults or merely observational limitations. We use Global Positioning System and passive seismic records of the easily observed daily ice stream earthquake cycle of the Whillans Ice Plain, West Antarctica, to quantify the prevalence of migratory versus self-nucleation in a large-scale, natural stick-slip system. We find abundant and predominantly migratory precursory slip, whereas self-nucleation is nearly absent. This demonstration that migratory nucleation exists on a natural fault implies that more-observable migratory precursors may also occur before some earthquakes.

Development of a Technique for Short-Term Prediction of Hydrometeors Using Advection and Physical Forcing.

1983 ◽  
Author(s):  
Gregory S. Forbes ◽  
John J. Cahir ◽  
Paul B. Dorian ◽  
Walter D. Lottes ◽  
Kathy Chapman
Keyword(s):  
Short Term ◽  
Physical Forcing ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

2021 ◽  
Vol 11 (9) ◽  
pp. 4266
Author(s):  
Md. Shahriare Satu ◽  
Koushik Chandra Howlader ◽  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
Sheikh Mohammad Shariful Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Models ◽  
Short Term ◽  
Infected People ◽  
Infected Case ◽  
First Case ◽  
Novel Coronavirus ◽  
Short Term Prediction ◽  
Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

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