Dynamic source inversion of the 2014 Mw6 South Napa, California, earthquake

2020 ◽  
Author(s):  
Jan Premus ◽  
Frantisek Gallovic
Keyword(s):  
Monte Carlo ◽  
Strong Motion ◽  
Inversion Method ◽  
Source Inversion ◽  
Dynamic Rupture ◽  
Dynamic Simulations ◽  
Simulation Code ◽  
Strong Motion Data ◽  
Motion Data ◽  
Dynamic Source

<p>Dynamic rupture modeling coupled with strong motion data fitting (dynamic source inversion) offers an insight into the rupture physics, constraining and enriching information gained from standard kinematic slip inversions. We utilize the Bayesian Monte Carlo dynamic source inversion method introduced recently by Gallovič et al. (2019), which, in addition to finding a best-fitting model, allows assessing uncertainties of the inferred parameters by sampling the posterior probability density function. The Monte Carlo approach requires running a large number (millions) of dynamic simulations due to the nonlinearity of the inverse problem. It is achieved by using GPU accelerated dynamic rupture simulation code FD3D_TSN (Premus et al., submitted) as a forward solver. We apply the inversion to the 2014 Mw6 South Napa, California, earthquake, employing strong motion data (up to 0.5 Hz) from the 10 closest stations. As an output, we obtain samples of the spatial distributions of dynamic parameters (prestress and parameters of the slip-weakening friction law). Regarding the rupture geometry, we consider two, presently ambiguous, fault planes (Pollitz et al., 2019), showing considerable differences in fitting seismograms in very close vicinity of the fault. We investigate properties of the rupture, especially in the region close to the free surface, and the viability of the model samples to explain the observed data in a broader frequency range (up to 5Hz).</p>

Joint interpretation of macroseismic and strong motion data for recent large shallow mainshocks of the Aegean area using a Monte Carlo optimization of finite-fault stochastic simulations

2021 ◽  
Author(s):  
Michail Ravnalis ◽  
Charalampos Kkallas ◽  
Constantinos Papazachos ◽  
Basil Margaris ◽  
Christos Papaioannou
Keyword(s):  
Monte Carlo ◽  
Stochastic Simulation ◽  
Strong Motion ◽  
Macroseismic Data ◽  
Simulation Approach ◽  
Aegean Area ◽  
Strong Motion Data ◽  
Motion Data ◽  
Finite Fault ◽  
Joint Interpretation

<p>The study of strong historical and early instrumental earthquakes is based almost exclusively on the use of their macroseismic data, which usually constrain the area that has suffered the heaviest damage. In the recent decades, strong-motion data have been also employed for the same purpose. We present a stochastic simulation approach to jointly model macroseismic and strong motion data for selected shallow strong (<strong>M</strong>≥6.0) earthquakes that occurred in the broader Aegean region between 1978 and 1995. For the simulations we employed the finite-fault stochastic simulation method, as realized by the EXSIM algorithm. We calibrated several parameters for the stochastic simulation modeling using a priori published information (e.g., moment magnitude, stress parameter). Other rupture zone information were collected from published works, such as fault plane solutions, relocated seismicity, etc. A Monte Carlo approach was adopted to perform a parametric search for the stress parameter and the modelling both independently and jointly the available macroseismic data and the strong motion instrumental recordings. The validity and the reliability of this semi-automated simulation approach was examined, to test if this method could be applied either in a fully automated manner, or for the study of the source properties of historical earthquakes. The results suggest that a joint-misfit minimization from the simultaneous simulation of macroseismic and strong motion data is a feasible target, that can be potentially employed for the simulation of older events, for which a limited number of instrumental data is often available. In general, a good agreement of the spatial distribution of the original and modeled macroseismic intensities is observed, showing that can reliably reconstruct the main features of the damage distribution for strong shallow mainshocks in the Aegean area using the proposed joint interpretation approach.</p>

Rupture parameters of dynamic source models compatible with NGA-West2 GMPEs

2020 ◽  
Author(s):  
Frantisek Gallovic ◽  
Lubica Valentova
Keyword(s):  
Ground Motions ◽  
Source Parameters ◽  
Strong Motion ◽  
Seismic Hazard Assessment ◽  
Response Spectra ◽  
Maximum Magnitude ◽  
Source Inversion ◽  
Dynamic Rupture ◽  
Heterogeneous Distribution ◽  
Dynamic Source

<p>Dynamic source inversions of individual earthquakes provide constraints on stress and frictional parameters, which are inherent to the studied event. However, general characteristics of both kinematic and dynamic rupture parameters are not well known, especially in terms of their variability. Here we constrain them by creating and analyzing a synthetic event database of dynamic rupture models that generate waveforms compatible with strong ground motions in a statistical sense.</p><p>We employ a framework that is similar to the Bayesian dynamic source inversion by Gallovič et al. (2019). Instead of waveforms of a single event, the data are represented by Ground Motion Prediction Equations (GMPEs), namely NGA-West2  (Boore et al., 2014). The Markov chain Monte Carlo technique produces samples of the dynamic source parameters with heterogeneous distribution on a fault. For all simulations, we assume a vertical 36x20km strike-slip fault, which limits our maximum magnitude to Mw<7. For dynamic rupture calculations, we employ upgraded finite-difference code FD3D_TSN (Premus et al., 2020) with linear slip-weakening friction law. Seismograms are calculated on a regular grid of phantom stations assuming a 1D velocity model using precalculated full wavefield Green's functions. The procedure results in a database with those dynamic rupture models that generate ground motions compatible with the GMPEs (acceleration response spectra in period band 0.5-5s) in terms of both median and variability.</p><p>The events exhibit various magnitudes and degrees of complexity (e.g. one or more asperities). We inspect seismologically determinable parameters, such as duration, moment rate spectrum, stress drop, size of the ruptured area, and energy budget, including their variabilities.  Comparison with empirically derived values and scaling relations suggests that the events are compatible with real earthquakes (Brune, 1970, Kanamori and Brodsky, 2004). Moreover, we investigate the stress and frictional parameters in terms of their scaling, power spectral densities, and possible correlations. The inferred statistical properties of the dynamic source parameters can be used for physics-based strong-motion modeling in seismic hazard assessment.</p>

Processed strong-motion data for the El Salvador earthquake of June 19, 1982

1988 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Sylvester Theodore Algermissen
Keyword(s):  
El Salvador ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data

Empirical equations for the prediction of PGA and pseudo spectral accelerations using Iranian strong-motion data

2017 ◽  
Vol 22 (1) ◽  
pp. 263-285 ◽  
Author(s):  
H. Zafarani ◽  
Lucia Luzi ◽  
Giovanni Lanzano ◽  
M. R. Soghrat
Keyword(s):  
Strong Motion ◽  
Empirical Equations ◽  
Strong Motion Data ◽  
Motion Data ◽  
Spectral Accelerations ◽  
Pseudo Spectral

Duration prediction of Chilean strong motion data using machine learning

2021 ◽  
Vol 109 ◽  
pp. 103253
Author(s):  
Sarit Chanda ◽  
M.C. Raghucharan ◽  
K.S.K. Karthik Reddy ◽  
Vasudeo Chaudhari ◽  
Surendra Nadh Somala
Keyword(s):  
Machine Learning ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data ◽  
Duration Prediction

Re-digitization of Strong Motion Data Recorded by SMAC-M Accelerographs

2021 ◽  
Vol 21 (1) ◽  
pp. 1_25-1_45
Author(s):  
Toshihide KASHIMA ◽  
Shin KOYAMA ◽  
Hiroto NAKAGAWA
Keyword(s):  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data

A comparison of two methods for earthquake source inversion using strong motion seismograms

1994 ◽  
Vol 37 (6) ◽  
Author(s):  
B. P. Cohee ◽  
G. C. Beroza
Keyword(s):  
Rise Time ◽  
Seismic Moment ◽  
Strong Motion ◽  
Rupture Time ◽  
Rupture Propagation ◽  
Rupture Velocity ◽  
Strong Motion Data ◽  
Motion Data ◽  
Inversion Methods ◽  
Window Method

In this paper we compare two time-domain inversion methods that have been widely applied to the problem of modeling earthquake rupture using strong-motion seismograms. In the multi-window method, each point on the fault is allowed to rupture multiple times. This allows flexibility in the rupture time and hence the rupture velocity. Variations in the slip-velocity function are accommodated by variations in the slip amplitude in each time-window. The single-window method assumes that each point on the fault ruptures only once, when the rupture front passes. Variations in slip amplitude are allowed and variations in rupture velocity are accommodated by allowing the rupture time to vary. Because the multi-window method allows greater flexibility, it has the potential to describe a wider range of faulting behavior; however, with this increased flexibility comes an increase in the degrees of freedom and the solutions are comparatively less stable. We demonstrate this effect using synthetic data for a test model of the Mw 7.3 1992 Landers, California earthquake, and then apply both inversion methods to the actual recordings. The two approaches yield similar fits to the strong-motion data with different seismic moments indicating that the moment is not well constrained by strong-motion data alone. The slip amplitude distribution is similar using either approach, but important differences exist in the rupture propagation models. The single-window method does a better job of recovering the true seismic moment and the average rupture velocity. The multi-window method is preferable when rise time is strongly variable, but tends to overestimate the seismic moment. Both methods work well when the rise time is constant or short compared to the periods modeled. Neither approach can recover the temporal details of rupture propagation unless the distribution of slip amplitude is constrained by independent data.

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