scholarly journals Estimation of dynamic rupture parameters from the radiated seismic energy and apparent stress

2000 ◽  
Vol 27 (23) ◽  
pp. 3945-3948 ◽  
Author(s):  
N. Pulido ◽  
K. Irikura
Keyword(s):  
Seismic Energy ◽  
Apparent Stress ◽  
Dynamic Rupture ◽  
Radiated Seismic Energy

Stress drop estimates for some aftershocks of the Ometepec, Guerrero, Mexico, earthquake of June 7, 1982

1987 ◽  
Vol 24 (8) ◽  
pp. 1727-1733 ◽  
Author(s):  
Cecilio J. Rebollar ◽  
Rosa M. Alvarez
Keyword(s):  
Stress Drop ◽  
Wave Attenuation ◽  
Seismic Energy ◽  
Coda Wave ◽  
Apparent Stress ◽  
Radiated Seismic Energy ◽  
Seismic Coda ◽  
Seismic Quality Factor ◽  
Single Station ◽  
Stress Drops

Brune's stress drop, apparent stress, and arms stress drop are estimated at a single station for 25 aftershocks of the Ometepec earthquakes (Ms = 6.9 and Ms = 7.0). The arms stress drops and apparent stresses are systematically smaller than Brune's stress drops. Stress drops from the root mean square of acceleration and apparent stress range from 0.01 to 10.2 bars (1 bar = 100 kPa) except for two values (21.4 and 33.0 bars). On the other hand, Brune's stress drops range from 0.6 to 239 bars. Seismic moments ranging from 0.5 × 1019 to 289 × 1019 dyn∙cm (1 dyn∙cm = 10 μN∙cm) were estimated for events with coda magnitudes between 0.6 and 2.2. Values of radiated seismic energy calculated by integration of the displacement spectra range from 2.5 × 1012 to 2.3 × 1016 dyn∙cm. The fmax values lie between 16 and 30 Hz. Seismic coda wave attenuation measured on narrow band-pass-filtered seismograms show a linear dependence of the seismic quality factor of the form [Formula: see text] in the range of frequencies from 3 to 24 Hz.

The Coda Calibration and Processing Tool: Java-Based Freeware for the Geophysical Community

2020 ◽  
Author(s):  
Kevin Mayeda ◽  
Rengin Gok ◽  
Justin Barno ◽  
William Walter ◽  
Jorge Roman-Nieves
Keyword(s):  
Seismic Energy ◽  
Corner Frequency ◽  
Measurement Information ◽  
Apparent Stress ◽  
Source Information ◽  
Source Discrimination ◽  
Radiated Seismic Energy ◽  
Source Spectra ◽  
Stable Source ◽  
Calibration Tool

<p>The coda magnitude method of <em>Mayeda and Walter</em> (1996) provides stable source spectra and moment magnitudes (<em>M</em><em><sub>w</sub></em>) for local to regional events from as few as one station that are virtually insensitive to source and path heterogeneity. The method allows for a consistent measure of <em>M</em><em><sub>w</sub></em> over a broad range of event sizes rather than relying on empirical magnitude relationships that attempt to tie various narrowband relative magnitudes (<em>e.g.,</em> <em>M</em><em><sub>L</sub>, M<sub>D</sub>, m<sub>b</sub></em>, etc.) to absolute <em>M</em><em><sub>w </sub></em>derived from long-period waveform modeling. The use of <em>S</em>-coda and <em>P</em>-coda envelopes has been well documented over the past several decades for stable source spectra, apparent stress scaling, and hazard studies. However, up until recently, the method requires extensive calibration effort and routine operational use was limited only to proprietary US NDC software. The Coda Calibration Tool (CCT) stems from a multi-year collaboration between the US NDC and LLNL scientists with the goal of developing a fast and easy Java-based, platform independent coda envelope calibration and processing tool. We present an overview of the tool and advantages of the method along with several calibration examples, all of which are freely available to the public via GitHub (https://github.com/LLNL/coda-calibration-tool). Once a region is calibrated, the tool can then be used in routine processing to obtain stable source spectra and associated source information (<em>e.g.</em>, <em>M</em><em><sub>w</sub></em>, radiated seismic energy, apparent stress, corner frequency, source discrimination on event type and/or depth). As more events are recorded or new stations added, simple updates to the calibration can be performed. All calibration and measurement information (<em>e.g.,</em> site and path correction terms, raw & measured amplitudes, errors, etc.) is stored within an internal database that can be queried for future use. We welcome future collaboration, testing and suggestions by the geophysical community.  </p>

Radiated seismic energy of earthquakes in the south–central region of the Gulf of California, Mexico

2018 ◽  
Vol 214 (2) ◽  
pp. 990-1003
Author(s):  
Raúl R Castro ◽  
Antonio Mendoza-Camberos ◽  
Arturo Pérez-Vertti
Keyword(s):  
Central Region ◽  
Gulf Of California ◽  
Seismic Energy ◽  
The South ◽  
Radiated Seismic Energy ◽  
South Central

Synthetic analysis of seismic back-projection using 3D dynamic rupture simulations of the 2018, Palu Sulawesi earthquake

2020 ◽  
Author(s):  
Thomas Ulrich ◽  
Bo Li ◽  
Alice-Agnes Gabriel
Keyword(s):  
Seismic Energy ◽  
Fault System ◽  
Beam Power ◽  
Small Scale ◽  
Back Projection ◽  
Secondary Phases ◽  
Dynamic Rupture ◽  
Rupture Dynamics ◽  
Projection Techniques ◽  
Spatio Temporal

<p>Back-projection uses the time-reversal property of the seismic wavefield recorded at large aperture dense seismic arrays. Seismic energy radiation is imaged by applying array beam-forming techniques. The spatio-temporal rupture complexity of large earthquakes can be imaged simply and rapidly with a limited number of assumptions, which makes back-projection techniques an important tool of modern seismology. However, back-projection analyses exhibit frequency and array dependency (e.g. Wu et al., AGU19). In addition, the method relies on station network geometry and data quality and can suffer from imaging artifacts (e.g., Fan and Shearer, 2017) and back-projection results may not be consistently interpreted.</p><p>The Mw7.5 Palu, Sulawesi earthquake that occurred on September 28, 2018, ruptured a 180 km long section of the Palu-Koro fault. The earthquake triggered a localized but powerful tsunami within Palu Bay, which swept away houses and buildings. The supershear earthquake and unexpected tsunami led to more than 4000 fatalities. Ulrich et al. (2019) propose a physics-based, coupled earthquake-tsunami scenario of the event, tightly constrained by observations. The model matches key observed earthquake characteristics, including moment magnitude, rupture duration, fault plane solution, teleseismic waveforms, and inferred horizontal ground displacements. It suggests that time-dependent earthquake-induced uplift and subsidence could have sourced the observed tsunami within Palu Bay.</p><p>Back-projection has been used to track the rupture propagation of the Palu earthquake. Bao et al. (2019) image unilateral rupture traveling at a supershear rupture speed. Their results show array dependent ruptures, from a rather relatively linear rupture using the Australian array, to a spatio-temporally more scattered image using the seismic array in Turkey. In addition, they do not resolve any portion of the rupture as traveling at sub-Rayleigh speeds, while Wei et al. (AGU19) suggest a gradually accelerating rupture.</p><p>In this study, we build upon the dynamic rupture model of Ulrich et al. (2019) to investigate the reliability of standard back-projection techniques using a realistic and perfectly known earthquake model. In particular, we investigate whether or not rupture transfers across the segmented fault system, and the effect of specific geometric features of the fault system, such as fault bends, on rupture dynamics, leave a clear signal on the inferred beam power. Also, we investigate the effect of secondary phases, such as reflections from the free-surface or from fault segment boundaries, naturally captured by dynamic rupture modeling. In addition, we study the effect of small-scale source heterogeneities on the back-projection results by including different levels of fault roughness in the dynamic rupture simulations. Finally, we investigate the array dependence of back-projection results.</p><p>Overall, this study should help to better understand which features of rupture dynamics back-projection can capture. Our results are a first step towards fundamental analysis to better understand which features can be captured by back-projection and to provide guidelines for back-projection interpretation.</p>

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