The Ahar‐Varzeghan Earthquake Doublet (Mw 6.4 and 6.2) of 11 August 2012: Regional Seismic Moment Tensors and a Seismotectonic Interpretation

2015 ◽  
Vol 105 (2A) ◽  
pp. 791-807 ◽  
Author(s):  
Stefanie Donner ◽  
Abdolreza Ghods ◽  
Frank Krüger ◽  
Dirk Rößler ◽  
Angela Landgraf ◽  
...  
Keyword(s):  
Seismic Moment ◽  
Moment Tensors ◽  
Seismotectonic Interpretation ◽  
Earthquake Doublet

The 2014–2015 complex collapse of the Bárðarbunga caldera, Iceland, revealed by seismic moment tensors

2021 ◽  
pp. 107275
Author(s):  
Félix Rodríguez-Cardozo ◽  
Vala Hjörleifsdóttir ◽  
Kristín Jónsdóttir ◽  
Arturo Iglesias ◽  
Sara Ivonne Franco ◽  
...  
Keyword(s):  
Seismic Moment ◽  
Moment Tensors

scholarly journals Seismic moment tensors from synthetic rotational and translational ground motion: Green’s functions in 1-D versus 3-D

2020 ◽  
Vol 223 (1) ◽  
pp. 161-179
Author(s):  
S Donner ◽  
M Mustać ◽  
B Hejrani ◽  
H Tkalčić ◽  
H Igel
Keyword(s):  
Ground Motion ◽  
Seismic Moment ◽  
Structural Model ◽  
Moment Tensor ◽  
Waveform Inversion ◽  
Seismic Moment Tensor ◽  
Tectonic Event ◽  
Moment Tensors ◽  
Rotational Ground Motion ◽  
D Model

SUMMARY Seismic moment tensors are an important tool and input variable for many studies in the geosciences. The theory behind the determination of moment tensors is well established. They are routinely and (semi-) automatically calculated on a global scale. However, on regional and local scales, there are still several difficulties hampering the reliable retrieval of the full seismic moment tensor. In an earlier study, we showed that the waveform inversion for seismic moment tensors can benefit significantly when incorporating rotational ground motion in addition to the commonly used translational ground motion. In this study, we test, what is the best processing strategy with respect to the resolvability of the seismic moment tensor components: inverting three-component data with Green’s functions (GFs) based on a 3-D structural model, six-component data with GFs based on a 1-D model, or unleashing the full force of six-component data and GFs based on a 3-D model? As a reference case, we use the inversion based on three-component data and 1-D structure, which has been the most common practice in waveform inversion for moment tensors so far. Building on the same Bayesian approach as in our previous study, we invert synthetic waveforms for two test cases from the Korean Peninsula: one is the 2013 nuclear test of the Democratic People’s Republic of Korea and the other is an Mw  5.4 tectonic event of 2016 in the Republic of Korea using waveform data recorded on stations in Korea, China and Japan. For the Korean Peninsula, a very detailed 3-D velocity model is available. We show that for the tectonic event both, the 3-D structural model and the rotational ground motion, contribute strongly to the improved resolution of the seismic moment tensor. The higher the frequencies used for inversion, the higher is the influence of rotational ground motions. This is an important effect to consider when inverting waveforms from smaller magnitude events. The explosive source benefits more from the 3-D structural model than from the rotational ground motion. Nevertheless, the rotational ground motion can help to better constraint the isotropic part of the source in the higher frequency range.

Linear inversion of seismic moment tensors of general point source by using generalized ray theory

1997 ◽  
Vol 10 (2) ◽  
pp. 253-259 ◽  
Author(s):  
Rui-Feng Liu ◽  
Jing-Ping Dang ◽  
Shao-Dong Fang ◽  
Pei-Shan Chen
Keyword(s):  
Point Source ◽  
Seismic Moment ◽  
General Point ◽  
Ray Theory ◽  
Linear Inversion ◽  
Moment Tensors

scholarly journals Seismic Moment Tensor Solutions from GeoNet Data to Provide a Moment Magnitude Scale for New Zealand

2021 ◽  
Author(s):  
◽  
Elizabeth de Joux Robertson
Keyword(s):  
New Zealand ◽  
Seismic Moment ◽  
Moment Tensor ◽  
Velocity Model ◽  
Moment Tensor Inversion ◽  
Moment Magnitude ◽  
Seismic Moment Tensor ◽  
Waveform Data ◽  
Moment Tensors ◽  
The Moment

<p>The aim of this project is to enable accurate earthquake magnitudes (moment magnitude, MW) to be calculated routinely and in near real-time for New Zealand earthquakes. This would be done by inversion of waveform data to obtain seismic moment tensors. Seismic moment tensors also provide information on fault-type. I use a well-established seismic moment tensor inversion method, the Time-Domain [seismic] Moment Tensor Inversion algorithm (TDMT_INVC) and apply it to GeoNet broadband waveform data to generate moment tensor solutions for New Zealand earthquakes. Some modifications to this software were made. A velocity model can now be automatically used to calculate Green's functions without having a pseudolayer boundary at the source depth. Green's functions can be calculated for multiple depths in a single step, and data are detrended and a suitable data window is selected. The seismic moment tensor solution that has either the maximum variance reduction or the maximum double-couple component is automatically selected for each depth. Seismic moment tensors were calculated for 24 New Zealand earthquakes from 2000 to 2005. The Global CMT project has calculated CMT solutions for 22 of these, and the Global CMT project solutions are compared to the solutions obtained in this project to test the accuracy of the solutions obtained using the TDMT_INVC code. The moment magnitude values are close to the Global CMT values for all earthquakes. The focal mechanisms could only be determined for a few of the earthquakes studied. The value of the moment magnitude appears to be less sensitive to the velocity model and earthquake location (epicentre and depth) than the focal mechanism. Distinguishing legitimate seismic signal from background seismic noise is likely to be the biggest problem in routine inversions.</p>

scholarly journals Estimation of seismic moment tensors using variational inference machine learning

2021 ◽  
Author(s):  
Andreas Steinberg ◽  
Hannes Vasyura‐Bathke ◽  
Peter Gaebler ◽  
Matthias Ohrnberger ◽  
Lars Ceranna
Keyword(s):  
Machine Learning ◽  
Seismic Moment ◽  
Variational Inference ◽  
Moment Tensors

Focal mechanisms for small to intermediate earthquakes in the northern part of the Alps and their seismotectonic interpretation

2020 ◽  
Author(s):  
Thomas Plenefisch ◽  
Laura Barth ◽  
Keyword(s):  
Stress Field ◽  
Focal Mechanisms ◽  
Small Magnitude ◽  
Moment Tensors ◽  
Time Period ◽  
Double Couple ◽  
Geodynamic Processes ◽  
The Alps ◽  
Seismic Networks ◽  
Seismotectonic Interpretation

&lt;p&gt;In the framework of the AlpArray project more than 600 broadband stations have been installed and operated in the Alps and the surroundings. Together with the permanent stations in the area it is one of the most densely spaced seismic networks worldwide. Thereby, it offers an excellent opportunity to investigate the seismicity and seismotectonics of the Alpine chain. Due to the huge number of stations focal mechanisms can be calculated even for small magnitude earthquakes with high accuracy. The focal mechanisms are one important key to reveal the contemporary stress field and thus contribute to a better understanding of the geodynamic processes of the Alps.&lt;/p&gt;&lt;p&gt;In our study we focus on small to intermediate earthquakes in the Northern Alps, namely on four distinct sub-regions. These are from West to East the Lake Constance, the Arlberg region, the area of Garmisch-Partenkirchen and the broader region of Innsbruck. In order to calculate the focal mechanisms, we apply the FOCMEC program (Snoke, 2003), which inverts for a pure double-couple source. P-polarities as well as amplitude ratios of SH to P are used as input parameters for the inversion. Thanks to the dense network a good coverage of the focal sphere is achieved in most cases.&lt;/p&gt;&lt;p&gt;Altogether, we calculated focal mechanisms for 25 earthquakes in the magnitude range between 2.5 and 3.5 from the time period 2016 to 2019. Most of the focal mechanisms represent reverse or strike-slip faulting, normal faulting events are rather rare. The mechanisms are analysed with respect to lateral changes along the Northern Alpine. On one hand we compare the mechanisms with mechanisms of older studies as well as with moment tensors of events of slightly larger magnitudes. Those events are the scope of another subproject in the framework of the AlpArray (Petersen et al., 2019). On the other hand, we compare our mechanisms with geological indicators, namely orientation of faults. Finally, the focal mechanisms are used as input to invert for the stress field.&lt;/p&gt;

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