Inversion of waveform from the data of six moderate strong earthquakes in Chinese mainland to seismic moment tensors and source mechanism

1993 ◽  
Vol 6 (3) ◽  
pp. 583-592 ◽  
Author(s):  
Shao-Dong Fang ◽  
Pei-Shan Chen
Keyword(s):  
Seismic Moment ◽  
Chinese Mainland ◽  
Source Mechanism ◽  
Strong Earthquakes ◽  
Moment Tensors

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

scholarly journals Seismic moment tensor event screening

2020 ◽  
Vol 221 (1) ◽  
pp. 77-88
Author(s):  
Sean R Ford ◽  
Gordon D Kraft ◽  
Gene A Ichinose
Keyword(s):  
Seismic Moment ◽  
Moment Tensor ◽  
Test Site ◽  
Seismic Moment Tensor ◽  
Data Set ◽  
New Approach ◽  
Moment Tensors ◽  
Explosion Monitoring ◽  
Langevin Distribution ◽  
Monitoring Practice

SUMMARY Event screening is an explosion monitoring practice that aims to identify an event as an explosion (‘screened in’) or not (‘screened out’). Confidence in event screening can be increased if multiple independent approaches are used. We describe a new approach to event screening using the seismic moment tensor and its representation on the hypersphere, specifically the 5-sphere of 6-degree unit vectors representing the normalized symmetric moment tensor. The sample of moment tensors from an explosion data set is unimodal on the 5-sphere and can be parametrized by the Langevin distribution, which is sometimes referred to as the Normal distribution on the hypersphere. Screening is then accomplished by finding the angle from the explosion population mean to any newly measured moment tensor and testing if that angle is in the tail of the Langevin distribution (conservatively quantified as greater than 99.9 per cent of the cumulative density). We apply the screen to a sample of earthquakes from the Western USA and the September 2017 explosion and subsequent collapse at the Pungyye-Ri Test Site in North Korea. All the earthquakes and the collapse screen out, but the explosion does not.

scholarly journals The Mw = 5.6 Kanallaki Earthquake of 21 March 2020 in West Epirus, Greece: Reverse Fault Model from InSAR Data and Seismotectonic Implications for Apulia-Eurasia Collision

Geosciences ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 454
Author(s):  
Sotiris Valkaniotis ◽  
Pierre Briole ◽  
Athanassios Ganas ◽  
Panagiotis Elias ◽  
Vassilis Kapetanidis ◽  
...  
Keyword(s):  
Seismic Moment ◽  
Satellite System ◽  
Synthetic Aperture ◽  
Reverse Fault ◽  
Ionian Sea ◽  
Moment Tensors ◽  
Collision Zone ◽  
Lefkada Island ◽  
Global Navigation Satellite ◽  
Gnss Data

We identify the source of the Mw = 5.6 earthquake that hit west-central Epirus on 21 March 2020 00:49:52 UTC. We use Sentinel-1 synthetic aperture radar interferograms tied to one permanent Global Navigation Satellite System (GNSS) station (GARD). We model the source by inverting the INSAR displacement data. The inversion model suggests a shallow source on a low-angle fault (39°) dipping towards east with a centroid depth of 8.5 km. The seismic moment deduced from our model agrees with those of the published seismic moment tensors. This geometry is compatible with reverse-slip motion along the west-verging Margariti thrust fault that accommodates part of the convergence within the collision zone between Apulia and Eurasia. We also processed new GNSS data and estimate a total convergence rate between Apulia and Eurasia of 8.9 mm yr−1, of which the shortening of the crust between the Epirus coastal GNSS stations and station PAXO in the Ionian Sea (across the Ionian Thrust) is equivalent to ~50% of it or 4.6 mm yr−1. By back-slip modelling we found that a 60-km wide deformation zone takes up nearly most of the convergence between Apulia-Eurasia, trending N318°E. Its central axis runs along the southwest coast of Corfu, along the northeast coast of Paxoi, heading toward the northern extremity of the Lefkada island. The island of Paxoi appears kinematically as part of the Apulian plate.

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