The Mw 7.6 Dusky Sound earthquake of 2009

2010 ◽  
Vol 43 (1) ◽  
pp. 24-40 ◽  
Author(s):  
Bill Fry ◽  
Stephen Bannister ◽  
John Beavan ◽  
Lara Bland ◽  
Brendon Bradley ◽  
...  
Keyword(s):  
Moment Tensor ◽  
Seismic Energy ◽  
Rupture Directivity ◽  
Centroid Moment Tensor ◽  
M Wave ◽  
Radiated Seismic Energy ◽  
Australian Plate ◽  
Finite Fault ◽  
Minimal Damage ◽  
Sparse Population

The Mw 7.6 Dusky Sound earthquake of July 15th, 2009, was the largest magnitude earthquake in New Zealand since the devastating 1931 Hawke’s Bay event (Ms 7.8). The earthquake was sufficiently large to generate at least a 2.3 m wave at Passage Point. Despite its large magnitude, this event resulted in relatively minimal damage when compared to worldwide events of a similar size. This can be explained as a fortunate combination of the sparse population of the area and the specific physical characteristics of the earthquake. Centroid Moment Tensor (CMT) solutions define the rupture surface as a low-angle plane and finite fault inversions confirm the slip occurred on the interface between the eastward-subducting Australian plate and overriding Pacific plate, initiating at about 30 km depth and rupturing upward and southwestward to about 15 km depth. The oceanward rupture directivity likely contributed to the lower intensity of measured ground motion than might be expected for such a large, shallow event. The amount of radiated seismic energy from the earthquake was relatively small, and far fewer landslides were triggered from this event than from the 2003 Mw 7.2 Fiordland event.

Assessing the role of selected constraints in Bayesian dynamic source inversion: Application to the 2017 Mw 6.3 Lesvos earthquake

2021 ◽  
Author(s):  
Filip Kostka ◽  
Jiří Zahradník ◽  
Efthimios Sokos ◽  
František Gallovič
Keyword(s):  
Posterior Distribution ◽  
Moment Tensor ◽  
Strong Motion ◽  
Monte Carlo Algorithm ◽  
Source Inversion ◽  
Dynamic Rupture ◽  
Centroid Moment Tensor ◽  
Trade Offs ◽  
Finite Fault ◽  
The Mean

Summary A dynamic finite-fault source inversion for stress and frictional parameters of the Mw 6.3 2017 Lesvos earthquake is carried out. The mainshock occurred on June 12, offshore the southeastern coast of the Greek island of Lesvos in the north Aegean Sea. It caused 1 fatality, 15 injuries, and extensive damage to the southern part of the island. Dynamic rupture evolution is modeled on an elliptic patch, using the linear slip-weakening friction law. The inversion is posed as a Bayesian problem and the Parallel Tempering Markov Chain Monte Carlo algorithm is used to obtain posterior probability distributions by updating the prior distribution with progressively more constraints. To calculate the first posterior distribution, only the constraint that the model should expand beyond the nucleation patch is used. Then, we add the constraint that the model should reach a moment magnitude similar to that obtained from our centroid moment tensor inversion. For the final posterior distribution, 15 acceleration records from Greek and Turkish strong motion networks at near regional distances ($\approx 30 - 150$ km) in the frequency range of 0.05–0.15 Hz are used. The three posterior distributions are compared to understand how much each constraint contributes to resolving different quantities. The most probable values and uncertainties of individual parameters are also calculated, along with their mutual trade-offs. The features best determined by seismograms in the final posterior distribution include the position of the nucleation region, the mean direction of rupture (towards WNW), the mean rupture speed (with 68 per cent of the distribution lying between 1.4–2.6 km/s), radiated energy (12–65 TJ), radiation efficiency (0.09–0.38), and the mean stress drop (2.2–6.5 MPa).

scholarly journals The first month of the 2016 central Italy seismic sequence: fast determination of time domain moment tensors and finite fault model analysis of the ML 5.4 aftershock

2016 ◽  
Vol 59 ◽  
Author(s):  
Laura Scognamiglio ◽  
Elisa Tinti ◽  
Matteo Quintiliani
Keyword(s):  
Time Domain ◽  
Moment Tensor ◽  
Central Italy ◽  
Model Analysis ◽  
Fault Model ◽  
Normal Faults ◽  
Seismic Sequence ◽  
Moment Tensors ◽  
Finite Fault ◽  
Finite Fault Model

<p>We present the revised Time Domain Moment Tensor (TDMT) catalogue for earthquakes with M_L larger than 3.6 of the first month of the ongoing Amatrice seismic sequence (August 24th - September 25th). Most of the retrieved focal mechanisms show NNW–SSE striking normal faults in agreement with the main NE-SW extensional deformation of Central Apennines. We also report a preliminary finite fault model analysis performed on the larger aftershock of this period of the sequence (M_w 5.4) and discuss the obtained results in the framework of aftershocks distribution.</p>

scholarly journals Quick regional centroid moment tensor solutions for the Emilia 2012 (northern Italy) seismic sequence

2012 ◽  
Vol 55 (4) ◽  
Author(s):  
Silvia Pondrelli ◽  
Simone Salimbeni ◽  
Paolo Perfetti ◽  
Peter Danecek
Keyword(s):  
Moment Tensor ◽  
Mediterranean Area ◽  
Northern Italy ◽  
Seismic Sequence ◽  
Centroid Moment Tensor ◽  
Strong Earthquakes ◽  
Magnitude Range ◽  
The Mediterranean ◽  
Centroid Moment Tensor Solutions

<p>In May 2012, a seismic sequence struck the Emilia region (northern Italy). The mainshock, of Ml 5.9, occurred on May 20, 2012, at 02:03 UTC. This was preceded by a smaller Ml 4.1 foreshock some hours before (23:13 UTC on May 19, 2012) and followed by more than 2,500 earthquakes in the magnitude range from Ml 0.7 to 5.2. In addition, on May 29, 2012, three further strong earthquakes occurred, all with magnitude Ml ≥5.2: a Ml 5.8 earthquake in the morning (07:00 UTC), followed by two events within just 5 min of each other, one at 10:55 UTC (Ml 5.3) and the second at 11:00 UTC (Ml 5.2). For all of the Ml ≥4.0 earthquakes in Italy and for all of the Ml ≥4.5 in the Mediterranean area, an automatic procedure for the computation of a regional centroid moment tensor (RCMT) is triggered by an email alert. Within 1 h of the event, a manually revised quick RCMT (QRCMT) can be published on the website if the solution is considered stable. In particular, for the Emilia seismic sequence, 13 QRCMTs were determined and for three of them, those with M &gt;5.5, the automatically computed QRCMTs fitted the criteria for publication without manual revision. Using this seismic sequence as a test, we can then identify the magnitude threshold for automatic publication of our QRCMTs.</p>

scholarly journals New Updated Classification of Shallow Earthquakes Based on Faulting Style

2020 ◽  
pp. 103-111
Author(s):  
Emad Abulrahman Mohammed Salih Al-Heety
Keyword(s):  
Return Period ◽  
Moment Tensor ◽  
Focal Depth ◽  
Strike Slip ◽  
Shallow Depth ◽  
Main Trend ◽  
Maximum Magnitude ◽  
Centroid Moment Tensor ◽  
Significant Information

Earthquakes occur on faults and create new faults. They also occur on  normal, reverse and strike-slip faults. The aim of this work is to suggest a new unified classification of Shallow depth earthquakes based on the faulting styles, and to characterize each class. The characterization criteria include the maximum magnitude, focal depth, b-constant value, return period and relations between magnitude, focal depth and dip of fault plane. Global Centroid Moment Tensor (GCMT) catalog is the source of the used data. This catalog covers the period from Jan.1976 to Dec. 2017. We selected only the shallow (depth less than 70kms) pure, normal, strike-slip and reverse earthquakes (magnitude ≥ 5) and excluded the oblique earthquakes. The majority of normal and strike-slip earthquakes occurred in the upper crust, while the reverse earthquakes occurred throughout the thickness of the crust. The main trend for the derived b-values for the three classes was: b normal fault>bstrike-slip fault>breverse fault.  The mean return period for the normal earthquake was longer than that of the strike-slip earthquakes, while the reverse earthquakes had the shortest period. The obtained results report the relationship between the magnitude and focal depth of the normal earthquakes. A negative significant correlation between the magnitude and dip class for the normal and reverse earthquakes is reported. Negative and positive correlation relations between the focal depth and dip class were recorded for normal and reverse earthquakes, respectively. The suggested classification of earthquakes provides significant information to understand seismicity, seismtectonics, and seismic hazard analysis.

Centroid-moment tensor solutions for October–December 1994

1995 ◽  
Vol 91 (4) ◽  
pp. 187-201 ◽  
Author(s):  
A.M. Dziewonski ◽  
G. Ekström ◽  
M.P. Salganik
Keyword(s):  
Moment Tensor ◽  
Centroid Moment Tensor ◽  
Centroid Moment Tensor Solutions

Global seismicity of 1977: centroid-moment tensor solutions for 471 earthquakes

1987 ◽  
Vol 45 (1) ◽  
pp. 11-36 ◽  
Author(s):  
A.M. Dziewonski ◽  
G. Ekström ◽  
J.E. Franzen ◽  
J.H. Woodhouse
Keyword(s):  
Moment Tensor ◽  
Centroid Moment Tensor ◽  
Global Seismicity ◽  
Centroid Moment Tensor Solutions

The Global Centroid Moment Tensor Catalog: Heterogeneities and improvements

2021 ◽  
Author(s):  
Álvaro González
Keyword(s):  
Subduction Zones ◽  
Moment Tensor ◽  
Statistical Seismology ◽  
Centroid Moment Tensor ◽  
Moment Tensors ◽  
Global Database ◽  
Intermediate Period ◽  
Data Compilation ◽  
Forecast Models ◽  
The Moment

&lt;p&gt;Statistical seismology relies on earthquake catalogs as homogeneous and complete as possible. However, heterogeneities in earthquake data compilation and reporting are common and frequently are not adverted.&lt;/p&gt;&lt;p&gt;The Global Centroid Moment Tensor Catalog (www.globalcmt.org) is considered as the most homogeneous global database for large and moderate earthquakes occurred since 1976, and it has been used for developing and testing global and regional forecast models.&lt;/p&gt;&lt;p&gt;Changes in the method used for calculating the moment tensors (along with improvements in global seismological monitoring) define four eras in the catalog (1976, 1977-1985, 1986-2003 and 2004-present). Improvements are particularly stark since 2004, when intermediate-period surface waves started to be used for calculating the centroid solutions.&lt;/p&gt;&lt;p&gt;Fixed centroid depths, used when the solution for a free depth did not converge, have followed diverse criteria, depending on the era. Depth had to be fixed mainly for shallow earthquakes, so this issue is more common, e.g. in the shallow parts of subduction zones than in the deep ones. Until 2003, 53% of the centroids had depths calculated as a free parameter, compared to 78% since 2004.&lt;/p&gt;&lt;p&gt;Rake values have not been calculated homogenously either. Until 2003, the vertical-dip-slip components of the moment tensor were assumed as null when they could not be constrained by the inversion (for 3.3% of the earthquakes). This caused an excess of pure focal mechanisms: rakes of -90&amp;#176; (normal), 0&amp;#176; or &amp;#177;180&amp;#176; (strike-slip) or +90&amp;#176; (thrust). Even disregarding such events, rake histograms until 2003 and since 2004 are not equivalent to each other.&lt;/p&gt;&lt;p&gt;The magnitude of completeness (&lt;em&gt;M&lt;/em&gt;&lt;sub&gt;c&lt;/sub&gt;) of the catalog is analyzed here separately for each era. It clearly improved along time (average &lt;em&gt;M&lt;/em&gt;&lt;sub&gt;c&lt;/sub&gt; values being ~6.4 in 1976, ~5.7 in 1977-1985, ~5.4 in 1986-2003, and ~5.0 since 2004). Maps of &lt;em&gt;M&lt;/em&gt;&lt;sub&gt;c&lt;/sub&gt; for different eras show significant spatial variations.&lt;/p&gt;

Source Parameter Sensitivity of Earthquake Simulations assisted by Machine Learning 

2021 ◽  
Author(s):  
Marisol Monterrubio-Velasco ◽  
J. Carlos Carrasco-Jimenez ◽  
Otilio Rojas ◽  
Juan E. Rodriguez ◽  
David Modesto ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Moment Tensor ◽  
Source Parameters ◽  
Velocity Model ◽  
Source Parameter ◽  
Earthquake Simulation ◽  
Centroid Moment Tensor ◽  
Ground Shaking ◽  
Earthquake Simulations

&lt;p&gt;After large magnitude earthquakes have been recorded, a crucial task for hazard assessment is to quickly estimate Ground Shaking (GS) intensities at the affected region. Urgent physics-based earthquake simulations using High-Performance Computing (HPC) facilities may allow fast GS intensity analyses but are very sensitive to source parameter values. When using fast estimates of source parameters such as magnitude, location, fault dimensions, and/or Centroid Moment Tensor (CMT), simulations are prone to errors in their computed GS. Although the approaches to estimate earthquake location and magnitude are consolidated, depth location estimates are largely uncertain. Moreover, automatic CMT solutions are not always provided by seismological agencies, or such solutions are available at later times after waveform inversions allow the determination of moment tensor components. The uncertainty on these parameters, especially a few minutes after the earthquake has been registered, strongly affects GS maps resulting from simulations.&lt;/p&gt;&lt;p&gt;In this work, we present a workflow prototype to produce an uncertainty quantification method as a function of the source parameters. The core of this workflow is based on Machine Learning (ML) techniques. As a study case, we consider a domain of 110x80 km centered in 63.9&amp;#186;N-20.6&amp;#186;W in Southern Iceland, where the 17 best-mapped faults have hosted the historical events of the largest magnitude. We generate synthetic GS intensity maps using the AWP-ODC finite-difference code for earthquake simulation and a one-dimensional velocity model, with 40 recording surface stations. By varying a few source parameters (e.g. event magnitude, CMT, and hypocenter location), we finally model tens of thousands of hypothetical earthquakes. Our ML analog will then be able to relate GS intensity maps to source parameters, thus simplifying sensitivity studies.&lt;/p&gt;&lt;p&gt;Additionally, the results of this workflow prototype will allow us to obtain ML-based intensity maps a few seconds after an earthquake occurs exploiting the predictive power of ML techniques. We will evaluate the accuracy of these maps as standalone complements to GMPEs and simulations.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document