A framework for fast probabilistic centroid-moment-tensor determination—inversion of regional static displacement measurements

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 >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.

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New Updated Classification of Shallow Earthquakes Based on Faulting Style

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.1.11 ◽

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.

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Centroid-moment tensor solutions for October–December 1994

Physics of The Earth and Planetary Interiors ◽

10.1016/0031-9201(95)03081-7 ◽

1995 ◽

Vol 91 (4) ◽

pp. 187-201 ◽

Cited By ~ 32

Author(s):

A.M. Dziewonski ◽

G. Ekström ◽

M.P. Salganik

Keyword(s):

Moment Tensor ◽

Centroid Moment Tensor ◽

Centroid Moment Tensor Solutions

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

Physics of The Earth and Planetary Interiors ◽

10.1016/0031-9201(87)90194-4 ◽

1987 ◽

Vol 45 (1) ◽

pp. 11-36 ◽

Cited By ~ 114

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

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The Global Centroid Moment Tensor Catalog: Heterogeneities and improvements

10.5194/egusphere-egu21-3038 ◽

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

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.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.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.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.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&#176; (normal), 0&#176; or &#177;180&#176; (strike-slip) or +90&#176; (thrust). Even disregarding such events, rake histograms until 2003 and since 2004 are not equivalent to each other.The magnitude of completeness (Mc) of the catalog is analyzed here separately for each era. It clearly improved along time (average Mc values being ~6.4 in 1976, ~5.7 in 1977-1985, ~5.4 in 1986-2003, and ~5.0 since 2004). Maps of Mc for different eras show significant spatial variations.

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Source Parameter Sensitivity of Earthquake Simulations assisted by Machine Learning

10.5194/egusphere-egu21-5995 ◽

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

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.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&#186;N-20.6&#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.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.

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Comparison of local kinematic rupture joint inversion approaches for tsunami early warning: Examples of the 2017-2020 Mw > 6.3 East Aegean earthquakes

10.5194/egusphere-egu21-5221 ◽

2021 ◽

Author(s):

Malte Metz ◽

Marius Isken ◽

Rongjiang Wang ◽

Torsten Dahm ◽

Haluk Özener ◽

...

Keyword(s):

Early Warning ◽

Joint Inversion ◽

Moment Tensor ◽

Strong Motion ◽

Aegean Sea ◽

High Rate ◽

Source Inversion ◽

Dynamic Rupture ◽

Centroid Moment Tensor ◽

Tsunami Early Warning

The fast inversion of reliable centroid moment tensor and kinematic rupture parameters of earthquakes occurring near coastal margins is a key for the assessment of the tsunamigenic potential and early tsunami warning (TEW). In recent years, more and more multi-channel seismic and geodetic online station networks have been built-up to improve the TEW, for instance the GNSS and strong motion networks in Italy, Greece, and Turkey, additionally to the broadband seismological monitoring. Inclusion of such data for the fast kinematic source inversion can improve the resolution and robustness of its&#8217; solutions. However, methods have to be further developed and tested to fully exploit the potential of such rich joint dataset.In this frame, we compare and test two in-house developed, kinematic / dynamic rupture inversion methods which are based on completely different approaches. The IDS (Iterative Deconvolution and Stacking, Zhang et al., 2014) combines an iterative seismic network inversion with back projection techniques to retrieve subfault source time functions. The pseudo dynamic rupture model (Dahm et al., in review) links the rupture front propagation estimate based on the Eikonal equation with the dislocation derived from a boundary element method to model dislocation snapshots. We used the latter in both a fast rupture estimate and a fully probabilistic source inversion.We use some Mw > 6.3 earthquakes that occurred in the coastal range of the Aegean Sea as an example for comparison: the Mw 6.3 Lesbos earthquake (12 June 2017), the Mw 6.6 Bodrum earthquake (20 July 2017), and the recent Mw 7.0 earthquake which occurred at Samos on 30 October 2020. The latter earthquake and the resulting tsunami caused fatalities and severe damage at the shorelines of Samos and around the city of Izmir, Turkey. The fast estimates are based on only little data and/or prior information obtained from the regional seismicity catalogue and available active fault information. The large number of seismic (broadband, strong motion) and geodetic (high-rate GNSS) stations in local and regional distance from the earthquake with good azimuthal coverage jointly inverted with InSAR data allows for robust inversion results. These, and other solutions, are used as a reference for the comparison of our fast source estimates. Preliminary results of the slip distribution and the source time function are in good agreement with modelling results from other authors.We present our insights into the kinematics of the chosen earthquakes investigated by means of joint inversions. Finally, the accuracy of our first fast source estimates, which could be of potential use in tsunami early warning, will be discussed.

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