Ground-Motion Attenuation Relationships for Cascadia Subduction Zone Megathrust Earthquakes Based on a Stochastic Finite-Fault Model

2002 ◽  
Vol 92 (5) ◽  
pp. 1923-1932 ◽  
Author(s):  
N. J. Gregor
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Fault Model ◽  
Cascadia Subduction Zone ◽  
Megathrust Earthquakes ◽  
Attenuation Relationships ◽  
Ground Motion Attenuation ◽  
Finite Fault ◽  
Finite Fault Model

scholarly journals A maximum rupture model for the central and southern Cascadia subduction zone—reassessing ages for coastal evidence of megathrust earthquakes and tsunamis

2021 ◽  
Vol 261 ◽  
pp. 106922
Author(s):  
Alan R. Nelson ◽  
Christopher B. DuRoss ◽  
Robert C. Witter ◽  
Harvey M. Kelsey ◽  
Simon E. Engelhart ◽  
...  
Keyword(s):  
Subduction Zone ◽  
Cascadia Subduction Zone ◽  
Megathrust Earthquakes ◽  
Rupture Model

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>

Subduction zone heterogeneity observed across the magnitude spectrum for megathrust earthquakes

2021 ◽  
Author(s):  
Susan Bilek ◽  
Emily Morton
Keyword(s):  
Spatial Heterogeneity ◽  
Subduction Zone ◽  
Stress Drop ◽  
Subduction Zones ◽  
Cascadia Subduction Zone ◽  
Ocean Bottom ◽  
Small Earthquake ◽  
Megathrust Earthquakes ◽  
Seismic Slip ◽  
Subduction Zone Earthquakes

&lt;p&gt;Observations from recent great subduction zone earthquakes highlight the influence of spatial geologic heterogeneity on overall rupture characteristics, such as areas of high co-seismic slip, and resulting tsunami generation.&amp;#160; Defining the relevant spatial heterogeneity is thus important to understanding potential hazards associated with the megathrust. The more frequent, smaller magnitude earthquakes that commonly occur in subduction zones are often used to help delineate the spatial heterogeneity.&amp;#160; Here we provide an overview of several subduction zones, including Costa Rica, Mexico, and Cascadia, highlighting connections between the small earthquake source characteristics and rupture behavior of larger earthquakes.&amp;#160; Estimates of small earthquake locations and stress drop are presented in each location, utilizing data from coastal and/or ocean bottom seismic stations.&amp;#160; These seismicity characteristics are then compared with other geologic and geophysical parameters, such as upper and lower plate characteristics, geodetic locking, and asperity locations from past large earthquakes. &amp;#160;For example, in the Cascadia subduction zone, we find clusters of small earthquakes located in regions of previous seamount subduction, with variations in earthquake stress drop reflecting potentially disrupted upper plate material deformed as a seamount passed.&amp;#160; Other variations in earthquake location and stress drop can be correlated with observed geodetic locking variations.&amp;#160;&lt;/p&gt;

Comparative study of the 3D tsunami simulations performed with the use of different approaches to the reconstruction of the bottom movement 

2021 ◽  
Author(s):  
Kirill A. Sementsov ◽  
Sergey V. Kolesov ◽  
Anna V. Bolshakova ◽  
Mikhail A. Nosov
Keyword(s):  
Input Data ◽  
Fault Model ◽  
Earthquake Source ◽  
Data Type ◽  
Ocean Bottom ◽  
Source Type ◽  
Finite Fault ◽  
Finite Fault Model ◽  
Type 3

&lt;p&gt;Information on the earthquake source mechanism (Centroid Moment Tensor) becomes publicly available in a few minutes after the earthquake (for example, https://earthquake.usgs.gov/earthquakes or http://geofon.gfz-potsdam.de/eqinfo). Using this information, we can calculate the ocean bottom displacement in the earthquake area [Leonard, 2010; Okada, 1985] and then use this displacement as an input data for hydrodynamic simulation of the tsunami waves. Let us call this type of input data - Type 1. Somewhat later (and sometimes much later), than CMT, more detailed information on the rupture fault structure (Finite Fault Model) becomes available. According to Finite Fault Model, the rupture fault in the earthquake source consists of a certain number of segments characterized by their dip and strike angles. Each segment consists of a finite number of rectangular subfaults, for each of which a displacement vector, an activation time and a rise time are specified. By applying Okada's formulas to each subfault and using the principle of superposition, we can calculate the ocean bottom displacement in the earthquake area and also use it as an input data for tsunami simulations. Let us call this type of input data - Type 2. However, based on the Finite Fault Model, we are able to create a third type of input data (Type 3). To do this, it is necessary to take into account the displacement start time (subfault activation time) and the displacement duration (subfault rise time) of each subfault and consider the dynamics of the rupture process. In this case, we will be able to reconstruct not only the coseismic bottom displacement in the earthquake source (Type 2), but also describe the dynamics of the coseismic bottom displacement formation in the tsunami source (Type 3).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;This paper compares the tsunami simulation results performed with the of different types of input data (Type 1, Type 2 and Type 3). We performed calculations for a number of large earthquakes at the beginning of the 21st century. We took all the earthquake source information from the USGS catalog (https://earthquake.usgs.gov/earthquakes). The bottom deformations of all three types were calculated using the ffaultdisp code (http://ocean.phys.msu.ru/projects/ffaultdisp/). Tsunami modeling was carried out using a combined 2D / 3D CPTM model [Nosov, Kolesov, 2019; Sementsov et al., 2019]. The simulation results are compared with each other as well as with the DART ocean bottom observatories records.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The study was supported by Russian Foundation for Basic Research (projects 20-35-70038, 19-05-00351, 20-07-01098).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Speeding up and boosting tsunami warning in Chile

2019 ◽  
Author(s):  
Mauricio Fuentes ◽  
Sebastian Arriola ◽  
Sebastian Riquelme ◽  
Bertrand Delouis
Keyword(s):  
Real Time ◽  
Near Field ◽  
Linear Method ◽  
Seismic Energy ◽  
Fault Model ◽  
Tsunami Forecast ◽  
Finite Fault ◽  
Run Up ◽  
Finite Fault Model ◽  
Chile Tsunami

Abstract. Chile host a great tsunamigenic potential along its coast, even with the large earthquakes occurred during the last decade, there is still a large amount of seismic energy to release. This permanent feature and the fact that the distance between the trench and the coast is just 100 km creates a difficult environment to do real time tsunami forecast. In Chile tsunami warnings are based on reports of the seismic events (hypocenter and magnitude) and a database of precomputed tsunami scenarios. However, because yet there is no answer to image the finite fault model within first minutes (before the first tsunami wave arrival), the precomputed scenarios consider uniform slip distributions. Here, we propose a scheme of processes to fill the gaps in-between blind zones due to waiting of demanding computational stages. The linear shallow water equations are solved to obtain a rapid estimation of the run-up distribution in the near field. Our results show that this linear method captures most of the complexity of the run-up heights in terms of shape and amplitude when compared with a fully non-linear tsunami code. Also, the run-up distribution is obtained in quasi real-time as soon as the seismic finite fault model is produced.

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