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

<p>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).</p><p> </p><p>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.</p><p> </p><p>The study was supported by Russian Foundation for Basic Research (projects 20-35-70038, 19-05-00351, 20-07-01098).</p><p> </p>

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 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.

Slip-deficit estimation with a 3D fault model of the North Anatolian Fault by using InSAR time series

2021 ◽  
Author(s):  
Alison Seidel ◽  
Henriette Sudhaus
Keyword(s):  
Time Series ◽  
Crustal Deformation ◽  
Time Series Data ◽  
Fault Model ◽  
North Anatolian Fault ◽  
Series Data ◽  
The North ◽  
Slip Deficit ◽  
Finite Fault ◽  
Finite Fault Model

&lt;p&gt;Crustal earthquakes are events of sudden stress release throug&amp;#173;h rock failure, for example as a consequence of continuous and long-term stress buildup at tectonic faults that eventually exceeds the strength of rock. Before failure, under increasing stress at a fault, the surrounding crust is slowly deforming. The amount and pattern of crustal deformation carries information about location and potential magnitude of future earthquakes.&lt;/p&gt;&lt;p&gt;Time series of space-borne interferometric Synthetic Aperture Radar (InSAR) data can be used to precisely measure the surface motion, which corresponds to the crustal deformation, in the radar line-of-sight and across large areas. These observations open the opportunity to study fault loading in terms of location, size of locked parts at faults and their slip deficit. Here we study the North Anatolian Fault (NAF), a major right-lateral strike-slip fault zone of about 1500 km length in the north of Turkey and we create its first large-scale 3D finite-fault model based on InSAR data.&lt;/p&gt;&lt;p&gt;We use the InSAR time series of data recorded by ESA&amp;#8217;s Envisat SAR satellite between 2002 and 2010 (Hussain et al., 2018 and Walters et al., 2014).&lt;!-- Das ist nicht ganz eindeutig formuliert. rigid motion darf nicht auf die InSAR Daten bezogen werden. --&gt; We represent the fault with several vertical, planar fault segments that trace the NAF with reasonable resolution. The medium model is a layered half space with a viscoelastic lower crust and mantle. Several GNSS velocity measurements are used to apply a trend correction and calibrate the InSAR time series data to an Eurasia-fixed-reference frame. We use the plate motion difference of the Anatolian and the Eurasian plates calculated through an Euler pole to set up a back-slip finite-fault model. We then optimize the back-slip as the slip deficit, the width and the depth of the locked fault zone at each segment to achieve a good fit to the measured surface motion.&lt;/p&gt;&lt;p&gt;We find shallow locking depths and small slip deficits in the eastern and westernmost regions of the NAF, while the central part shows both deeper locking depths and larger slip deficits for the observation period. &lt;!-- So wie es jetzt ist sind es zu viele W&amp;#246;rter, wenn man diesen erkl&amp;#228;r-Satz rausnehmen w&amp;#252;rde, w&amp;#252;rde es gerade so passen. F&amp;#252;r die Erdbebenaktivit&amp;#228;t im Osten hab ich bis jetzt f&amp;#252;r den Zeitraum auch noch kein entsprechendes Paper gefunden, da suche ich aber noch. --&gt;For both parameters the trends are in an overall agreement to earlier studies. There, InSAR-time series data have been used to calculate slip deficits at the North Anatolian fault with 2D models and/or assuming a homogeneous and purely elastic medium.&lt;!-- Passt vom flow jetzt besser hier hin, denke ich. --&gt; Local modeled differences therefore might be connected to differences in the modeling approaches, but also remain subject to further investigations and discussions.&lt;/p&gt;&lt;p&gt;Our model provides a very suitable basis for future time-dependent modeling of the slip deficit at the NAF that includes also more recent InSAR time series based on data from the Sentinel-1 radar satellite mission of ESA.&lt;/p&gt;

Rupture process of the 7 May 2020 Mw 5.0 Tehran earthquake and its relation with the Damavand stratovolcano, and Mosha Fault

2021 ◽  
Author(s):  
Pınar Büyükakpınar ◽  
Mohammadreza Jamalreyhani ◽  
Mehdi Rezapour ◽  
Stefanie Donner ◽  
Nima Nooshiri ◽  
...  
Keyword(s):  
Moment Tensor ◽  
Source Parameters ◽  
Active Faults ◽  
Strong Motion ◽  
Fault Model ◽  
Focal Mechanism Solution ◽  
Strong Motion Records ◽  
Finite Fault ◽  
Finite Fault Model ◽  
Mosha Fault

&lt;p&gt;In May 2020 an earthquake with Mw 5.0 struck at ~40 km east of Tehran metropolis and ~15 km south of the Damavand stratovolcano. It was responsible for 2 casualties and 23 injured. The mainshock was preceded by a foreshock with Ml 2.9 and followed by a significant aftershock sequence, including ten events with Ml 3+. The occurrence of this event raised the question of its relation with volcanic activities and/or concern about the occurrence of larger future earthquakes in the capital of Iran. Tehran megacity is surrounded by several inner-city and adjacent active faults that correspond to high-risk seismic sources in the area. The Mosha fault with ~150 km long is one of the major active faults in central Alborz and east of Tehran. It has hosted several historical earthquakes (i.e. 1665 Mw 6.5 and 1830 Mw 7.1 earthquakes) in the vicinity of the 2020 Mw 5.0 Tehran earthquake&amp;#8217;s hypocenter. In this study, we evaluate the seismic sequence of the Tehran earthquake and obtain the full moment tensor inversion of this event and its larger aftershocks, which is a key tool to discriminate between tectonic and volcanic earthquakes. Furthermore, we obtain a robust characterization of the finite fault model of this event applying probabilistic earthquake source inversion framework using near-field strong-motion records and broadband seismograms, with an estimation of the uncertainties of source parameters. Due to the relatively weak magnitude and deeper centroid depth (~12 km), no static surface displacement was observed in the coseismic interferograms, and modeling performed by seismic records. Focal mechanism solution from waveform inversion, with a significant double-couple component, is compatible with the orientation of the sinistral north-dipping Mosha fault at the centroid location. The finite fault model suggests that the mainshock rupture propagated towards the northwest. This directivity enhanced the peak acceleration in the direction of rupture propagation, observed in strong-motion records. The 2020 moderate magnitude earthquake with 2 casualties, highlights the necessity of high-resolution seismic monitoring in the capital of Iran, which is exposed to a risk of destructive earthquakes with more than 10 million population. Our results are important for the hazard and risk assessment, and the forthcoming earthquake early warning system development in Tehran metropolis.&lt;/p&gt;

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