Near‐Real‐Time Finite‐Fault Inversions for Large Earthquakes in Chile Using Strong‐Motion Data

2019 ◽  
Author(s):  
Pablo Koch ◽  
Francisco Bravo ◽  
Sebastian Riquelme ◽  
Jorge G. F. Crempien
Keyword(s):  
Real Time ◽  
Strong Motion ◽  
Integrated System ◽  
Origin Time ◽  
Strong Motion Data ◽  
Motion Data ◽  
Finite Fault ◽  
Set Up ◽  
Finite Fault Model ◽  
Large Earthquakes

ABSTRACT Recent efforts have been made to model the rupture process of large earthquakes in near‐real time (NRT) in Chile. In this study, we propose an automated procedure using strong‐motion data in an integrated system, which can characterize large earthquakes with a finite‐fault model (FFM) in NRT. We developed several heuristic rules using the preliminary W‐phase solutions to automatically set up the search ranges of the finite‐fault inversions. The results show using strong‐motion data and a W‐phase magnitude, it is possible to obtain a rapid kinematic FFM in just a few minutes after the earthquake origin time.

scholarly journals The INGV real time strong motion data sharing during the 2016 Amatrice (central Italy) seismic sequence

2016 ◽  
Vol 59 ◽  
Author(s):  
Marco Massa ◽  
Ezio D'Alema ◽  
Chiara Mascandola ◽  
Sara Lovati ◽  
Davide Scafidi ◽  
...  
Keyword(s):  
Real Time ◽  
Data Sharing ◽  
Strong Motion ◽  
Main Task ◽  
Seismic Sequence ◽  
Web Portal ◽  
Epicentral Area ◽  
Strong Motion Data ◽  
Motion Data ◽  
The Web

<p><em>ISMD is the real time INGV Strong Motion database. During the recent August-September 2016 Amatrice, Mw 6.0, seismic sequence, ISMD represented the main tool for the INGV real time strong motion data sharing.  Starting from August 24<sup>th</sup>,  the main task of the web portal was to archive, process and distribute the strong-motion waveforms recorded  by the permanent and temporary INGV accelerometric stations, in the case of earthquakes with magnitude </em><em>≥</em><em> 3.0, occurring  in the Amatrice area and surroundings.  At present (i.e. September 30<sup>th</sup>, 2016), ISMD provides more than 21.000 strong motion waveforms freely available to all users. In particular, about 2.200 strong motion waveforms were recorded by the temporary network installed for emergency in the epicentral area by SISMIKO and EMERSITO working groups. Moreover, for each permanent and temporary recording site, the web portal provide a complete description of the necessary information to properly use the strong motion data.</em></p>

ISMD, a Web Portal for Real-Time Processing and Dissemination of INGV Strong-Motion Data

2014 ◽  
Vol 85 (4) ◽  
pp. 863-877 ◽  
Author(s):  
M. Massa ◽  
S. Lovati ◽  
G. Franceschina ◽  
E. D'Alema ◽  
S. Marzorati ◽  
...  
Keyword(s):  
Real Time ◽  
Strong Motion ◽  
Web Portal ◽  
Strong Motion Data ◽  
Real Time Processing ◽  
Time Processing ◽  
Motion Data

Joint interpretation of macroseismic and strong motion data for recent large shallow mainshocks of the Aegean area using a Monte Carlo optimization of finite-fault stochastic simulations

2021 ◽  
Author(s):  
Michail Ravnalis ◽  
Charalampos Kkallas ◽  
Constantinos Papazachos ◽  
Basil Margaris ◽  
Christos Papaioannou
Keyword(s):  
Monte Carlo ◽  
Stochastic Simulation ◽  
Strong Motion ◽  
Macroseismic Data ◽  
Simulation Approach ◽  
Aegean Area ◽  
Strong Motion Data ◽  
Motion Data ◽  
Finite Fault ◽  
Joint Interpretation

&lt;p&gt;The study of strong historical and early instrumental earthquakes is based almost exclusively on the use of their macroseismic data, which usually constrain the area that has suffered the heaviest damage. In the recent decades, strong-motion data have been also employed for the same purpose. We present a stochastic simulation approach to jointly model macroseismic and strong motion data for selected shallow strong (&lt;strong&gt;M&lt;/strong&gt;&amp;#8805;6.0) earthquakes that occurred in the broader Aegean region between 1978 and 1995. For the simulations we employed the finite-fault stochastic simulation method, as realized by the EXSIM algorithm. We calibrated several parameters for the stochastic simulation modeling using a priori published information (e.g., moment magnitude, stress parameter). Other rupture zone information were collected from published works, such as fault plane solutions, relocated seismicity, etc. A Monte Carlo approach was adopted to perform a parametric search for the stress parameter and the modelling both independently and jointly the available macroseismic data and the strong motion instrumental recordings. The validity and the reliability of this semi-automated simulation approach was examined, to test if this method could be applied either in a fully automated manner, or for the study of the source properties of historical earthquakes. The results suggest that a joint-misfit minimization from the simultaneous simulation of macroseismic and strong motion data is a feasible target, that can be potentially employed for the simulation of older events, for which a limited number of instrumental data is often available. In general, a good agreement of the spatial distribution of the original and modeled macroseismic intensities is observed, showing that can reliably reconstruct the main features of the damage distribution for strong shallow mainshocks in the Aegean area using the proposed joint interpretation approach.&lt;/p&gt;

scholarly journals INGV strong-motion data web-portal: a focus on the Emilia seismic sequence of May-June 2012

2012 ◽  
Vol 55 (4) ◽  
Author(s):  
Marco Massa ◽  
Sara Lovati ◽  
Dario Sudati ◽  
Gianlorenzo Franceschina ◽  
Emiliano Russo ◽  
...  
Keyword(s):  
Real Time ◽  
Working Group ◽  
Strong Motion ◽  
Automatic Processing ◽  
Seismic Sequence ◽  
Web Portal ◽  
Strong Motion Data ◽  
Motion Data ◽  
Processing Procedure ◽  
Event Occurrence

<p>[…] Recently, an INGV working group developed the first version of a web portal with the aim of archiving, processing and distributing accelerometric data recorded by permanent and temporary INGV stations. This web portal (www.mi.ingv.it/ISMD/) is composed of two main modules: the former is known as the INGV Strong Motion Data (ISMD, www.mi.ingv.it/ISMD/ismd.html/) and has as its main scope the analyse and distribution in quasi-real time (a few hours after event occurrence) of the uncorrected accelerometric data, and the related metadata obtained after an automatic processing procedure. This latter, known as the Dynamic Archive (DYNA, http://dyna.mi.ingv.it/DYNA-archive/) is a dynamic database where manually post-processed accelerometric waveforms are provided, together with their metadata. Both of these archives are designed and structured in such a way that their compilations and updating will be almost completely automatic. At the end of May 2012, a first prototype of the ISMD module was published, providing the uncorrected strong-motion data recorded by the INGV stations for the main events of the Emilia seismic sequence. […]</p>

Strong-motion data recorded near Coalinga, California (May 2, 1983) and processed data from May 2 and May 9, 1983 (U.S. National Strong Motion Network)

1984 ◽  
Author(s):  
R. Maley ◽  
E.C. Etheredge ◽  
D. Johnson ◽  
J. Switzer ◽  
P. Mork ◽  
...  
Keyword(s):  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data ◽  
Strong Motion Network

Processed strong-motion data for the El Salvador earthquake of June 19, 1982

1988 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Sylvester Theodore Algermissen
Keyword(s):  
El Salvador ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data

Empirical equations for the prediction of PGA and pseudo spectral accelerations using Iranian strong-motion data

2017 ◽  
Vol 22 (1) ◽  
pp. 263-285 ◽  
Author(s):  
H. Zafarani ◽  
Lucia Luzi ◽  
Giovanni Lanzano ◽  
M. R. Soghrat
Keyword(s):  
Strong Motion ◽  
Empirical Equations ◽  
Strong Motion Data ◽  
Motion Data ◽  
Spectral Accelerations ◽  
Pseudo Spectral

scholarly journals Energy Rate Functions: An Overview of HHT-based Earthquake Source Characterization using Strong Motion Data

2020 ◽  
Author(s):  
Swapnil Mache ◽  
Nishant Chauhan ◽  
Avigyan Chatterjee ◽  
Kusala Rajendran
Keyword(s):  
Strong Motion ◽  
Earthquake Source ◽  
Source Characterization ◽  
Strong Motion Data ◽  
Energy Rate ◽  
Motion Data ◽  
Rate Functions

Duration prediction of Chilean strong motion data using machine learning

2021 ◽  
Vol 109 ◽  
pp. 103253
Author(s):  
Sarit Chanda ◽  
M.C. Raghucharan ◽  
K.S.K. Karthik Reddy ◽  
Vasudeo Chaudhari ◽  
Surendra Nadh Somala
Keyword(s):  
Machine Learning ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data ◽  
Duration Prediction

Re-digitization of Strong Motion Data Recorded by SMAC-M Accelerographs

2021 ◽  
Vol 21 (1) ◽  
pp. 1_25-1_45
Author(s):  
Toshihide KASHIMA ◽  
Shin KOYAMA ◽  
Hiroto NAKAGAWA
Keyword(s):  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data
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