scholarly journals Earthquake source parameters from GPS-measured static displacements with potential for real-time application

2013 ◽  
Vol 40 (1) ◽  
pp. 60-65 ◽  
Author(s):  
Thomas B. O'Toole ◽  
Andrew P. Valentine ◽  
John H. Woodhouse
Keyword(s):  
Real Time ◽  
Source Parameters ◽  
Earthquake Source ◽  
Earthquake Source Parameters ◽  
Real Time Application

scholarly journals Predicting approximate seismic responses in multistory buildings from real-time earthquake source information, for earthquake early warning applications

2021 ◽  
Author(s):  
Gemma Cremen ◽  
Omar Velazquez ◽  
Benazir Orihuela ◽  
Carmine Galasso
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Source Parameters ◽  
Earthquake Source ◽  
Earthquake Early Warning ◽  
Multistory Buildings ◽  
Building Model ◽  
Beam Combination ◽  
Earthquake Source Parameters ◽  
Wide Range

AbstractRegional earthquake early warning (EEW) alerts and related risk-mitigation actions are often triggered when the expected value of a ground-motion intensity measure (IM), computed from real-time magnitude and source location estimates, exceeds a predefined critical IM threshold. However, the shaking experienced in mid- to high-rise buildings may be significantly different from that on the ground, which could lead to sub-optimal decision-making (i.e., increased occurrences of false and missed EEW alarms) with the aforementioned strategy. This study facilitates an important advancement in EEW decision-support, by developing empirical models that directly relate earthquake source parameters to resulting approximate responses in multistory buildings. The proposed models can leverage real-time earthquake information provided by a regional EEW system, to provide rapid predictions of structure-specific engineering demand parameters that can be used to more accurately determine whether or not an alert is triggered. We use a simplified continuum building model consisting of a flexural/shear beam combination and vary its parameters to capture a wide range of deformation modes in different building types. We analyse the approximate responses for the building model variations, using Italian accelerometric data and corresponding source parameter information from 54 earthquakes. The resulting empirical prediction equations are incorporated in a real-time Bayesian framework that can be used for building-specific EEW applications, such as (1) early warning of floor-shaking sensed by occupants; and (2) elevator control. Finally, we demonstrate the improvement in EEW alert accuracy that can be achieved using the proposed models.

Earthworm-based automatic system for real-time calculation of local earthquake source parameters

2017 ◽  
Vol 53 (4) ◽  
pp. 267-279 ◽  
Author(s):  
A. A. Stepnov ◽  
A. V. Konovalov ◽  
A. V. Gavrilov ◽  
K. A. Manaychev
Keyword(s):  
Real Time ◽  
Automatic System ◽  
Source Parameters ◽  
Earthquake Source ◽  
Earthquake Source Parameters ◽  
Time Calculation ◽  
Local Earthquake

Near real-time determination of earthquake source parameters from the coastal ocean

2017 ◽  
Author(s):  
Daniel M. Toma ◽  
C. Artero-Delgado ◽  
J. del Rio ◽  
E. Trullol ◽  
X. Roset
Keyword(s):  
Real Time ◽  
Source Parameters ◽  
Coastal Ocean ◽  
Earthquake Source ◽  
Earthquake Source Parameters ◽  
Time Determination

scholarly journals Earthquake source parameters that display the first digit phenomenon

2015 ◽  
Vol 22 (5) ◽  
pp. 625-632
Author(s):  
P. A. Toledo ◽  
S. R. Riquelme ◽  
J. A. Campos
Keyword(s):  
Seismic Moment ◽  
Waiting Times ◽  
Source Parameters ◽  
Tohoku Earthquake ◽  
Earthquake Source ◽  
Coseismic Slip ◽  
Self Organized ◽  
Earthquake Source Parameters ◽  
Self Organized Criticality

Abstract. We study the main parameters of earthquakes from the perspective of the first digit phenomenon: the nonuniform probability of the lower first digit different from 0 compared to the higher ones. We found that source parameters like coseismic slip distributions at the fault and coseismic inland displacements show first digit anomaly. We also found the tsunami runups measured after the earthquake to display the phenomenon. Other parameters found to obey first digit anomaly are related to the aftershocks: we show that seismic moment liberation and seismic waiting times also display an anomaly. We explain this finding by invoking a self-organized criticality framework. We demonstrate that critically organized automata show the first digit signature and we interpret this as a possible explanation of the behavior of the studied parameters of the Tohoku earthquake.

Insights into deep learning for earthquake magnitude and location estimation

2021 ◽  
Author(s):  
Jannes Münchmeyer ◽  
Dino Bindi ◽  
Ulf Leser ◽  
Frederik Tilmann
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Early Warning ◽  
Source Parameters ◽  
Spectral Composition ◽  
Learning Model ◽  
Location Estimation ◽  
Training Set ◽  
Earthquake Source Parameters ◽  
Deep Learning Model

<p><span>The estimation of earthquake source parameters, in particular magnitude and location, in real time is one of the key tasks for earthquake early warning and rapid response. In recent years, several publications introduced deep learning approaches for these fast assessment tasks. Deep learning is well suited for these tasks, as it can work directly on waveforms and </span><span>can</span><span> learn features and their relation from data.</span></p><p><span>A drawback of deep learning models is their lack of interpretability, i.e., it is usually unknown what reasoning the network uses. Due to this issue, it is also hard to estimate how the model will handle new data whose properties differ in some aspects from the training set, for example earthquakes in previously seismically quite regions. The discussions of previous studies usually focused on the average performance of models and did not consider this point in any detail.</span></p><p><span>Here we analyze a deep learning model for real time magnitude and location estimation through targeted experiments and a qualitative error analysis. We conduct our analysis on three large scale regional data sets from regions with diverse seismotectonic settings and network properties: Italy and Japan with dense networks </span><span>(station spacing down to 10 km)</span><span> of strong motion sensors, and North Chile with a sparser network </span><span>(station spacing around 40 km) </span><span>of broadband stations. </span></p><p><span>We obtained several key insights. First, the deep learning model does not seem to follow the classical approaches for magnitude and location estimation. For magnitude, one would classically expect the model to estimate attenuation, but the network rather seems to focus its attention on the spectral composition of the waveforms. For location, one would expect a triangulation approach, but our experiments instead show indications of a fingerprinting approach. </span>Second, we can pinpoint the effect of training data size on model performance. For example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. <span>Third, the model fails for events with few similar training examples. For magnitude, this means that the largest event</span><span>s</span><span> are systematically underestimated. For location, events in regions with few events in the training set tend to get mislocated to regions with more training events. </span><span>These characteristics can have severe consequences in downstream tasks like early warning and need to be taken into account for future model development and evaluation.</span></p>

Earthquake Source Parameters: Rapid Estimates for Tsunami Forecasts and Warnings

2021 ◽  
pp. 299-333
Author(s):  
Barry Hirshorn ◽  
Stuart Weinstein ◽  
Dailin Wang ◽  
Kanoa Koyanagi ◽  
Nathan Becker ◽  
...  
Keyword(s):  
Source Parameters ◽  
Earthquake Source ◽  
Earthquake Source Parameters
Sign in / Sign up

Export Citation Format

Share Document