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.

Strain to Ground Motion Conversion of DAS Data for Earthquake Magnitude and Stress Drop Determination

2021 ◽  
Author(s):  
Itzhak Lior ◽  
Anthony Sladen ◽  
Diego Mercerat ◽  
Jean-Paul Ampuero ◽  
Diane Rivet ◽  
...  
Keyword(s):  
Early Warning ◽  
Stress Drop ◽  
Source Parameters ◽  
Simulated Data ◽  
Earthquake Early Warning ◽  
Body Waves ◽  
Ocean Bottom ◽  
Correlated Noise ◽  
S Wave ◽  
Earthquake Source Parameters

<p>The use of Distributed Acoustic Sensing (DAS) presents unique advantages for earthquake monitoring compared with standard seismic networks: spatially dense measurements adapted for harsh environments and designed for remote operation. However, the ability to determine earthquake source parameters using DAS is yet to be fully established. In particular, resolving the magnitude and stress drop, is a fundamental objective for seismic monitoring and earthquake early warning. To apply existing methods for source parameter estimation to DAS signals, they must first be converted from strain to ground motions. This conversion can be achieved using the waves’ apparent phase velocity, which varies for different seismic phases ranging from fast body-waves to slow surface- and scattered-waves. To facilitate this conversion and improve its reliability, an algorithm for slowness determination is presented, based on the local slant-stack transform. This approach yields a unique slowness value at each time instance of a DAS time-series. The ability to convert strain-rate signals to ground accelerations is validated using simulated data and applied to several earthquakes recorded by dark fibers of three ocean-bottom telecommunication cables in the Mediterranean Sea. The conversion emphasizes fast body-waves compared to slow scattered-waves and ambient noise, and is robust even in the presence of correlated noise and varying wave propagation directions. Good agreement is found between source parameters determined using converted DAS waveforms and on-land seismometers for both P- and S-wave records. The demonstrated ability to resolve source parameters using P-waves on horizontal ocean-bottom fibers is key for the implementation of DAS based earthquake early warning, which will significantly improve hazard mitigation capabilities for offshore and tsunami earthquakes.</p>

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>

scholarly journals NEAR REAL-TIME DETERMINATION OF EARTHQUAKE SOURCE PARAMETERS FOR TSUNAMI EARLY WARNING FROM GEODETIC OBSERVATIONS

2016 ◽  
Vol XLI-B8 ◽  
pp. 117-120
Author(s):  
Sunanda Manneela ◽  
T. Srinivasa Kumar ◽  
Shailesh R. Nayak
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Rapid Determination ◽  
Moment Tensor ◽  
Source Parameters ◽  
Critical Role ◽  
Tsunami Source ◽  
Tsunami Early Warning ◽  
Earthquake Source Parameters

Exemplifying the tsunami source immediately after an earthquake is the most critical component of tsunami early warning, as not every earthquake generates a tsunami. After a major under sea earthquake, it is very important to determine whether or not it has actually triggered the deadly wave. The near real-time observations from near field networks such as strong motion and Global Positioning System (GPS) allows rapid determination of fault geometry. Here we present a complete processing chain of Indian Tsunami Early Warning System (ITEWS), starting from acquisition of geodetic raw data, processing, inversion and simulating the situation as it would be at warning center during any major earthquake. We determine the earthquake moment magnitude and generate the centroid moment tensor solution using a novel approach which are the key elements for tsunami early warning. Though the well established seismic monitoring network, numerical modeling and dissemination system are currently capable to provide tsunami warnings to most of the countries in and around the Indian Ocean, the study highlights the critical role of geodetic observations in determination of tsunami source for high-quality forecasting.

scholarly journals Strain to Ground Motion Conversion of DAS Data for Earthquake Magnitude and Stress Drop Determination

2021 ◽  
Author(s):  
Itzhak Lior ◽  
Anthony Sladen ◽  
Diego Mercerat ◽  
Jean-Paul Ampuero ◽  
Diane Rivet ◽  
...  
Keyword(s):  
Early Warning ◽  
Stress Drop ◽  
Source Parameters ◽  
Simulated Data ◽  
Earthquake Early Warning ◽  
Body Waves ◽  
Ocean Bottom ◽  
Correlated Noise ◽  
S Wave ◽  
Earthquake Source Parameters

Abstract. The use of Distributed Acoustic Sensing (DAS) presents unique advantages for earthquake monitoring compared with standard seismic networks: spatially dense measurements adapted for harsh environments and designed for remote operation. However, the ability to determine earthquake source parameters using DAS is yet to be fully established. In particular, resolving the magnitude and stress drop, is a fundamental objective for seismic monitoring and earthquake early warning. To apply existing methods for source parameter estimation to DAS signals, they must first be converted from strain to ground motions. This conversion can be achieved using the waves' apparent phase velocity, which varies for different seismic phases ranging from fast body-waves to slow surface- and scattered-waves. To facilitate this conversion and improve its reliability, an algorithm for slowness determination is presented, based on the local slant-stack transform. This approach yields a unique slowness value at each time instance of a DAS time-series. The ability to convert strain-rate signals to ground accelerations is validated using simulated data and applied to several earthquakes recorded by dark fibers of three ocean-bottom telecommunication cables in the Mediterranean Sea. The conversion emphasizes fast body-waves compared to slow scattered-waves and ambient noise, and is robust even in the presence of correlated noise and varying wave propagation directions. Good agreement is found between source parameters determined using converted DAS waveforms and on-land seismometers for both P- and S-wave records. The demonstrated ability to resolve source parameters using P-waves on horizontal ocean-bottom fibers is key for the implementation of DAS based earthquake early warning, which will significantly improve hazard mitigation capabilities for offshore and tsunami earthquakes.

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

scholarly journals Comparing the Performance of Regional Earthquake Early Warning Algorithms in Europe

2021 ◽  
Vol 9 ◽  
Author(s):  
Elisa Zuccolo ◽  
Gemma Cremen ◽  
Carmine Galasso
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Risk Mitigation ◽  
Source Parameters ◽  
Warning System ◽  
Optimal Choice ◽  
Earthquake Early Warning ◽  
Ground Shaking ◽  
Processing Times

Several earthquake early warning (EEW) algorithms have been developed worldwide for rapidly estimating real-time information (i.e., location, magnitude, ground shaking, and/or potential consequences) about ongoing seismic events. This study quantitatively compares the operational performance of two popular regional EEW algorithms for European conditions of seismicity and network configurations. We specifically test PRobabilistic and Evolutionary early warning SysTem (PRESTo) and the implementation of the Virtual Seismologist magnitude component within SeisComP, VS(SC), which we use jointly with the SeisComP scanloc module for locating events. We first evaluate the timeliness and accuracy of the location and magnitude estimates computed by both algorithms in real-time simulation mode, accounting for the continuous streaming of data and effective processing times. Then, we focus on the alert-triggering (decision-making) phase of EEW and investigate both algorithms’ ability to yield accurate ground-motion predictions at the various temporal instances that provide a range of warning times at target sites. We find that the two algorithms show comparable performances in terms of source parameters. In addition, PRESTo produces better rapid estimates of ground motion (i.e., those that facilitate the largest lead times); therefore, we conclude that PRESTo may have a greater risk-mitigation potential than VS(SC) in general. However, VS(SC) is the optimal choice of EEW algorithm if shorter warning times are permissible. The findings of this study can be used to inform current and future implementations of EEW systems in Europe.

scholarly journals Strain to ground motion conversion of distributed acoustic sensing data for earthquake magnitude and stress drop determination

Solid Earth ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 1421-1442
Author(s):  
Itzhak Lior ◽  
Anthony Sladen ◽  
Diego Mercerat ◽  
Jean-Paul Ampuero ◽  
Diane Rivet ◽  
...  
Keyword(s):  
Early Warning ◽  
Stress Drop ◽  
Source Parameters ◽  
Earthquake Early Warning ◽  
Body Waves ◽  
Ocean Bottom ◽  
Correlated Noise ◽  
Acoustic Sensing ◽  
Earthquake Source Parameters ◽  
Distributed Acoustic Sensing

Abstract. The use of distributed acoustic sensing (DAS) presents unique advantages for earthquake monitoring compared with standard seismic networks: spatially dense measurements adapted for harsh environments and designed for remote operation. However, the ability to determine earthquake source parameters using DAS is yet to be fully established. In particular, resolving the magnitude and stress drop is a fundamental objective for seismic monitoring and earthquake early warning. To apply existing methods for source parameter estimation to DAS signals, they must first be converted from strain to ground motions. This conversion can be achieved using the waves' apparent phase velocity, which varies for different seismic phases ranging from fast body waves to slow surface and scattered waves. To facilitate this conversion and improve its reliability, an algorithm for slowness determination is presented, based on the local slant-stack transform. This approach yields a unique slowness value at each time instance of a DAS time series. The ability to convert strain-rate signals to ground accelerations is validated using simulated data and applied to several earthquakes recorded by dark fibers of three ocean-bottom telecommunication cables in the Mediterranean Sea. The conversion emphasizes fast body waves compared to slow scattered waves and ambient noise and is robust even in the presence of correlated noise and varying wave propagation directions. Good agreement is found between source parameters determined using converted DAS waveforms and on-land seismometers for both P and S wave records. The demonstrated ability to resolve source parameters using P waves on horizontal ocean-bottom fibers is key for the implementation of DAS-based earthquake early warning, which will significantly improve hazard mitigation capabilities for offshore earthquakes, including those capable of generating tsunami.

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