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>

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.

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.

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.

Stress Drop Derived from Spectral Analysis Considering the Hypocentral Depth in the Attenuation Model: Application to the Ridgecrest Region, California

2021 ◽  
Author(s):  
Dino Bindi ◽  
Hoby N. T. Razafindrakoto ◽  
Matteo Picozzi ◽  
Adrien Oth
Keyword(s):  
Stress Drop ◽  
Spectral Decomposition ◽  
Epicentral Distance ◽  
Source Parameters ◽  
S Wave ◽  
Qualitative Comparison ◽  
Ground Shaking ◽  
Attenuation Model ◽  
Hypocentral Distance ◽  
The Impact

ABSTRACT We investigate the impact of considering a depth-dependent attenuation model on source parameters assessed through a spectral decomposition. In particular, we evaluate the effect of considering the hypocentral depth as an additional variable for the attenuation model, using as the target the tendency of the average stress drop to increase with depth, as observed in recent studies. We analyze the Fourier spectra of S-wave windows for about 1900 earthquakes with a magnitude above 2.5 recorded in the Ridgecrest region, southern California. Two different parameterizations of the attenuation term are implemented in the spectral decomposition, either as a function of the hypocentral distance alone or as a function of both epicentral distance and depth. The comparison of the spectral attenuation curves shows that, although the hypocentral model describes, on average, the range of values spanned by the attenuation curve for different depths, systematic differences with distance, depth, and frequency are observed. These differences are transferred to the source spectra and, in turn, to the source parameters extracted from the best-fitting ω−2 models. In particular, stress drops for events deeper than 7 km are, on average, almost double even when depth is introduced explicitly in the attenuation model. The increase of stress drop with depth is confirmed also after accounting for the increase of the shear velocity with depth, which absorbs about 30%–40% of the total increase. Moreover, a qualitative comparison with a model for the gradient of the effective normal stress confirms the reliability of the observed trend. Finally, the coherent spatial patterns shown by a simplified 2D tomographic representation of the spectral residuals highlights the impact on ground-shaking variability of the lateral variability of the crustal attenuation properties in the region.

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>

Off-Network Earthquake Location by Earthquake Early Warning Systems: Methodology and Validation

2021 ◽  
Author(s):  
Mark Netanel ◽  
Andreas Samuel Eisermann ◽  
Alon Ziv
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
S Phase ◽  
Earthquake Early Warning ◽  
Warning Systems ◽  
S Wave ◽  
Earthquake Early Warning Systems ◽  
Back Azimuth ◽  
Magnitude Determination ◽  
Epicenter Location

ABSTRACT Regional source-based earthquake early warning systems perform three consecutive tasks: (1) detection and epicenter location, (2) magnitude determination, and (3) ground-motion prediction. The correctness of the magnitude determination is contingent on that of the epicenter location, and the credibility of the ground-motion prediction depends on those of the epicenter location and the magnitude determination. Thus, robust epicenter location scheme is key for regional earthquake early warning systems. Available source-based systems yield acceptably accurate locations when the earthquakes occur inside the real-time seismic network, but they return erroneous results otherwise. In this study, a real-time algorithm that is intended as a supplement to an existing regional earthquake early warning systems is introduced with the sole objective of ameliorating its off-network location capacity. The new algorithm combines measurements from three or more network stations that are analyzed jointly using an array methodology to give the P-wave slowness vector and S-phase arrival time. Prior to the S-phase picking, the nonarrival of the S phase is used for determining a minimum epicentral distance. This estimate is updated repeatedly with elapsed time until the S phase is picked. Thus, the system timeliness is not compromised by waiting for the S-phase arrival. After the S wave is picked, an epicentral location can be determined using a single array by intersecting the back-azimuth beam with the S-minus-P annulus. When several arrays are assembled, the back azimuth and P and S picks from all arrays are combined to constrain the epicenter. The performance of the array processing for back azimuth and S-wave picking is assessed using a large number of accelerograms, recorded by nine strong motion sensors of the KiK-net seismic network in Japan. The nine stations are treated as three distinct seismic arrays, comprising three stations each. Good agreement is found between array-based and catalog-reported parameters. Finally, the advantage of the new array methodology with respect to alternative schemes for back azimuth and distance is demonstrated.

Source parameters of the 1949 magnitude 7.1 south Puget Sound, Washington, earthquake as determined from long-period body waves and strong ground motions

1987 ◽  
Vol 77 (5) ◽  
pp. 1530-1557
Author(s):  
Glenn Eli Baker ◽  
Charles A. Langston
Keyword(s):  
Focal Mechanism ◽  
Source Parameters ◽  
Puget Sound ◽  
Vertical Component ◽  
Test Laboratory ◽  
Body Waves ◽  
Polarization Angle ◽  
S Wave ◽  
Fault Surface ◽  
Strong Ground

Abstract Teleseismic P, SH, and SV first motions and SH to SV amplitude ratios recorded at eight teleseismic receivers from the 1949 magnitude 7.1 Olympia, Washington, earthquake in combination with data from three stations at regional distances were utilized in a grid testing routine to constrain focal mechanism. Identification of the pP phase places the event at 54 km depth. Distinct pulses, assumed to be source effects, are observed in the far-field waveforms. Analysis of these pulses for directivity made possible discrimination between the fault and auxiliary planes. The plane taken to represent the fault surface strikes east-west ± 15°, dips 45° ± 15° to the north, and has nearly pure left-lateral slip. The preferred source model has an eastward propagation of 40 km. Surface reflections of successive source pulses suggest an upward component of propagation of 5 km. Bounds on the earthquake location and rupture of the 13 April event were determined using depth and source mechanism constraints from the teleseismic study and characteristics of local strong ground motion recordings. The 9-sec S-instrument trigger time seen in the Seattle acceleration recordings places the event at least 60 km from Seattle. Strong motion velocity at the Olympia Highway Test Laboratory is characterized by an impulsive and rectilinear S wave. The low amplitude of the vertical component of initial S motion suggests that either the epicenter is within 5 km of the Olympia Highway Test Laboratory for a pure incident SV wave or located along an azimuth of N159° if the wave is SH. The combined constraint of minimum distance from Seattle and the S polarization angle implied by the teleseismic data focal mechanism places the initiation of rupture 5 to 10 km north to north-northwest of the Olympia Highway Test Laboratory at 47.13°N, 122.95°W. This is approximately 20 km west of previously determined epicenters. The T axis, gently dipping to the southeast, supports other evidence that the Juan de Fuca plate dips to the southeast in a zone between segments of the plate north and south of the event's location. The fault plane's slip is taken to indicate that subduction is still active beneath Washington and that motion of the two segments is probably independent.

Non-parametric spectral modelling of source parameters, path attenuation and site effects from broad-band waveforms of the Alborz earthquakes (2005–2017)

2019 ◽  
Vol 219 (3) ◽  
pp. 1514-1531
Author(s):  
Somayeh Ahmadzadeh ◽  
G Javan Doloei ◽  
Stefano Parolai ◽  
Adrien Oth
Keyword(s):  
Source Parameters ◽  
Broad Band ◽  
Site Amplification ◽  
Earthquake Source ◽  
Frequency Range ◽  
S Wave ◽  
Magnitude Range ◽  
Earthquake Source Parameters ◽  
Generalized Inversion Technique ◽  
Non Parametric

SUMMARY S-wave spectral amplitudes from 312 crustal earthquakes recorded at the Iranian National Broadband Seismic Network in the Alborz region between 2005 and 2017 are analysed in order to evaluate earthquake source parameters, path attenuation and site amplification functions using the non-parametric generalized inversion technique (GIT). We exploit a total number of 1117 seismograms with ML 3–5.6 in the frequency range 0.3–20 Hz. The evaluated non-parametric attenuation functions decay uniformly with distance for the entire frequency range and the estimated S-wave quality factor shows low Q values with relatively strong frequency dependence. We assume the omega-square source model to retrieve earthquake source parameters from the inverted source spectra. The obtained stress drops range from 0.02 to 16 MPa with a mean value of 1.1 MPa. Stress drop and radiated energy show fairly self-similar scaling with seismic moment over the available magnitude range; however, the magnitude range of this study is too narrow to draw a definite conclusion on source scaling characteristics. The obtained moment magnitude Mw and the local magnitude ML are linearly correlated and approximately equivalent in the range of Mw 3–4. For larger events, Mw generally underestimates ML by about 0.1–0.5 magnitude units. The estimated site amplification functions for horizontal component (GIT H) are nearly flat with no obvious pre-dominant frequency peaks for most stations, as expected for the sites of permanent broad-band seismic stations located on rock, though a few stations show amplification peaks from 1 to 8 Hz, with a maximum amplification of about a factor of 7 with respect to the reference site. The evaluated site responses for the vertical components present remarkable amplification or deamplification, leading to differences of the H/V amplitude levels in comparison with the GIT H amplification curves. The results of this study provide a valuable basis for predicting appropriate ground motions in a context of seismic hazard assessment.

scholarly journals An Evaluation of Strong-Motion Parameters at the S-net Ocean-Bottom Seismograph Sites Near the Kanto Basin for Earthquake Early Warning

2021 ◽  
Vol 9 ◽  
Author(s):  
Yadab P. Dhakal ◽  
Takashi Kunugi
Keyword(s):  
Early Warning ◽  
Low Frequency ◽  
Strong Motion ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Japan Trench ◽  
S Wave ◽  
Earthquake Disaster ◽  
Strong Motion Records ◽  
Kanto Basin

We analyzed strong-motion records at the ground and borehole in and around the Kanto Basin and the seafloor in the Japan Trench area from three nearby offshore earthquakes of similar magnitudes (Mw 5.8–5.9). The seafloor strong-motion records were obtained from S-net, which was established to enhance tsunami and earthquake early warnings after the 2011 great Tohoku-oki earthquake disaster. The borehole records were obtained from MeSO-net, a dense network of seismometers installed at a depth of 20 m in the Tokyo metropolitan area. The ground records were obtained from the K-NET and KiK-net networks, established after the 1995 great Hanshin-Awaji earthquake disaster. The MeSO-net and S-net stations record the shakings continuously, while the K-NET and KiK-net records are based on triggering thresholds. It is crucial to evaluate the properties of strong motions recorded by the S-net for earthquake early warning (EEW). This paper compared the peak ground accelerations (PGAs) and peak ground velocities (PGVs) between the S-net and K-NET/KiK-net stations. Because the MeSO-net records were from the borehole, we compared the PGAs and significant durations of the low-frequency motions (0.1–0.5 Hz) between the S-net and MeSO-net stations from identical record lengths. We found that the horizontal PGAs and PGVs at the S-net sites were similar to or larger than the K-NET/KiK-net sites for the S wave. In contrast, the vertical PGAs and PGVs at the S-net sites were similar to or smaller than those at the K-NET/KiK-net sites for the S wave. Particularly, the PGAs and PGVs for the P-wave parts on the vertical-component records of S-net were, on average, much smaller than those of K-NET/KiK-net records. The difference was more evident in the PGAs. The average ratios of S-wave horizontal to vertical PGAs were about 2.5 and 5 for the land and S-net sites, respectively. The low-frequency PGAs at the S-net sites were similar to or larger than those of the MeSO-net borehole records. The significant durations between the two-networks low-frequency records were generally comparable. Quantification of the results from a larger dataset may contribute to ground-motion prediction for EEW and the design of the offshore facilities.

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.

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