Generic Source Parameter Determination for Earthquake Early Warning: Theory, Observations and Implications for the Mw 7.1 Ridgecrest Earthquake

2020 ◽  
Author(s):  
alon ziv ◽  
Itzhak Lior
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Stress Drop ◽  
Epicentral Distance ◽  
Strong Motion ◽  
Parameter Determination ◽  
Real Time Processing ◽  
Tectonic Environment ◽  
The Time Domain

<p>A generic approach for real-time magnitude and stress drop is introduced that is based on the omega-squared model (Brune, 1970) and results from Lior and Ziv (2018). This approach leads to approximate expressions for earthquake magnitude and stress drop as functions of epicentral distance and ground motion root-mean-squares (rms). Because the rms of the ground motion (acceleration, velocity and displacement) may be calculated directly from the seismogram in the time domain, the use of this approach for automated real-time processing is rather straightforward. Once the seismic moment and stress drop are known, they may be plugged in the ground motion prediction equations (GMPE) of Lior and Ziv (2018) to map the predicted peak shaking.</p><p>This method is generic in the sense that it is readily implementable in any tectonic environment, without having to go through a calibration phase. The potential of these results for automated early warning applications is demonstrated using a large dataset of about 6000 seismograms recorded by strong-motion and broadband velocity sensors from different tectonic environments. Optimal real-time performance is achieved by integrating magnitude and stress drop estimates into an evolutionary algorithm. The result of such an evolutionary calculation for the Mw 7.1 Ridgecrest earthquake indicates close agreement with the true magnitude.</p>

Generic Source Parameter Determination and Ground-Motion Prediction for Earthquake Early Warning

2020 ◽  
Vol 110 (1) ◽  
pp. 345-356 ◽  
Author(s):  
Itzhak Lior ◽  
Alon Ziv
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Stress Drop ◽  
Seismic Moment ◽  
Source Parameter ◽  
Parameter Determination ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Empirical Relations

ABSTRACT Currently available earthquake early warning systems employ region-specific empirical relations for magnitude determination and ground-motion prediction. Consequently, the setting up of such systems requires lengthy calibration and parameter tuning. This situation is most problematic in low seismicity and/or poorly instrumented regions, where the data available for inferring those empirical relations are scarce. To address this issue, a generic approach for real-time magnitude, stress drop, and ground-motion prediction is introduced that is based on the omega-squared model. This approach leads to the following approximate expressions for seismic moment: M0∝RT0.5Drms1.5/Vrms0.5, and stress drop: Δτ∝RT0.5Arms3/Vrms2, in which R is the hypocentral distance; T is the data interval; and Drms, Vrms, and Arms are the displacement, velocity, and acceleration root mean squares, respectively, which may be calculated in the time domain. The potential of these relations for early warning applications is demonstrated using a large composite data set that includes the two 2019 Ridgecrest earthquakes. A quality parameter is introduced that identifies inconsistent earthquake magnitude and stress-drop estimates. Once initial estimates of the seismic moment and stress drop become available, the peak ground velocity and acceleration may be estimated in real time using the generic ground-motion prediction equation of Lior and Ziv (2018). The use of stress drop for ground-motion prediction is shown to be critical for strong ground accelerations. The main advantages of the generic approach with respect to the empirical approach are that it is readily implementable in any seismic region, allows for the easy update of magnitude, stress drop, and shaking intensity with time, and uses source parameter determination and peak ground motion predictions that are subject to the same model assumptions, thus constituting a self-consistent early warning method.

Too-Late Warnings by Estimating Mw: Earthquake Early Warning in the Near-Fault Region

2020 ◽  
Vol 110 (3) ◽  
pp. 1276-1288
Author(s):  
Mitsuyuki Hoshiba
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Strong Motion ◽  
Earthquake Early Warning ◽  
Motion Generation ◽  
Near Fault ◽  
Time Gap ◽  
Fault Region ◽  
Strong Ground

ABSTRACT Earthquake early warning (EEW) systems aim to provide advance warnings of impending strong ground shaking. Many EEW systems are based on a strategy in which precise and rapid estimates of source parameters, such as hypocentral location and moment magnitude (Mw), are used in a ground-motion prediction equation (GMPE) to predict the strength of ground motion. For large earthquakes with long rupture duration, the process is repeated, and the prediction is updated in accordance with the growth of Mw during the ongoing rupture. However, in some regions near the causative fault this approach leads to late warnings, because strong ground motions often occur during earthquake ruptures before Mw can be confirmed. Mw increases monotonically with elapsed time and reaches its maximum at the end of rupture, and ground motion predicted by a GMPE similarly reaches its maximum at the end of rupture, but actual generation of strong motion is earlier than the end of rupture. A time gap between maximum Mw and strong-motion generation is the first factor contributing to late warnings. Because this time gap exists at any point of time during the rupture, a late warning is inherently caused even when the growth of Mw can be monitored in real time. In the near-fault region, a weak subevent can be the main contributor to strong ground motion at a site if the distance from the subevent to the site is small. A contribution from a weaker but nearby subevent early in the rupture is the second factor contributing to late warnings. Thus, an EEW strategy based on rapid estimation of Mw is not suitable for near-fault regions where strong shaking is usually recorded. Real-time monitoring of ground motion provides direct information for real-time prediction for these near-fault locations.

Source parameter analysis from strong motion records of the Friuli, Italy, earthquake sequence (1976-1977)

1987 ◽  
Vol 77 (4) ◽  
pp. 1127-1146
Author(s):  
Giuseppe De Natale ◽  
Raul Madariaga ◽  
Roberto Scarpa ◽  
Aldo Zollo
Keyword(s):  
Frequency Domain ◽  
Stress Drop ◽  
High Frequency ◽  
Epicentral Distance ◽  
Strong Motion ◽  
Source Model ◽  
Parameter Analysis ◽  
Corner Frequency ◽  
Spectral Parameters ◽  
Single Station

Abstract Time and frequency domain analyses are applied to strong motion data recorded in Friuli, Italy, during 1976 to 1977. An inversion procedure to estimate spectral parameters (low frequency level, corner frequency, and high frequency decay) has been applied to displacement spectra using a simple earthquake source model with a single corner frequency. The data were digitized accelerograms from ENEA-ENEL portable and permanent networks. Instrument-corrected SH waves were selected from a set of 138 three-component, hand-digitized records and 28 automatically digitized records. Thirty-eight events with stations having 8 to 32 km epicentral distance were studied. Different stress drop estimates were performed showing high values (200 to 300 bars, on the average) with seismic moments ranging from 2.8 × 1022 to 8.0 × 1024 dyne-cm. The observation of systematic higher values of Brune stress drop (obtained from corner frequencies) with respect to other time and frequency domain estimates of stress release, and the evidence on time series of multiple rupture episodes suggest that the observed corner frequencies are most probably related to subevent ruptures rather than the overall fault size. Seven events recorded at more than one station show a good correlation between rms, Brune, and dynamic stress drops, and a constant scaling of this parameter as a function of the seismic moment. When single station events are also considered, a slight moment dependence of these three stress drop estimates is observed differently. This may be explained by an inadequacy of the ω−2 high-frequency decay of the source model or by high-frequency attenuation due to propagation effects. The high-frequency cutoff of acceleration spectra indicates the presence of an Fmax in the range of 5 to 14 Hz, except for the stations where local site effects produce spectral peaks.

30 Ooctober 2020 Samos-Sigacik Earthquake: On Strong Ground Motion and Local Site Amplification

2021 ◽  
Author(s):  
Eser Çakti ◽  
Karin Sesetyan ◽  
Ufuk Hancilar ◽  
Merve Caglar ◽  
Emrullah Dar ◽  
...  
Keyword(s):  
Ground Motion ◽  
Structural Damage ◽  
Strong Ground Motion ◽  
Epicentral Distance ◽  
Strong Motion ◽  
Aegean Sea ◽  
Site Amplification ◽  
Local Site ◽  
Basin Effects ◽  
Strong Ground

<p>The Mw 6.9 earthquake that took place offshore between the Greek island of Samos and Turkey’s İzmir province on 30 October 2020 came hardly as a surprise. Due to the extensional tectonic regime of the Aegean and high deformation rates, earthquakes of similar size frequently occur in the Aegean Sea on fault segments close to the shores of Turkey, affecting the settlements on mainland Turkey and on the Greek Islands. Samos-Sigacik earthquake had a normal faulting mechanism. It was recorded by the strong motion networks in Turkey and Greece. Although expected, the earthquake was an  outstanding event in the sense of  highly localized, significant levels of building damage as a result of amplified ground motion levels. This presentation is an overview of strong ground motion characteristics of this important event both regionally and locally. Mainshock records suggest that local site effects, enhanced by basin effects could be responsible for structural damage in central Izmir, the third largest city of Turkey located at 60-70 km epicentral distance. We installed a seven-station network in Bayraklı and Karşıyaka districts of İzmir within three days of the mainshock in search of site and basin effects.  Through analysis of recorded aftershocks we explore the amplification characeristics of soils in the two aforementioned districts  and try to understand the role basin effects might have played in the resulting ground motion levels and consequently damage. </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

End-to-end PGA estimation for earthquake early warning using transformer networks

2020 ◽  
Author(s):  
Jannes Münchmeyer ◽  
Dino Bindi ◽  
Ulf Leser ◽  
Frederik Tilmann
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Lead Times ◽  
Ground Shaking ◽  
Target Sites ◽  
End To End

<p>The key task of earthquake early warning is to provide timely and accurate estimates of the ground shaking at target sites. Current approaches use either source or propagation based methods. Source based methods calculate fast estimates of the earthquake source parameters and apply ground motion prediction equations to estimate shaking. They suffer from saturation effects for large events, simplified assumptions and the need for a well known hypocentral location, which usually requires arrivals at multiple stations. Propagation based methods estimate levels of shaking from the shaking at neighboring stations and therefore have short warning times and possibly large blind zones. Both methods only use specific features from the waveform. In contrast, we present a multi-station neural network method to estimate horizontal peak ground acceleration (PGA) anywhere in the target region directly from raw accelerometer waveforms in real time.</p><p>The three main components of our model are a convolutional neural network (CNN) for extracting features from the single-station three-component accelerograms, a transformer network for combining features from multiple stations and for transferring them to the target site features and a mixture density network to generate probabilistic PGA estimates. By using a transformer network, our model is able to handle a varying set and number of stations as well as target sites. We train our model end-to-end using recorded waveforms and PGAs. We use data augmentation to enable the model to provide estimations at targets without waveform recordings. Starting with the arrival of a P wave at any station of the network, our model issues real-time predictions at each new sample. The predictions are Gaussian mixtures, giving estimates of both expected value and uncertainties. The model can be used to predict PGA at specific target sites, as well as to generate ground motion maps.</p><p>We analyze the model on two strong motion data sets from Japan and Italy in terms of standard deviation and lead times. Through the probabilistic predictions we are able to give lead times for different levels of uncertainty and ground shaking. This allows to control the ratio of missed detections to false alerts. Preliminary analysis suggest that for levels between 1%g and 10%g our model achieves multi-second lead times even for the closest stations at a false-positive rate below 25%. For an example event at 50 km depth, lead times at the closest stations with epicentral distances below 20 km are 6 s and 7.5 s. This suggests that our model is able to effectively use the difference between P and S travel time and accurately assess the future level of ground shaking from the first parts of the P wave. It additionally makes effective use of the information contained in the absence of signal at other stations.</p>

scholarly journals Real-Time Characterization of Finite Rupture and Its Implication for Earthquake Early Warning: Application of FinDer to Existing and Planned Stations in Southwest China

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiawei Li ◽  
Maren Böse ◽  
Yu Feng ◽  
Chen Yang
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Line Source ◽  
Average Distance ◽  
Strong Motion ◽  
Earthquake Early Warning ◽  
Time Data ◽  
Yunnan Region ◽  
Motion Time ◽  
Source Models

Earthquake early warning (EEW) not only improves resilience against the risk of earthquake disasters, but also provides new insights into seismological processes. The Finite-Fault Rupture Detector (FinDer) is an efficient algorithm to retrieve line-source models of an ongoing earthquake from seismic real-time data. In this study, we test the performance of FinDer in the Sichuan-Yunnan region (98.5oE–106.0oE, 22.0oN–34.0oN) of China for two datasets: the first consists of seismic broadband and strong-motion records of 58 earthquakes with 5.0 ≤ MS ≤ 8.0; the second comprises additional waveform simulations at sites where new stations will be deployed in the near future. We utilize observed waveforms to optimize the simulation approach to generate ground-motion time series. For both datasets the resulting FinDer line-source models agree well with the reported epicenters, focal mechanisms, and finite-source models, while they are computed faster compared to what traditional methods can achieve. Based on these outputs, we determine a theoretical relation that can predict for which magnitudes and station densities FinDer is expected to trigger, assuming that at least three neighboring stations must have recorded accelerations of 4.6 cm/s2 or more. We find that FinDer likely triggers and sends out a report, if the average distance between the epicenter and the three closest stations, Depi, is equal or smaller than log10 (Ma + b) + c, where a = 1.91, b = 5.93, and c = 2.34 for M = MW ≥ 4.8, and c = 2.49 for M = MS ≥ 5.0, respectively. If the data used in this study had been available in real-time, 40–70% of sites experiencing seismic intensities of V-VIII (on both Chinese and MMI scales) and 20% experiencing IX-X could have been issued a warning 5–10 s before the S-wave arrives. Our offline tests provide a useful reference for the planned installation of FinDer in the nationwide EEW system of Chinese mainland.

Empirical Spatial Coherency Functions for Application to Soil-Structure Interaction Analyses

1991 ◽  
Vol 7 (1) ◽  
pp. 1-27 ◽  
Author(s):  
N. A. Abrahamson ◽  
J. F. Schneider ◽  
J. C. Stepp
Keyword(s):  
Ground Motion ◽  
Soil Structure ◽  
Epicentral Distance ◽  
Strong Motion ◽  
Soil Structure Interaction ◽  
High Frequencies ◽  
S Waves ◽  
Structure Interaction ◽  
Strong Ground ◽  
Spatial Coherency

The spatial coherency of strong ground motion from fifteen earthquakes recorded by the Lotung LSST strong motion array is analyzed. The earthquakes range in magnitude from 3.7 to 7.8 and in epicentral distance from 5 to 80 km. In all, a total of 533 station pairs are used with station separations ranging from 6 to 85 meters. Empirical coherency functions for the horizontal component S-waves appropriate for use in engineering analyses are derived from these data. The derived coherency functions are applicable to all frequencies and to separation distances up to 100 m. For these short station separations, the coherency decreases much faster with increasing frequency than with increasing station separation. The computed coherencies indicate that at high frequencies (>10 Hz) over 25 percent of the power of the ground motion is random for station separations greater than 30 m.

scholarly journals One minute after: Strong-motion map, effective epicenter, and effective magnitude

1997 ◽  
Vol 87 (5) ◽  
pp. 1209-1219 ◽  
Author(s):  
Ta-liang Teng ◽  
Ludan Wu ◽  
Tzay-Chyn Shin ◽  
Yi-Ben Tsai ◽  
William H. K. Lee
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Initial Point ◽  
Strong Motion ◽  
Large Earthquake ◽  
Seismic Monitoring ◽  
Earthquake Early Warning ◽  
Force Balance ◽  
Total Energy Release ◽  
Intensity Map

Abstract This article reports the recent progress on real-time seismic monitoring in Taiwan, particularly the real-time strong-motion monitoring by the Taiwan Central Weather Bureau's telemetered seismic network (CWBSN), which is presently aiming at rapid reporting immediately after a large earthquake occurrence. If rapid reporting can be achieved before the arrival of the strong shaking, earthquake early warning will become possible. CWBSN has achieved the generation of the intensity map, epicenter, and magnitude within 1 min of the occurrence of a large earthquake. Both rapid reporting and early warning are principally applied to large (M ≫ 5) events; the requirement of on-scale waveform recording prompted CWBSN in 1995 to integrate strong-motion sensors (e.g., force-balance accelerometers) into its telemetered seismic monitoring system. Time-domain recursive processing is applied to the multi-channel incoming seismic signals by a group of networked personal computers to generate the intensity map. From the isoseismal contours, an effective epicenter is immediately identified that resides in the middle of the largest (usually the 100-gal) contour curve of the intensity map. An effective magnitude is also defined that can be derived immediately from the surface area covered by the largest (usually the 100-gal) contour curve. For a large event with a finite rupture surface, the epicenter and magnitude so derived are more adequate estimates of the source location and of the strength of destruction. The effective epicenter gives the center of the damage area; it stands in contrast with the conventional epicenter location, which only gives the initial point of rupture nucleation. The effective magnitude reflects more closely the earthquake damage potential, instead of the classical magnitude definition that emphasizes the total energy release. The CWBSN has achieved in obtaining the above crucial source information well within 1 min. This time can further be reduced to better than 30 sec, as illustrated by the example in this article, showing that earthquake early warning is indeed an achievable goal. The rapid reporting and early warning information is electronically transmitted to users to allow rapid response actions, with or without further human intervention.

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