Early Warning For Large Earthquakes: Observations, Models and Real-Time Data Analysis

This thesis is a collection of works focused on the topic of Earthquake Early Warning, with a special attention to large magnitude events. The topic is addressed from different points of view and the structure of the thesis reflects the variety of the aspects which have been analyzed. The first part is dedicated to the giant, 2011 Tohoku-Oki earthquake. The main features of the rupture process are first discussed. The earthquake is then used as a case study to test the feasibility Early Warning methodologies for very large events. Limitations of the standard approaches for large events arise in this chapter. The difficulties are related to the real-time magnitude estimate from the first few seconds of recorded signal. An evolutionary strategy for the real-time magnitude estimate is proposed and applied to the single Tohoku-Oki earthquake. In the second part of the thesis a larger number of earthquakes is analyzed, including small, moderate and large events. Starting from the measurement of two Early Warning parameters, the behavior of small and large earthquakes in the initial portion of recorded signals is investigated. The aim is to understand whether small and large earthquakes can be distinguished from the initial stage of their rupture process. A physical model and a plausible interpretation to justify the observations are proposed. The third part of the thesis is focused on practical, real-time approaches for the rapid identification of the potentially damaged zone during a seismic event. Two different approaches for the rapid prediction of the damage area are proposed and tested. The first one is a threshold-based method which uses traditional seismic data. Then an innovative approach using continuous, GPS data is explored. Both strategies improve the prediction of large scale effects of strong earthquakes

Early magnitude and potential damage zone estimates for the great Mw 9 Tohoku-Oki earthquake

The Mw 9.0, 2011 Tohoku-Oki earthquake has reopened the discussion among the scientific community about the effectiveness of earthquake early warning for large events. A well-known problem with real-time procedures is the parameter saturation, which may lead to magnitude underestimation for large earthquakes. Here we measure the initial peak ground displacement and the predominant period by progressively expanding the time window and distance range, to provide consistent magnitude estimates (M = 8.4) and a rapid prediction of the potential damage area. This information would have been available 35 s after the first P-wave detection and could have been refined in the successive 20 s using data from more distant stations. We show the suitability of the existing regression relationships between early warning parameters and magnitude, provided that an appropriate P-wave time window is used for parameter estimation. We interpret the magnitude under-estimation as a combined effect of high-pass filtering and frequency dependence of the main radiating source during the rupture process

Source and dynamics of a volcanic caldera unrest : Campi Flegrei, 1983–84

Acknowledgements We thank Tiziana Vanorio, Antonella Amoruso, Luca Crescentini, Nicholas Rawlinson, Yasuko Takei, and David Cornwell for the valuable suggestions regarding the methodology and interpretation. Reviews from Tim Greenfield and two anonymous reviewers helped improving both clarity of the manuscript and interpretation. The Royal Society of Edinburgh - Accademia dei Lincei Bilateral Agreement, the Santander Mobility Award of the College of Physical Sciences, University of Aberdeen, and the TIDES EU COST action granted L.D.S. travel grants for the realisation of this study. E.D.P. has been supported by the EPHESTO and KNOWAVES projects, funded by the Spanish Ministry of Education and Science.Peer reviewedPublisher PD

Test of a Threshold‐Based Earthquake Early‐Warning Method Using Japanese Data

Most of existing earthquake early‐warning systems are regional or on‐site systems. A new concept is the integration of these approaches for the definition of alert levels and the estimation of the earthquake potential damage zone (PDZ). The key element of the method is the real‐time, simultaneous measurement of initial peak displacement (P_d) and period parameter (τ_c) in a 3‐s window after the first P‐wave arrival time at accelerometer stations located at increasing distances from the epicenter. As for the on‐site approach, the recorded values of P_d and τ_c are compared to threshold values, which are set for a minimum magnitude M 6 and instrumental intensity I_MM VII, according to empirical regression analysis of strong‐motion data from different seismic regions. At each recording site the alert level is assigned based on a decisional table with four entries defined by threshold values of the parameters P_d and τ_c. A regional network of stations provides the event location and transmits the information about the alert levels recorded at near‐source stations to more distant sites, before the arrival of the most destructive phase. We present the results of performance tests of this method using ten M>6 Japanese earthquakes that occurred in the period 2000–2009 and propose a very robust methodology for mapping the PDZ in the first seconds after a moderate‐to‐large earthquake. The studied cases displayed a very good matching between the rapidly predicted earthquake PDZ inferred from initial P‐peak displacement amplitudes and the instrumental intensity map, the latter being mapped after the event, using peak ground velocity and/or acceleration, or from field macroseismic surveys

The next generation of Earthquake Early Warning Systems

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Fast determination of earthquake magnitude and fault extent from real-time P-wave recordings

This work is aimed at the automatic and fast characterization of the extended earthquake source, through the progressive measurement of the P-wave displacement amplitude along the recorded seismograms. We propose a straightforward methodology to quickly characterize the earthquake magnitude and the expected length of the rupture, and to provide an approximate estimate of the average stress drop to be used for Earthquake EarlyWarning and rapid response purposes. We test the methodology over a wide distance and magnitude range using a massive Japan earthquake, accelerogram data set. Our estimates of moment magnitude, source duration/ length and stress drop are consistent with the ones obtained by using other techniques and analysing the whole seismic waveform. In particular, the retrieved source parameters follow a self-similar, constant stress-drop scaling (median value of stress drop = 0.71 MPa). For the M 9.0, 2011 Tohoku-Oki event, both magnitude and length are underestimated, due to limited, available P-wave time window (PTWs) and to the low-frequency cut-off of analysed data. We show that, in a simulated real-time mode, about 1-2 seconds would be required for the source parameter determination of M 4-5 events, 3-10 seconds for M 6-7 and 30-40 s for M 8-8.5. The proposed method can also provide a rapid evaluation of the average slip on the fault plane, which can be used as an additional discriminant for tsunami potential, associated to large magnitude earthquakes occurring offshore

A Prospect Of Early Warning Application In Africa

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A p-wave based methodology for rapid, real time determination of seismic moment, fault extent and stress drop

The source characterization for earthquake early warning systems is generally based on the measurement of the peak amplitude or period parameters measured along the early portion of the recorded P and S-wave signals (3 or 4 seconds). These parameters are related to the earthquake size or to the peak ground shaking amplitude through empirical scaling relationships. Standard methodologies for real-time applications typically assume a point-source model of the earthquake source and this assumption may be inadequate to describe the source of large earthquakes, possibly introducing significant biases in the real-time estimation of earthquake magnitude and ground shaking prediction. To avoid magnitude underestimation, the use of limited time windows has been recently replaced by the concept of expanding time windows, showing that standard parameters and existing empirical relationships can be used also for very large earthquakes, provided that appropriate time windows are selected for the parameter measurement. Following the concept of expanding P-wave time windows, here we propose a straightforward methodology, based on the P-wave amplitude, to quickly characterize the finite extension of the seismic source and its scalar moment. In particular, here we investigate whether and how the progressive and evolutionary measurement of early warning parameters can provide a rapid estimate of the event magnitude and of the expected length of the rupture. The methodology we propose is computationally simple and does not require complex signal processing. It is expected to provide a rapid and robust estimation of the source extent, which can significantly improve the accuracy of the ground shaking prediction during the occurrence of very large events

Peak-Duration magnitude for the Irpinia Seismic Network, Southern Italy

The estimation of magnitude is a routine task in the seismological observatories and can be obtained through well-established automatic procedures. Several magnitude scales are available, based on amplitude measurements of different seismic phases, and/or on total signal duration. In particular, duration magnitude is adopted in many regional networks since it allows for a rapid determination of earthquake size, for a large number of events, through a fairly simple procedure. The main purposes of this work are two: to derive a duration magnitude relationship for the Irpinia Seismic Network (ISNet, http://isnet.na.infn.it) in Southern Italy and to develop an automatic procedure for discriminating among events occurring inside and outside the network. To the former aim, we performed a multiple regression analysis to get a duration magnitude relationship of the form M = a*logτ + b*logR + c where M is the local magnitude, τ is the signal duration, and R is the epi(hypo) -central distance. Signal duration is evaluated on the vertical component of velocimeter records as the time from the first P-arrival time to the time along the trace at which the wave amplitude has decreased to the noise level. The parameters a, b and c are determined through a linear regression analysis. For events inside the network (R < 100 km) the coefficient c turned out to be negligible, so we adopted a simpler relationship of the form M = a’*logτ + b’. Measuring the distances in kilometers and the durations in seconds, we obtained the following set of parameters: a =-3.83+/-0.12 , b =3.03+/- 0.12, c =0.42+/-0.08, a’ = -1.59+/-0.14, b’ =2.06+/-0.08. Moreover, for each station we determined a station correction coefficient, comparing theoretical and observed magnitude values, to improve the accuracy on magnitude estimation. The ISNet data management system is set to automatically detect earthquakes having magnitude greater than 2. A current problem for the automatic detection system is the discrimination of seismic events occurring inside the network from those located outside. Given the dependency of signal duration (τ) and peak-amplitude (P) on source-to-receiver distance, the simultaneous measurement of these two parameters can be effective to identify events inside the seismic network. To this aim, we propose two methodologies, both based on the combined use of τ and P. The former approach is based on the Peak-Duration Magnitude that is defined as Mi = a*logτi + b*logPi + c*log Ri + d where Mi, τ i, and Pi represent the magnitude, the total signal duration, and the peak-amplitude for the i-th station, respectively, and Ri depends on both the i-th station and epicentral coordinates. Parameters a, b, c, and d are determined through a multivariate linear regression analysis. The previous equation can be then used to determine the epicentral coordinates given a set of (τ i, Pi) measurements at the network stations. A rough, but faster method is based on a the definition of a decision-table according to threshold values for τ and P. The basic idea is that (large) earthquakes far away from the network produce small amplitudes and long durations. We then derive two threshold values for τ and P for earthquakes occurring inside the network and having a maximum magnitude of 3. The τ threshold is derived from a relationship linking the duration and magnitude, such as logτ = A + B*M, while the P threshold is derived from a similar relationship relating the peak-amplitude and magnitude, such as logP = A’ + B’*M. The coefficients are determined through a best-fit procedure obtaining A=1.202+/-0.017, B=0.258+/-0.009, A’=2.28+/-0.12, and B’=0.72+/-0.06. Some examples of application of both approaches will be shown