Real-Time Estimation of Fault Rupture Extent Using Envelopes of Acceleration

We present a new strategy to estimate the geometry of a rupture on a finite fault in real time for earthquake early warning. We extend the work of Cua and Heaton who developed the virtual seismologist (VS) method (Cua, 2005), which is a Bayesian approach to seismic early warning using envelope attenuation relationships. This article extends the VS method to large earthquakes where fault finiteness is important. We propose a new model to simulate high-frequency motions from earthquakes with large rupture dimension: the envelope of high-frequency ground motion from a large earthquake can be expressed as a root-mean-squared combination of envelope functions from smaller earthquakes. We use simulated envelopes of ground acceleration to estimate the direction and length of a rupture in real time. Using the 1999 Chi-Chi earthquake dataset, we have run simulations with different parameters to discover which parameters best describe the rupture geometry as a function of time. We parameterize the fault geometry with an epicenter, a fault strike, and two along-strike rupture lengths. The simulation results show that the azimuthal angle of the fault line converges to the minimum uniquely, and the estimation agrees with the actual Chi-Chi earthquake fault geometry quite well. The rupture direction can be estimated at 10 s after the event onset, and the final solution is achieved after 20 s. While this methodology seems quite promising for warning systems, it only works well when there is an adequate distribution of near-source stations

Early Warning for Earthquakes with Large Rupture Dimension

Earthquake early warning systems have become popular these days, and many seismologists and engineers are making research efforts for their practical application. The existing earthquake early warning systems provide estimates of the location and size of earthquakes, and then ground motions at a site are estimated as a function of the epicentral distance and site soil properties. However, for large earthquakes, the energy is radiated from a large area surrounding the entire fault plane, and the epicenter indicates only where rupture starts. In this project, we focus on an earthquake early warning system considering fault finiteness. We provide a new methodology to estimate rupture geometry and slip size on a finite fault in real time for the purpose of earthquake early warning. We propose a new model to simulate high-frequency motions from earthquakes with large fault dimension: the envelope of high-frequency ground motion from a large earthquake can be expressed as a root-mean-squared combination of envelope functions from smaller earthquakes. We parameterize the fault geometry with an epicenter, a fault strike, and two along-strike rupture lengths, and find these parameters by minimizing the residual sum of squares of errors between ground motion models and observed ground motion envelopes. To provide the information on the spatial extent of rupture geometry, we present a methodology to estimate a fault dimension of an earthquake in real time by classifying seismic records into near-source or far-source records. We analyze peak ground motions and use Bayesian model class selection to find a function that best classifies near-source and far-source records based on these parameters. This discriminant function is useful to estimate the fault rupture dimension in real time, especially for large earthquakes. In order to characterize slip on the fault in real time, we construct an analytical function to estimate slip on the fault from near-source ground displacement observations. In real-time analysis, we back project the recorded displacement data onto the fault line to estimate the size of the slip on the fault. The simulation results show that the slip size estimation predicts the observed GPS static displacement on the fault quite well. This current slip size on the fault is used for a probabilistic prediction of additional rupture length in the near future. We characterize the distribution of additional rupture length conditioned on the current slip on the fault for the ongoing rupture from the simulation with a 1-D slip model. The probability density of additional rupture length can be approximated by a lognormal distribution conditioned on the current slip size

Bayesian Approach for Identification of Multiple Events in an Early Warning System

The 2011 Tohoku earthquake (M_w 9.0) was followed by a large number of aftershocks that resulted in 70 early warning messages in the first month after the mainshock. Of these warnings, a non‐negligible fraction (63%) were false warnings in which the largest expected seismic intensities were overestimated by at least two intensities or larger. These errors can be largely attributed to multiple concurrent aftershocks from distant origins that occur within a short period of time. Based on a Bayesian formulation that considers the possibility of having more than one event present at any given time, we propose a novel likelihood function suitable for classifying multiple concurrent earthquakes, which uses amplitude information. We use a sequential Monte Carlo heuristic whose complexity grows linearly with the number of events. We further provide a particle filter implementation and empirically verify its performance with the aftershock records after the Tohoku earthquake. The initial case studies suggest promising performance of this method in classifying multiple seismic events that occur closely in time

P-wave picking for earthquake early warning: refinement of a T[pd] method

Detecting P-wave onsets for online processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new T[pd] method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the T[pd] method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The T[pd] method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The T[pd] method can be computed quickly, so it would be suitable for the implementation in earthquake early warning systems

XYtracker: a new approach to estimate fault rupture extent in real time for large earthquakes

We present a methodology for estimating fault geometry and utilizing the distance to the fault for the shaking estimation to improve the accuracy of real-time shaking estimates for large earthquakes. Most of the earthquake early warning system currently estimates the seismic intensity with the ground-motion prediction equations (GMPE) as a function of the hypocenter distance. However, using the fault distance computed from a finite source model can improve the accuracy of the shaking intensity estimation for large earthquakes. This study proposes a novel methodology, XYtracker, to estimate the surface projection of the fault extent and real-time seismic intensity. For large earthquakes, high-frequency ground motions tend to saturate over the magnitude range and strongly correlate with fault distance. As a result, this work can achieve the fault extent using seismic intensity and GMPE. We considered three types of fault models: point-source, line-source, and rectangle-source model. We found the most probable model parameters for each model by minimizing the residual sum of squares between the observed and estimated seismic intensities. The Akaike Information Criterion selected the most probable model among them. The strong motion data set of the 2008 Wenchuan, 2011 Tohoku, and 2016 Kumamoto earthquakes was used to test our methodology. The new method for estimating the fault geometry can obtain the ongoing rupture length and direction using the strong motion data. The model selection scheme with the Akaike Information Criterion selected the finite-source model to explain the shaking distribution. Results revealed that this new approach performed well in estimating the fault dimension. The method can promote the accuracy of the seismic intensity estimation for future large earthquakes, including the subduction earthquakes

Automatic hypocenter determination with the IPFx method for the 2018 Hualien earthquake sequence

The extended integrated particle filter (IPFx) method is an automatic source determination algorithm designed for the Japanese earthquake early warning (EEW) system. The method improved earthquake source determination during active seismicity by incorporating the smart phase association scheme. We applied this method to the 2018 Hualien earthquake sequence and evaluated its performance by comparing it to the manual catalog. We used 1-month continuous waveforms from February 2018 at 170 stations. Owing to the higher noise level, we improved the phase association algorithm to avoid noise contamination. Out of 127 earthquakes with a seismic intensity ≥ 4, 105 were successfully detected in one month, of which 103 had good accuracy with a location error of < 30 km. The detectability of earthquakes decreased immediately following large events. The IPFx method showed good performance in detecting earthquakes with seismic intensity ≥ 4 during the 2018 Hualien earthquake sequence. The method was also applied to the 1-day continuous data on April 18, 2021, and detected 14 earthquakes with a magnitude ~ 2 that were not on the manual catalog. Currently, the Central Weather Bureau in Taiwan uses the effective epicenter method to locate earthquakes for the EEW system. However, source determination for offshore events is difficult as most of the stations are on land. We expect the IPFx method to provide better location estimates for offshore earthquakes and during the period of active seismicity. It also provides an earlier warning as it sends the first message when three stations are triggered. This new method can potentially improve the speed and accuracy of the Taiwanese EEW system

Ocean gravity waves generated by the meteotsunami at the Japan Trench following the 2022 Tonga volcanic eruption

The 2022 eruption of the Hunga Tonga-Hunga Ha'apai volcano excited an atmospheric Lamb wave, which induced a fast-traveling tsunami. This tsunami was driven by the pressure-forced wave traveling at the speed of the Lamb wave and, thus, was much faster than conventional tsunamis. This was the first case in which ocean bottom monitoring systems widely observed an air pressure-induced tsunami. We found that the pressure-forced waves split and generated ocean gravity waves after passing the Japan Trench based on the S-net data. Our simulations show that changes in water depth can amplify or decrease the pressure-forced wave. Simultaneously, an ocean gravity wave is generated due to the conservation of water volume. Because the ocean gravity wave was slower than the pressure-forced wave near Japan, it was separated from, and traveled behind, the pressure-forced wave. We explained the wave separation phenomenon and reproduced the waveforms of different splitting stages observed by the stations near the Japan Trench

Statistical Features of Short-Period and Long-Period Near-Source Ground Motions

This study collects recorded ground motions from the near-source region of large earthquakes and considers to what extent this historic record can inform expectations of future ground motions at similar sites. The distribution of observed peak ground acceleration (PGA) is well approximated by the lognormal distribution, and we expect the observed distribution to remain unchanged with the addition of data from future earthquakes. However, the distribution of peak ground displacements (PGD) will likely change after a well-recorded large earthquake. Specifically we expect future observations of PGD greater than those previously recorded. We use seismic scaling relations to motivate the expected distribution of PGD as uniform on the logarithmic scale, or at least fat-tailed. Because PGA does not scale with fault rupture area or slip on the fault, there are no such scaling relations to predict the observed distribution of PGA. The observed records show that there is essentially no correlation between PGD and PGA for near-source ground motions from large events. The large uncertainty in a future value of PGD in the near-source region of a large earthquake exists despite the ability of Earth scientists to accurately model long-period ground motions. In contrast, the relative certainty in a future value of PGA exists despite the inability to model short-period ground motions reliably. The stability of the observed distribution of PGA with respect to new ground-motion records enables us to predict the distribution of future PGA and to calculate the probability of exceeding the largest recorded PGA

Reply to "Comment on ‘Statistical Features of Short-Period and Long-Period Near-Source Ground Motions’ by Masumi Yamada, Anna H. Olsen, and Thomas H. Heaton" by Roberto Paolucci, Carlo Cauzzi, Ezio Faccioli, Marco Stupazzini, and Manuela Villani

The comment by Paolucci and colleagues (Paolucci et al., 2011) states that a probabilistic seismic hazard analysis (PSHA) can provide "reliable prediction of long-period spectral ordinates." The result of such an analysis would be in contrast to the more uncertain prediction suggested by our empirical, and proposed theoretical, distribution of near-source ground displacements in past, large magnitude earthquakes (Yamada et al., 2009). After addressing two specific concerns of Paolucci and colleagues, we use the balance of this reply to discuss the apparent differences between a PSHA and our observations. These two approaches to understanding the seismic hazard of long-period ground motions should be consistent even though they view the problem from different perspectives