Article records
https://feeds.library.caltech.edu/people/Yamada-Masumi/article.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 14:33:00 +0000Real-time estimation of fault rupture extent using near-source versus far-source classification
https://resolver.caltech.edu/CaltechAUTHORS:YAMbssa07
Authors: {'items': [{'id': 'Yamada-Masumi', 'name': {'family': 'Yamada', 'given': 'Masumi'}}, {'id': 'Heaton-T-H', 'name': {'family': 'Heaton', 'given': 'Thomas'}, 'orcid': '0000-0003-3363-2197'}, {'id': 'Beck-J-L', 'name': {'family': 'Beck', 'given': 'James'}}]}
Year: 2007
DOI: 10.1785/0120060243
To estimate the fault dimension of an earthquake in real time, we present a methodology to classify seismic records into near-source or far-source records. Characteristics of ground motion, such as peak ground acceleration, have a strong correlation with the distance from a fault rupture for large earthquakes. This study analyzes peak ground motions and finds the function that best classifies near-source and far-source records based on these parameters. We perform (1) Fisher's linear discriminant analysis and two different Bayesian methods to find the coefficients
of the linear discriminant function and (2) Bayesian model class selection to find the best combination of the peak ground-motion parameters. Bayesian model class selection shows that the combination of vertical acceleration and horizontal velocity produces the best performance for the classification. The linear discriminant function produced by the three methods classifies near-source and far-source data, and in addition, the Bayesian methods give the probability for a station to be near-source, based on the ground-motion measurements. This discriminant function is useful to estimate the fault rupture dimension in real time, especially for large earthquakes.https://authors.library.caltech.edu/records/0yeda-fsv76Real-Time Estimation of Fault Rupture Extent Using Envelopes of Acceleration
https://resolver.caltech.edu/CaltechAUTHORS:YAMbssa08
Authors: {'items': [{'id': 'Yamada-Masumi', 'name': {'family': 'Yamada', 'given': 'Masumi'}}, {'id': 'Heaton-T-H', 'name': {'family': 'Heaton', 'given': 'Thomas'}, 'orcid': '0000-0003-3363-2197'}]}
Year: 2008
DOI: 10.1785/0120060218
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.https://authors.library.caltech.edu/records/s2c46-09961Bayesian Learning Using Automatic Relevance Determination Prior with an Application to Earthquake Early Warning
https://resolver.caltech.edu/CaltechAUTHORS:OHCjem08
Authors: {'items': [{'id': 'Oh-Chang-Kook', 'name': {'family': 'Oh', 'given': 'Chang Kook'}}, {'id': 'Beck-J-L', 'name': {'family': 'Beck', 'given': 'James L.'}}, {'id': 'Yamada-Masumi', 'name': {'family': 'Yamada', 'given': 'Masumi'}}]}
Year: 2008
DOI: 10.1061/(ASCE)0733-9399(2008)134:12(1013)
A novel method of Bayesian learning with automatic relevance determination prior is presented that provides a powerful approach to problems of classification based on data features, for example, classifying soil liquefaction potential based on soil and seismic shaking parameters, automatically classifying the damage states of a structure after severe loading based on features of its dynamic response, and real-time classification of earthquakes based on seismic signals. After introduction of the theory, the method is illustrated by applying it to an earthquake record dataset from nine earthquakes to build an efficient real-time algorithm for near-source versus far-source classification of incoming seismic ground motion signals. This classification is needed in the development of early warning systems for large earthquakes. It is shown that the proposed methodology is promising since it provides a classifier with higher correct classification rates and better generalization performance than a previous Bayesian learning method with a fixed prior distribution that was applied to the same classification problem.https://authors.library.caltech.edu/records/3chyy-ye680Bayesian Learning Using Automatic Relevance Determination Prior with an Application to Earthquake Early Warning
https://resolver.caltech.edu/CaltechAUTHORS:OHCjem08
Authors: {'items': [{'id': 'Oh-Chang-Kook', 'name': {'family': 'Oh', 'given': 'Chang Kook'}}, {'id': 'Beck-J-L', 'name': {'family': 'Beck', 'given': 'James L.'}}, {'id': 'Yamada-Masumi', 'name': {'family': 'Yamada', 'given': 'Masumi'}}]}
Year: 2008
DOI: 10.1061/(ASCE)0733-9399(2008)134:12(1013)
A novel method of Bayesian learning with automatic relevance determination prior is presented that provides a powerful approach to problems of classification based on data features, for example, classifying soil liquefaction potential based on soil and seismic shaking parameters, automatically classifying the damage states of a structure after severe loading based on features of its dynamic response, and real-time classification of earthquakes based on seismic signals. After introduction of the theory, the method is illustrated by applying it to an earthquake record dataset from nine earthquakes to build an efficient real-time algorithm for near-source versus far-source classification of incoming seismic ground motion signals. This classification is needed in the development of early warning systems for large earthquakes. It is shown that the proposed methodology is promising since it provides a classifier with higher correct classification rates and better generalization performance than a previous Bayesian learning method with a fixed prior distribution that was applied to the same classification problem.https://authors.library.caltech.edu/records/fhhbc-gxr28The Slapdown Phase in High-acceleration Records of Large Earthquakes
https://resolver.caltech.edu/CaltechAUTHORS:20091030-105027137
Authors: {'items': [{'id': 'Yamada-Masumi', 'name': {'family': 'Yamada', 'given': 'Masumi'}}, {'id': 'Mori-Jim', 'name': {'family': 'Mori', 'given': 'Jim'}}, {'id': 'Heaton-T-H', 'name': {'family': 'Heaton', 'given': 'Thomas'}, 'orcid': '0000-0003-3363-2197'}]}
Year: 2009
DOI: 10.1785/gssrl.80.4.559
The 2008 Iwate-Miyagi Nairiku earthquake (M_w 6.9, M_(jma) 7.2) produced strong shaking throughout northern Honshu, Japan, with severe damage to buildings and extensive landslides. The shallow event occurred in southwestern Iwate Prefecture (39.03°N, 140.88°E, depth 8 km) on 13 June 2008 at 23:43:45 GMT (Japan Meteorological Agency 2008). This earthquake produced relatively high-frequency ground motions, which resulted in large values of peak ground acceleration (PGA). The surface accelerometer of the station IWTH25 of KiK-net, located 3 km southwest of the epicenter, produced one of the largest strong-motion values of PGA (4,278 cm/s^2 for the vector sum of the three components) ever recorded (http://www.kik.bosai.go.jp/kik/index_en.shtml).
The new accelerometers installed in KiK-net last year have a recording range up to 4,000 cm/s^2, which made it possible to record such large ground motions near the source (http://www.kik.bosai.go.jp/kik/index_en.shtml). The sampling rate of the record of IWTH25 is 100 Hz (http://www.kik.bosai.go.jp/kik/index_en.shtml).
The surface acceleration record at station IWTH25 shows an asymmetric amplification in the vertical components (Aoi et al. 2008). The upward vertical acceleration is much larger than the downward direction, although in the borehole record at a depth of 260 m at the same site, the upward and downward accelerations have symmetric amplitudes (Figure 1). On the other hand, the horizontal components do not show this asymmetric effect. This difference between the surface and borehole recordings for the vertical component implies a strong nonlinear amplification. In this paper, we will analyze these records and propose a mechanism to produce the large vertical accelerations. The predominance of large upward acceleration spikes is not unique to the Iwate-Miyagi Nairiku earthquake, so our proposed mechanism may be applicable to a number of large vertical acceleration records.https://authors.library.caltech.edu/records/5wn39-42g71Statistical Features of Short-Period and Long-Period Near-Source Ground Motions
https://resolver.caltech.edu/CaltechAUTHORS:20091208-111331472
Authors: {'items': [{'id': 'Yamada-Masumi', 'name': {'family': 'Yamada', 'given': 'Masumi'}}, {'id': 'Olsen-A-H', 'name': {'family': 'Olsen', 'given': 'Anna H.'}}, {'id': 'Heaton-T-H', 'name': {'family': 'Heaton', 'given': 'Thomas H.'}, 'orcid': '0000-0003-3363-2197'}]}
Year: 2009
DOI: 10.1785/0120090067
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.https://authors.library.caltech.edu/records/3njgn-kzn36Reply 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
https://resolver.caltech.edu/CaltechAUTHORS:20110414-092400175
Authors: {'items': [{'id': 'Yamada-Masumi', 'name': {'family': 'Yamada', 'given': 'Masumi'}}, {'id': 'Olsen-A-H', 'name': {'family': 'Olsen', 'given': 'Anna H.'}}, {'id': 'Heaton-T-H', 'name': {'family': 'Heaton', 'given': 'Thomas H.'}, 'orcid': '0000-0003-3363-2197'}]}
Year: 2011
DOI: 10.1785/0120100210
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.https://authors.library.caltech.edu/records/hgd8j-2yq06Bayesian Approach for Identification of Multiple Events in an Early Warning System
https://resolver.caltech.edu/CaltechAUTHORS:20140731-082120109
Authors: {'items': [{'id': 'Liu-Annie-H', 'name': {'family': 'Liu', 'given': 'Annie'}}, {'id': 'Yamada-Masumi', 'name': {'family': 'Yamada', 'given': 'Masumi'}}]}
Year: 2014
DOI: 10.1785/0120130208
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.https://authors.library.caltech.edu/records/8p2b5-vn123Multi-events earthquake early warning algorithm using a Bayesian approach
https://resolver.caltech.edu/CaltechAUTHORS:20150326-084342494
Authors: {'items': [{'id': 'Wu-S', 'name': {'family': 'Wu', 'given': 'S.'}}, {'id': 'Yamada-Masumi', 'name': {'family': 'Yamada', 'given': 'M.'}}, {'id': 'Tamaribuchi-K', 'name': {'family': 'Tamaribuchi', 'given': 'K.'}}, {'id': 'Beck-J-L', 'name': {'family': 'Beck', 'given': 'J. L.'}}]}
Year: 2015
DOI: 10.1093/gji/ggu437
Current earthquake early warning (EEW) systems lack the ability to appropriately handle multiple concurrent earthquakes, which led to many false alarms during the 2011 Tohoku earthquake sequence in Japan. This paper uses a Bayesian probabilistic approach to handle multiple concurrent events for EEW. We implement the theory using a two-step algorithm. First, an efficient approximate Bayesian model class selection scheme is used to estimate the number of concurrent events. Then, the Rao-Blackwellized Importance Sampling method with a sequential proposal probability density function is used to estimate the earthquake parameters, that is hypocentre location, origin time, magnitude and local seismic intensity. A real data example based on 2 months data (2011 March 9–April 30) around the time of the 2011 M9 Tohoku earthquake is studied to verify the proposed algorithm. Our algorithm results in over 90 per cent reduction in the number of incorrect warnings compared to the existing EEW system operating in Japan.https://authors.library.caltech.edu/records/0cdjs-00z18