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PreSEIS

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Journal Article
Published: 01 September 2009
Seismological Research Letters (2009) 80 (5): 748–754.
...Nina Köhler; Georgia Cua; Friedemann Wenzel; Maren Böse © 2009 by the Seismological Society of America 2009 PreSEIS (Pre-SEISmic shaking) is a neural network-based approach to EEW that takes advantage of both regional and on-site early warning ( Böse 2006 ; Böse et al. 2008...
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Journal Article
Published: 01 February 2008
Bulletin of the Seismological Society of America (2008) 98 (1): 366–382.
...Maren Böse; Friedemann Wenzel; Mustafa Erdik Abstract The major challenge in the development of earthquake early warning ( EEW ) systems is the achievement of a robust performance at largest possible warning time. We have developed a new method for EEW —called PreSEIS (Pre-SEISmic)—that is as quick...
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Image
(a) Comparison of the results obtained from <b>PreSEIS</b> On‐site and the  τ   c ...
Published: 01 April 2012
Figure 7. (a) Comparison of the results obtained from PreSEIS On‐site and the τ c algorithm. (b) Similar trends in the error distributions for both algorithms indicate some general nature of the predictability of earthquake ruptures and magnitudes using a limited time window of seismic data
Image
Principal approach of <b>PreSEIS</b> On‐site. (a) The algorithm uses the logarithm...
Published: 01 April 2012
Figure 1. Principal approach of PreSEIS On‐site. (a) The algorithm uses the logarithmic values of the integrated absolute amplitudes of acceleration, velocity, and displacement waveform time series, , , and u ( t  ), at a single sensor, as well as V S 30 site characterization. Outputs
Image
Principal approach of <b>PreSEIS</b> On‐site. (a) The algorithm uses the logarithm...
Published: 01 April 2012
Figure 1. Principal approach of PreSEIS On‐site. (a) The algorithm uses the logarithmic values of the integrated absolute amplitudes of acceleration, velocity, and displacement waveform time series, , , and u ( t  ), at a single sensor, as well as V S 30 site characterization. Outputs
Image
Principal approach of <b>PreSEIS</b> On‐site. (a) The algorithm uses the logarithm...
Published: 01 April 2012
Figure 1. Principal approach of PreSEIS On‐site. (a) The algorithm uses the logarithmic values of the integrated absolute amplitudes of acceleration, velocity, and displacement waveform time series, , , and u ( t  ), at a single sensor, as well as V S 30 site characterization. Outputs
Journal Article
Published: 01 April 2012
Bulletin of the Seismological Society of America (2012) 102 (2): 738–750.
...Figure 7. (a) Comparison of the results obtained from PreSEIS On‐site and the τ c algorithm. (b) Similar trends in the error distributions for both algorithms indicate some general nature of the predictability of earthquake ruptures and magnitudes using a limited time window of seismic data...
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Demonstration of <b>PreSEIS</b> On‐site for three earthquakes from the test datase...
Published: 01 April 2012
Figure 6. Demonstration of PreSEIS On‐site for three earthquakes from the test dataset: (a) the 2010 M  4.1 Redlands and (b) the 2010 M  5.4 Collins Valley earthquakes in southern California, and (c) the 2008 M  6.9 Miyagi earthquake in Japan. For each earthquake, we show the results at two
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(a) Scheme of a two-layer feed-forward ( TLFF ) neural network composed of ...
Published: 01 February 2008
patterns. (b) PreSEIS applies three TLFF networks for the estimation of seismic source parameters. The first network uses the information on the time differences Δτ between the P -wave arrivals at the different sensors to estimate the location of the earthquake hypocenter. The second uses the CAV
Image
Shake (a) and alert maps (b) for the two scenario earthquakes in Figure  5 ...
Published: 01 February 2008
. The fronts of P and S waves are indicated by solid and dashed lines; the true and estimated epicenter locations are marked by stars and circles. PreSEIS initially underestimates the MMI levels of both scenarios by approximately one intensity unit, but exhibits a fast convergence towards the target
Image
Shake (a) and alert maps (b) for the two scenario earthquakes in Figure  5 ...
Published: 01 February 2008
. The fronts of P and S waves are indicated by solid and dashed lines; the true and estimated epicenter locations are marked by stars and circles. PreSEIS initially underestimates the MMI levels of both scenarios by approximately one intensity unit, but exhibits a fast convergence towards the target
Journal Article
Published: 01 September 2009
Seismological Research Letters (2009) 80 (5): 682–693.
... dependence is introduced by the combination of acceleration, velocity, and displacement. There is therefore a similarity between this amplitude-based approach and the frequency-based methodologies described above. The neural-network based PreSEIS approach makes use either of the cumulative absolute velocity...
FIGURES
Journal Article
Published: 01 January 2008
Seismological Research Letters (2008) 79 (1): 145–146.
.... A. Hashash Calibrating Median and Uncertainty Estimates for a Practical Use of Empirical Green's Functions Technique 
 Mathieu Causse, Fabrice Cotton, Cécile Cornou, and Pierre-Yves Bard Spatial Correlation of Peak Ground Motions and Response Spectra 
 K. Goda and H. P. Hong PreSEIS: A Neural...
Journal Article
Published: 01 May 2011
Seismological Research Letters (2011) 82 (3): 394–403.
... ). PreSEIS: A neural network-based approach to earthquake early warning for finite faults . Bulletin of the Seismological Society of America 98 , 366 – 382 . Brown , H. , R. M. Allen , and V. F. Grasso ( 2009 ). Testing ElarmS in Japan . Seismological Research Letters 80 ( 5...
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Journal Article
Published: 05 January 2016
Bulletin of the Seismological Society of America (2016) 106 (1): 1–12.
... ). PreSEIS: A neural networkbased approach to earthquake early warning for finite faults , Bull. Seismol. Soc. Am. 98 , no.  1 , 366–382 , doi: 10.1785/0120070002 . Brown H. Allen R. M. Grasso V. F. ( 2009 ). Testing ElarmS in Japan , Seismol. Res. Lett. 80 , no.  5 , 727–739...
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Journal Article
Published: 19 December 2018
Seismological Research Letters (2019) 90 (2A): 510–516.
... – 166 . Böse M. Wenzel F. , and Erdik M. 2008 . PreSEIS: A neural network‐based approach to earthquake early warning for finite faults , Bull. Seismol. Soc. Am. 98 , no.  1 , 366 – 382 . Del Pezzo E. Esposito A. Giudicepietro F. Marinaro M. Martini M...
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Journal Article
Published: 31 May 2017
Seismological Research Letters (2017) 88 (4): 1089–1096.
....2006.1819 . Bishop C. 1995 . Neural Networks for Pattern Recognition , Oxford University Press , New York, New York , 500  pp. Böse M. Wenzel F. , and Erdik M. 2008 . PreSEIS: A neural network-based approach to earthquake early warning for finite faults...
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Journal Article
Published: 14 November 2018
Seismological Research Letters (2019) 90 (1): 3–14.
.... , and Hauksson E. 2012 . Real‐time finite fault rupture detector (FinDer) for large earthquakes , Geophys. J. Int. 191 , no.  2 , 803 – 812 . Böse M. Wenzel F. , and Erdik M. 2008 . PreSEIS: A neural network‐based approach to earthquake early warning for finite faults , Bull...
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Journal Article
Journal: Interpretation
Published: 24 January 2018
Interpretation (2018) 6 (1): SB77–SB97.
.../s rate and a Preseis (Geomega) digital recording system. The high-frequency boomer source allows the acquisition of ultra-high-resolution seismic images (0.1 m scale) and a penetration of several tens of meters into the subsurface. Navigation and positioning were done using a Trimble PRO XRS GPS...
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Journal Article
Published: 06 March 2019
Seismological Research Letters (2019) 90 (3): 1377–1392.
.../2008GL036366 . Böse M. Ionescu C. , and Wenzel F. 2007 . Earthquake early warning for Bucharest, Romania: Novel and revised scaling relations , Geophys. Res. Lett. 34 , no.  7 , L07302 , doi: 10.1029/2007GL029396 . Böse M. Wenzel F. , and Erdik M. 2008 . PreSEIS...
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