Seismic Waves And Rays In Elastic Media

Author: Michael A. Slawinski
Publisher: Elsevier
ISBN: 9780080439303
Size: 39.28 MB
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3d Updated and Revised Edition (B.A. Hardage) ISBN 0-08-043518-1 2001 -
Seismic Signatures and Analysis of Reflection Data in Anisotropic Media (I.
Tsvankin) ISBN 0-08-043649-8 2001 - Computational Neural Networks for
Geophysical Data Processing (M.M. Poulton) ISBN 0-08-043986-1 2001 - Wave
Fields in Real Media: Wave Propagation in Anisotropic, Anelastic and Porous
Media (J.M. Carcione) ISBN 0-08-043929-2 2002 - Multi-Component VSP
Analysis for Applied ...

Expanded Abstracts With Biographies

Author:
Publisher:
ISBN:
Size: 23.30 MB
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Zhang, L., Quirein, J., and Schuelke, J., 2001, “Selforganizing map (SOM) neural
network for classifying seismic traces and picking horizons", in Poulton, M., ed.,
Computational neural networks for geophysical data processing; Chapter l0, in
press. =-*;IL”'" aw Figure 6 — Velocity curycs in color are interpreted to be
completely outside of salt based on the time seismic data. Figure 7 -
Classification map of the velocity curves based the salt-contaminated (red) and
sediment-only (blue) ...

Intelligent Computational Paradigms In Earthquake Engineering

Author: Nikos D. Lagaros
Publisher: IGI Global
ISBN: 1599041014
Size: 62.48 MB
Format: PDF, Mobi
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"This book contains contributions that cover a wide spectrum of very important real-world engineering problems, and explores the implementation of neural networks for the representation of structural responses in earthquake engineering.

Handbook Of Neural Network Signal Processing

Author: Yu Hen Hu
Publisher: CRC Press
ISBN: 1420038613
Size: 68.65 MB
Format: PDF, ePub, Docs
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You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

Process Neural Networks

Author: Xingui He
Publisher: Springer Science & Business Media
ISBN: 9783540737629
Size: 14.11 MB
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6 Feedback Process Neural NetWorks A feedback neural network is an artificial
neural network model that has been widely applied to signal processing ",
optimal computation", convex nonlinear programming", seismic data filtering", etc.
A traditional feedback neural network model generally has time-invariant inputs.
However, when a biological neural organization processes information, it actually
feeds back time-delay information and the inputs of external signals will last for a
period ...

Geophysical Applications Of Artificial Neural Networks And Fuzzy Logic

Author: W. Sandham
Publisher: Springer Science & Business Media
ISBN: 9781402017292
Size: 21.42 MB
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Using methods from statistical pattern recognition, it is possible to determine the
properties of the features input to the neural network that impact upon the
reliability of the picking process. Interpreting the output of the neural network as
the probability that the associated feature is the first-arrival, requires an
appropriate probability model. This model is based on the multinomial
distribution, since it can reflect the fact that there is only one first-arrival event on
each data trace. Optimization ...

Monitoring The Comprehensive Nuclear Test Ban Treaty Data Processing And Infrasound

Author: Zoltan A. Der
Publisher: Springer
ISBN: 3034881444
Size: 36.94 MB
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The correct rateof the initial phasetype identificationin the automated event
bulletin comparedto theanalyst reviewed database rangedfrom 25.6 %to73.4 %.
The PIDC has accumulated millions of seismic phase readings for the current
threecomponent stations, which were not availableat the timeofthe originalneural
network implementation. Therefore, itis worthwhile totrain neural networks for
eachspecific stationto improve the performance of automatic phase identification,
which, inturn ...

Applied Neural Networks For Signal Processing

Author: Fa-Long Luo
Publisher: Cambridge University Press
ISBN: 9780521644006
Size: 50.27 MB
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As a result, the conventional nonparametric methods have found wide use in
advanced radar, sonar, communication, speech, biomedical, geophysical, and
other data processing systems. However, two problems plague nonparametric
spectral estimation methods, namely, high estimation variances and low
resolution, particularly, in the cases that the data are short and the signal-to-noise
ratio is low. As an alternative, parametric spectral estimation methods have been
proposed and ...