Seismic Waves And Rays In Elastic Media

Author: Michael A. Slawinski
Publisher: Elsevier
ISBN: 9780080439303
Size: 39.88 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 ...

Handbook Of Neural Network Signal Processing

Author: Yu Hen Hu
Publisher: CRC Press
ISBN: 1420038613
Size: 20.50 MB
Format: PDF, Kindle
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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.

Expanded Abstracts With Biographies

Author:
Publisher:
ISBN:
Size: 73.75 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: 53.28 MB
Format: PDF, Kindle
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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.

Monitoring The Comprehensive Nuclear Test Ban Treaty Data Processing And Infrasound

Author: Zoltan A. Der
Publisher: Springer
ISBN: 3034881444
Size: 37.50 MB
Format: PDF, ePub
View: 4505
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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: 10.36 MB
Format: PDF, Mobi
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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 ...

Geophysical Applications Of Artificial Neural Networks And Fuzzy Logic

Author: W. Sandham
Publisher: Springer Science & Business Media
ISBN: 9781402017292
Size: 58.81 MB
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Modern 3-D seismic surveys, with their increased quantities of recorded data,
have only exacerbated this bottleneck in the productivity of processing seismic
data. Previous efforts to automate the picking of first-arrival times, rely on various
models of the refraction process (Hatherly, 1982; Gelchinsky and Shtivelman,
1983; Coppens, 1985; Spagnolini, 1991). Recently, neural networks have been
brought to bear on many pattern recognition problems. Quite naturally then, they
have ...

Proceedings Of The International Conference On Neural Networks

Author: Institute of Electrical and Electronics Engineers
Publisher:
ISBN: 9780780341234
Size: 49.57 MB
Format: PDF, ePub, Docs
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Abstract We train an artificial neural network to perform decon- volution of seismic
data and thereby recognize and remove multiple arrivals in reflection seismic
data. Basis for the learning process is a well log that is typical for the area in
which the data were gathered. Modeling data from this well log and comparing it
to real recorded data allows deduce relations between the subsurface model in
the recorded data. In contrast to conventional geophysical data processing
techniques, ...