Seismic Waves And Rays In Elastic Media

Author: Michael A. Slawinski
Publisher: Elsevier
ISBN: 9780080439303
Size: 69.69 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

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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) ...

Process Neural Networks

Author: Xingui He
Publisher: Springer Science & Business Media
ISBN: 9783540737629
Size: 26.17 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 ...

Handbook Of Neural Network Signal Processing

Author: Yu Hen Hu
Publisher: CRC Press
ISBN: 1420038613
Size: 56.53 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.

Monitoring The Comprehensive Nuclear Test Ban Treaty Data Processing And Infrasound

Author: Zoltan A. Der
Publisher: Springer
ISBN: 3034881444
Size: 52.39 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 ...

Proceedings Of The International Conference On Neural Networks

Author: Institute of Electrical and Electronics Engineers
ISBN: 9780780341234
Size: 64.73 MB
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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, ...

Applied Neural Networks For Signal Processing

Author: Fa-Long Luo
Publisher: Cambridge University Press
ISBN: 9780521644006
Size: 19.97 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 ...

Soft Computing In Machine Learning

Author: Sang-Yong Rhee
Publisher: Springer
ISBN: 331905533X
Size: 74.61 MB
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In the paper “adjacency cube” method for integration of results of few
interpretation algorithms is proposed. Learning algorithm for the “adjacency cube
” with low computational complexity was developed. The proposed method
improves quality of recognition by 2-3 percent. Keywords: Geophysical research
of boreholes, machine learning, artificial neural network, k-NN, uranium deposit,
post-processing data, learning sample, “adjacency cube” method. 1 Introduction
This problem of ...

Neural Networks In Atmospheric Remote Sensing

Author: William J. Blackwell
Publisher: Artech House
ISBN: 1596933739
Size: 69.59 MB
Format: PDF
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We have seen that as the complexity of the neural network is increased by the
addition of nodes or layers there is a concomitant increase in the amount of
computation required for training (or an increase in the amount of time required
for a given computational budget), the amount of training data required, and the
potential for instabilities. A motivation therefore exists to keep the network as
simple as possible (but no simpler, to quote Albert Einstein). Pre- and post-
processing of the ...