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| Titel |
Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach |
| VerfasserIn |
S. Maiti, G. Gupta, V. C. Erram, R. K. Tiwari |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 18, no. 2 ; Nr. 18, no. 2 (2011-03-09), S.179-192 |
| Datensatznummer |
250013893
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| Publikation (Nr.) |
 copernicus.org/npg-18-179-2011.pdf |
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| Zusammenfassung |
| Koyna region is well-known for its triggered seismic activities since the
hazardous earthquake of M=6.3 occurred around the Koyna reservoir on
10 December 1967. Understanding the shallow distribution of resistivity
pattern in such a seismically critical area is vital for mapping faults,
fractures and lineaments. However, deducing true resistivity distribution
from the apparent resistivity data lacks precise information due to intrinsic
non-linearity in the data structures. Here we present a new technique based
on the Bayesian neural network (BNN) theory using the concept of Hybrid Monte
Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulation scheme. The new method
is applied to invert one and two-dimensional Direct Current (DC) vertical
electrical sounding (VES) data acquired around the Koyna region in India.
Prior to apply the method on actual resistivity data, the new method was
tested for simulating synthetic signal. In this approach the objective/cost
function is optimized following the Hybrid Monte Carlo (HMC)/Markov Chain
Monte Carlo (MCMC) sampling based algorithm and each trajectory was updated
by approximating the Hamiltonian differential equations through a leapfrog
discretization scheme. The stability of the new inversion technique was
tested in presence of correlated red noise and uncertainty of the result was
estimated using the BNN code. The estimated true resistivity distribution was
compared with the results of singular value decomposition (SVD)-based
conventional resistivity inversion results. Comparative results based on the
HMC-based Bayesian Neural Network are in good agreement with the existing
model results, however in some cases, it also provides more detail and
precise results, which appears to be justified with local geological and
structural details. The new BNN approach based on HMC is faster and proved to
be a promising inversion scheme to interpret complex and non-linear
resistivity problems. The HMC-based BNN results are quite useful for the
interpretation of fractures and lineaments in seismically active region. |
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