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Path‐integral seismic imaging
Author(s) -
Landa E.,
Fomel S.,
Moser T.J.
Publication year - 2006
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/j.1365-2478.2006.00552.x
Subject(s) - geophysical imaging , weighting , path integral formulation , path (computing) , stacking , offset (computer science) , algorithm , inversion (geology) , computer science , mathematics , mathematical analysis , geology , geophysics , physics , seismology , quantum , nuclear magnetic resonance , quantum mechanics , acoustics , tectonics , programming language
A new type of seismic imaging, based on Feynman path integrals for waveform modelling, is capable of producing accurate subsurface images without any need for a reference velocity model. Instead of the usual optimization for traveltime curves with maximal signal semblance, a weighted summation over all representative curves avoids the need for velocity analysis, with its common difficulties of subjective and time‐consuming manual picking. The summation over all curves includes the stationary one that plays a preferential role in classical imaging schemes, but also multiple stationary curves when they exist. Moreover, the weighted summation over all curves also accounts for non‐uniqueness and uncertainty in the stacking/migration velocities. The path‐integral imaging can be applied to stacking to zero‐offset and to time and depth migration. In all these cases, a properly defined weighting function plays a vital role: to emphasize contributions from traveltime curves close to the optimal one and to suppress contributions from unrealistic curves. The path‐integral method is an authentic macromodel‐independent technique in the sense that there is strictly no parameter optimization or estimation involved. Development is still in its initial stage, and several conceptual and implementation issues are yet to be solved. However, application to synthetic and real data examples shows that it has the potential for becoming a fully automatic imaging technique.

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