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RESIDUAL STATICS ESTIMATION BY STACK‐POWER MAXIMIZATION IN THE FREQUENCY DOMAIN 1
Author(s) -
NØRMARK E.
Publication year - 1993
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.1365-2478.1993.tb00870.x
Subject(s) - statics , residual , frequency domain , computer science , monte carlo method , simulated annealing , stack (abstract data type) , noise (video) , mathematical optimization , algorithm , mathematics , physics , statistics , classical mechanics , artificial intelligence , image (mathematics) , programming language , computer vision
A bstract Traditionally, residual static corrections are based on timeshifts estimated for individual CMP sorted traces, which are later resolved into surface‐consistent statics. This is a stable and attractive procedure because the data flow is simple and the memory storage required is limited. An alternative station‐oriented method maximizing the stack‐power estimates surface‐consistent static corrections directly. The statics evaluation in this method involves several CMP gathers, which should improve the prediction of statics on noise‐contaminated data. In this paper the performance of the above methods will be compared using synthetic as well as real seismic data. Neither method is capable of estimating large statics compared to the dominating period, because local optimization might fail. Global Monte Carlo search by, for instance, simulated annealing has been used to overcome the cycle‐skipping problems when proper field statics are missing. Although this procedure is computationally very heavy, it may be the only way to deal with large residual statics. In order to enlarge the operational field for local optimization, it is suggested that the stack‐power in the frequency domain is maximized. This makes it easy to change the frequency band during the optimization. Making use of the frequency domain will also normally be faster than the traditional time‐domain optimization even for a limited number of iterations. Moreover, the main memory storage required can be significantly reduced, since it is only necessary to keep the frequency band in the memory, where the signal‐to‐noise ratio is good.