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Volume 33 Number 1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The geophysical data processor today has on offer a great variety of tools for the inversion of seismic reflection data to estimate geological structure. The major subset of these comprises migration procedures, which span a wide range of sophistication and cost in terms both of computation time and manual effort on the part of interpreters and processing staff. The choice of an over‐powerful process can be very wasteful, but on the other hand too naive a migration procedure can lead to wrong interpretations which are much more costly still.

Complete inversion procedures which aim to delineate all changes in rock densities and elastic properties in the subsurface are still in the imaginative stages of research. Not even the most sophisticated migration procedure in current use with real data, however, provides a complete inversion, but all depend in some measure on prior knowledge of the velocity structure of the section of the earth traversed by the seismic energy. Such knowledge may be very approximate at first, but each inversion should, through the skill of the interpreter, allow him to revise his velocity model and, up to some limit imposed by the quality and ambiguity of the original data, to improve the next inversion. Paradoxically, he can often be helped by using forward modeling procedures to check the implications of his ideas in the data domain, both in deciding how to update the velocity model and in selecting the most appropriate migration process to use next.

We review here the currently available toolkit of migration and modeling processes and make suggestions as to how each process can fit into a learning strategy which can improve the interpretation as economically as possible and in as many iterative steps as the complexity of the earth's velocity structure makes necessary. An example is shown of the strategy being used in a complex overthrust region.

The authors wish to thank the Chairman and Board of Directors of BP Exploration Co. for permission to publish this paper, and also make acknowledgment to our colleagues whose labours in research and development have made available to our use many of the essential tools required to implement the strategies we describe.

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2006-04-27
2024-04-19

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