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Modelling of local velocity anomalies: a cookbook
Author(s) -
Juliard Corinne,
Thore Pierre D.
Publication year - 1999
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 - 10.1046/j.1365-2478.1999.00130.x
Subject(s) - geology , a priori and a posteriori , anomaly (physics) , inversion (geology) , ambiguity , synthetic data , algorithm , range (aeronautics) , set (abstract data type) , data set , regional geology , data processing , computer science , geophysics , seismology , artificial intelligence , physics , volcanism , philosophy , materials science , epistemology , tectonics , programming language , operating system , composite material , condensed matter physics
The determination of small‐scale velocity anomalies (from tens to a few hundreds of metres) is a major problem in seismic exploration. The impact of such anomalies on a structural interpretation can be dramatic and conventional techniques such as tomographic inversion or migration velocity analysis are powerless to resolve the ambiguity between structural and velocity origins of anomalies. We propose an alternative approach based on stochastic modelling of numerous anomalies until a set of models is found which can explain the real data. This technique attempts to include as much a priori geological information as possible. It aims at providing the interpreter with a set of velocity anomalies which could possibly be responsible for the structural response. The interpreter can then choose one or several preferred models and pursue a more sophisticated analysis. The class of retained models are all equivalent in terms of data and therefore represent the uncertainty in the model space. The procedure emulates the real processing sequence using a simplified scheme. Essentially, the technique consists of five steps: 1 Interpretation of a structural anomaly in terms of a velocity anomaly with its possible variations in terms of position, size and amplitude. 2 Drawing a model by choosing the parameters of the anomaly within the acceptable range. 3 Modelling the traveltimes in this model and producing the imaging of the reflected interface. 4 Comparing the synthetic data with the real data and keeping the model if it lies within the data uncertainty range. 5 Iterate from step 2. In order to avoid the high computational cost inherent in using statistical determinations, simplistic assumptions have been made: • The anomaly is embedded in a homogeneous medium: we assume that the refraction and the time shift due to the anomaly have a first‐order effect compared with ray bending in the intermediate layers. • We model only the zero‐offset rays and therefore we restrict ourselves to structural problems. • We simulate time migration and so address only models of limited structural complexity. These approximations are justified in a synthetic model which includes strong lateral velocity variations, by comparing the result of a full processing sequence (prestack modelling, stack and depth migration) with the simplified processing. This model is then used in a blind test on the inversion scheme.