Premium
IMPROVEMENT OF MULTICHANNEL SEISMIC DATA THROUGH APPLICATION OF THE MEDIAN CONCEPT 1
Author(s) -
NÆSS O. E.,
BRULAND L.
Publication year - 1989
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.1365-2478.1989.tb02204.x
Subject(s) - weighting , median filter , amplitude , stack (abstract data type) , filter (signal processing) , offset (computer science) , algorithm , stacking , truncated mean , computer science , geology , mathematics , statistics , acoustics , image processing , physics , optics , artificial intelligence , computer vision , nuclear magnetic resonance , estimator , image (mathematics) , programming language
A bstract Different types of median‐based methods can be used to improve multichannel seismic data, particularly at the stacking stage in processing. Different applications of the median concept are described and discussed. The most direct application is the Simple Median Stack (SMS), i.e. to use as output the median value of the input amplitudes at each reflection time. By the Alpha‐Trimmed Mean (ATM) method it is possible to exclude an optional amount of the input amplitudes that differ most from the median value. A more novel use of the median concept is the Weighted Median Stack (WMS). This method is based on a long‐gapped median filter. The implicit weighting, which is purely statistical in nature, is due to the edge effects that occur when the gapped filter is applied. By shifting the traces around before filtering, the maximum weight may be given to, for example, the far‐offset traces. The fourth method is the Iterative Median Stack (IMS). This method, which also includes a strong element of weighting, consists of a repeated use of a gapped median filter combined with a gradual shortening of the filter after each pass. Examples show how the seismic data can benefit from the application of these methods.