Correction for water depth variations of marine crustal reflection seismic data using refraction statics

1992 ◽  
Vol 14 (1) ◽  
pp. 65-76 ◽  
Author(s):  
C. Samson ◽  
G. F. West
Keyword(s):  
Water Depth ◽  
Seismic Data ◽  
Reflection Seismic ◽  
Crustal Reflection

Time-frequency analysis of deep crustal reflection seismic data using Wigner-Ville distributions

2001 ◽  
Vol 38 (7) ◽  
pp. 1027-1035 ◽  
Author(s):  
Kris Vasudevan ◽  
Frederick A Cook
Keyword(s):  
Frequency Analysis ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Drill Hole ◽  
Lateral Variation ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Reflection Seismic ◽  
Instantaneous Frequencies ◽  
Crustal Reflection

One important component of deep crustal reflection seismic data in the absence of drill-hole data and surface-outcrop constraints is classifying and quantifying reflectivity patterns. One approach to this component uses a recently developed data-decomposition technique, seismic skeletonization. Skeletonized coherent events and their attributes are identified and stored in a relational database, allowing easy visualization and parameterization of the reflected wavefield. Because one useful attribute, the instantaneous frequency, is difficult to derive within the current framework of skeletonization, time–frequency analysis and a new method, empirical mode skeletonization, are used to derive it. Other attributes related to time–frequency analysis that can be derived from the methods can be used for shallow and deep reflection seismic interpretation and can supplement the seismic attributes accrued from seismic skeletonization. Bright reflections observed from below the sedimentary basin in the Southern Alberta Lithosphere Transect have recently been interpreted to be caused by highly reflective sills. Time–frequency analysis of one of these reflections shows the lateral variation of energy with instantaneous frequency for any given time and the lateral variation of energy with time for any instantaneous frequency. Results from empirical mode skeletonization for the same segment of data illustrate the differences in the instantaneous frequencies among the intrinsic modes of the data. Thus, time–frequency distribution of amplitude or energy for any signal may be a good indicator of compositional differences that can vary from one location to another.

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