Premium
Vibroseis productivity: shake and go
Author(s) -
Krohn Christine,
Johnson Marvin,
Ho Rachel,
Norris Michael
Publication year - 2010
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.1365-2478.2009.00836.x
Subject(s) - seismic vibrator , computer science , sampling (signal processing) , noise (video) , shake , offset (computer science) , acoustics , geology , seismology , engineering , telecommunications , artificial intelligence , physics , mechanical engineering , detector , image (mathematics) , programming language
We use both model and field data to compare three methods for increasing vibroseis productivity and decreasing acquisition costs. The first method, HFVS (high‐fidelity vibratory seismic), allows us to separate the responses from individual vibrators when multiple vibrators are operating simultaneously. The data quality of the separated records is superior to that of conventional correlated data because they are processed with measured ground‐force signals, but the number of sweeps must be greater than or equal to the number of vibrators. The second method, cascaded sweep, eliminates the listening time between multiple sweeps and partially mitigates harmonic noise observed at later times on near‐offset traces. Finally, a combined method, continuous‐HFVS (C‐HFVS), allows source separation with a single, long, segmented sweep. Separation is as good as with HFVS and interference noise is limited to times near the end of a sweep‐segment length. All three methods produce acceptable seismic images for post‐stack and prestack amplitude interpretation. The choice of which option to use depends upon the area being investigated. HFVS has numerous benefits, especially when fine sampling is required to mitigate static problems and elevation changes. Due to the ability to separate individual responses, fine sampling can be achieved without sacrificing productivity. For deeper targets, cascaded sweep can be more efficient but data quality suffers from harmonic noise. C‐HFVS, which combines features of HFVS and cascaded sweep, has the potential to result in the highest productivity, without sacrificing either fine sampling or data quality.