Painting the near surface using geology, geophysics, and satellites

Abstract

A smooth interpolation of a sparse uphole survey may be the main tool available for modeling the near surface in desert areas. An uphole survey does not properly define sharp, nearsurface velocity changes associated with lithologic and topographic variations such as cliffs, canyons, or outcrops, which may be delineated by satellite imagery instead. We present a patchy interpolation technique to build a consistent near-surface model. The algorithm is based on K-means clustering to integrate geological, geophysical, and remote-sensing information. A somewhat arbitrary aspect of the method is the choice of how many clusters are used in segregating the data. Linking this parameter to surface geologic formations may be inadequate because P-or S-wave velocities are unlikely to have a sufficiently unique correlation with geologic age and lithology. We suggest an empirical criterion: improvement in the seismic stack section achieved by processing with parameters derived by various clustering choices and data-type combinations. This approach was tested using field data from Saudi Arabia.

Recent Technologies for Land Seismic Imaging Over a Complex Near Surface Overburden

“…This may introduce some inconsistency in the related data and may explain part of the noisy appearance of the row data in Figure 1. Such a drawback is avoided when it comes to another kind of auxiliary data for near surface seismic processing, i.e., satellite imagery (Vesnaver et al 2009). …”

“…Indeed, this approach overcomes only partially the problem affecting refraction statics, i.e., the so-called Љhidden layer(s)Љ, while it works optimally when lateral and vertical velocity variations are mild. When lateral changes are sharp instead, other recent methods can be used and are reviewed in this paper: for example, the integration of remote sensing in the near-surface modelling (Vesnaver et al 2009), and the use of geostatistics (co-kriging) for extracting information from the Vibroseis response, such as the stiffness and viscosity of the ground (Ley et al 2006).…”

On the use of microtremors for hydrocarbon detection

“…Plots like this one may be used for near surface characterization and modelling, eventually in conjunction with other geophysical measurements via clustering (Vesnaver et al . ), to improve static corrections and imaging.…”

“…Factors such as wind and air waves are strongly attenuated by such a deployment, so most signal energy is due to surface waves. Plots like this one may be used for near surface characterization and modelling, eventually in conjunction with other geophysical measurements via clustering (Vesnaver et al 2009), to improve static corrections and imaging.…”