Reliably estimating near-surface P-wave (longitudinal or compression wave) velocity is one of the key problems in land seismic exploration. Conventionally, uphole surveys have been used to acquire this near-surface velocity information. However, in the presence of rapidly varying near-surface geology, this method of estimation is inherently uncertain due to the sparse uphole sampling grid.
Other conventional indirect methods to determine the near surface layers velocity model have included refraction and shallow reflection surveys. These indirect measurements for near surface properties determination are conducted separately from the surveys directed towards imaging deep layers. Such refraction and shallow reflection surveys normally require further constraining factors to validate their results, and data from upholes has therefore been used for this extra validation.
Correspondingly, data from refraction and shallow reflection surveys has been used to interpolate between sparsely located upholes for improving the near surface velocity model. The velocity of the near surface layer is estimated from the direct arrivals from these two techniques. Sometimes other kinds of data are used for interpolation, such as ground penetrating radar (GPR) or resistivity. All these techniques are good aids for interpolation between upholes, but they add an extra cost component to the survey.
A good knowledge of near-surface velocity macro model is vital for hydrocarbon reservoir exploration and characterization that utilize seismic data. This model is crucial for statics and depthing. However, the complexities of near surface layers make its determination, using direct measurements via uphole surveys, economically prohibitive. The problem that geophysicists face is how to interpolate between sparse near-surface velocity well controls knowing the fact that near-surface velocity varies laterally with lithology that does not equally vary in all directions. Therefore, techniques are needed to estimate near-surface velocity using available data in order to minimize the associated risk resulting from an incomplete knowledge of the near-surface velocity model.