Near Surface Seismic Reflection Imaging: Great Potential Under Critical Eye

Wednesday, 17 December 2014
Richard D Miller, Shelby Peterie and Brett E Judy, Kansas Geological Survey, Lawrence, KS, United States
Seismic-reflection imaging has long been a mainstay in the oil and gas exploration community with mind boggling advancements in just the last decade, but its application to engineering, environmental, and groundwater problems has not seen the same level of utilization. A great deal of the problem lies in the many assumptions that are valid for deep exploration that are violated in the very complex near surface. Large channel systems with acquisition geometries conducive for both deep and shallow targets are many times assumed to be capable of extending the imaging depth window. In reality, constraints of the source and sensor/recording systems must be considered, where large powerful sources are needed to image exploration depths while low-energy, high-frequency sources are required for the shallow and thin targets in the near surface. Attempts to make one size fit all will result in artifacts that result in bogus images and characterizations in the shallow subsurface.

Narrow optimum offsets, highly attenuative materials, extreme velocity variability, wavefield interference, and low signal-to-noise ratios provide an ideal breeding ground for the generation of artifacts on near-surface seismic-reflection data. With the cost of shallow reflection data being so high relative to other geophysical methods and invasive sampling, sometimes a single failure can hinder the growth in the use of the method. The method is extremely powerful and has the potential to provide vast quantities of information critical to understand the distributed hydrogeological and biogeochemical processes that elude borehole investigations. It is imperative that data be acquired in its rawest possible form and be processed with an eye to each operation. Cost savings sometimes result in one-size-fits-all acquisition and automated processing flows. Attention to detail and following signal from origination to characterization is essential.