This chapter describes the principal ways in which data acquisition affects the seismic data processing effort. This includes information about array effects, aperture, aliasing, and the physical arrangement of the acquisition methods themselves.
It may be that the most frequently asked question about seismic acquisition is about the optimum approach to seismic acquisition. Fundamentally this is a question about the geometry and sampling rate of the receiver array, but it easily expands to include what source we should use, what microphones we should employ, whether or not we should use geophone sub-arrays, how big our aperture should be, and, finally, what temporal and spatial sampling rates we should select. In the spatial sense, we have always acquired seismic data digitally. We never had continuous (analog) sampling in space; analog data was only acquired in time.
The answers to these questions are mathematically and physically clear. For each source, the receiver array should consist of point receivers (no arrays) densely sampled over a wide aperture array encompassing a large square area. The source, however it is formed, should be a point source (no arrays) generating energy uniformly in all directions. For maximum benefit, there should be full source-receiver reciprocity; that is, for each receiver position, there should be a source, and for each source there should be a receiver. Hopefully, this chapter will make the reason for these statements clear.
Unfortunately, there are many reasons why the mathematics and physics are almost always ignored—primarily, economics and practicality trump correctness. Furthermore, faced with budget limitations in an era when oil was relatively easy to find, little or no consideration was given to the underlying mathematical assumptions. Many geophysicists assumed that mathematics, including the wave equation, did not apply to the seismic acquisition process. Arrays were designed to control perceived noise, but frequently depressed the dip response. Fancy acquisition geometries were designed to reduce costs, but resulted in data sets that could not image geologic objectives. Illumination studies were conducted in an attempt to determine the impact of any given acquisition style, but, because they were often based on one-way equations or rays, the studies had no real impact on the solution—such studies can be fairly meaningless since complicated waveforms exist in even relatively simple geologic environments.
In contrast, mathematics does not lie. Mathematics, physics and a tremendous amount of empirical evidence suggests that imaging is a complex process almost totally controlled by the degree to which mathematical assumptions are honored. While we will not go into the mathematics in detail, we hope to provide a reasonable clarification of why we should acquire data in a precise, mathematically-correct manner. We will show that existing acquisition schemes can and should be modified to meet implicit assumptions.
