Measuring seismic data compression: What losses are acceptable?

Abstract

With the increasing use of lossy seismic data compression techniques by the industry (Bosman and Reiter, 1993; Stigant et al., 1995; Ergas et al., 1996), a dilemma facing users of this technology is to avoid loss of real geophysical information while still obtaining the advantages of smaller data volumes. At this time, each survey requires both testing and an explicit decision to accept or reject a compressed version of the original data. In this paper, we will discuss the available testing methods and how this decision may be made as routine as others we make in acquisition, processing, and interpretation of seismic data. The choice of a level of compression performance could be systematically formulated in terms of the economic cost of recovering from “overcompression” and its probability of occurrence versus the value derived from moving or storing less data. The probability is a difficult number to estimate, and will depend upon the purpose to which the data is put. A straightforward structural interpretation is likely to be more forgiving of compression errors than a quantitative inversion for lithology or fluid content. As a practical matter, we need to reduce the probabilityof compression failure to a very small number based on testing and experience, and to have a backup plan to use the either a lower compression ratio or the original, uncompressed data. Our experiences to date have been that loss of critical information is not difficult to detect at high compression ratios, and that the performance is reliable if we make a choice of compression ratio significantly lower than the point at which failure is evident. There are several possible approaches to testing compression performance on seismic data. These span the range from visual inspection of the original and compressed data on conventional plots to detailed statistical experiments of relative interpreter performance on sections with and without compression. We have found displays of the data along with the subtraction of the compressed and original data to be a robust, if labor-intensive, approach. Care must be taken with amplitudes in both processing and plotting, as simple but data-dependent processes such as AGC can make analysis difficult. Summary statistics, such as compression signal to noise ratio (SNR), which is the ratio of the energy in the original to the energy in the subtraction, can be useful as a monitoring device, but do not give a clear indication when unacceptable artifacts begin to appear. SNR can be measured as a function of time, space, frequency, wavenumber, etc. to provide more detailed criteria which can be used to decide compression acceptability. Other statistics such as entropy (Chen, 1995) can be used, as well as derived attributes from the seismic data. Selection of statistics and threshold values will vary in different geologic and seismic acquisition environments. A judgement of acceptability for a given compression dataset can be made on the basis of the data, or it can be done through measurements of interpreter performance, with the quality of the interpretation as the metric of compression performance. In the field of medical radiology, the same situation has arisen, with expert readers of images working on both compressed and original datasets. There is a well-developed statistical methodology (Cosman et al., 1994) which can be used to determine if significant performance differences can be detected. Such a test could be attempted in our field, using either real data or synthetic seismograms. In the later case, the ground truth is known, which is normally not the case in seismology. As with many other geophysical methods, the experiences and comfort level of the users of seismic data will determine when and whether compression becomes commonplace. Initial results indicate that wavelet transform compression, with proper attention to compression parameters, is robust and predictable. Over the next few years, if this continues, practical methods to insure that geophysical information is preserved through the compression step should evolve and stabilize, and compression will become a routine part of the geophysicist’s toolbox.