The authors consider the development and evaluation of transform coding algorithms for the storage of seismic signals. Transform coding algorithms are developed using the discrete Fourier transform (DFT), the discrete cosine transform (DCT), the Walsh-Hadamard transform (WHT), and the Karhunen-Loeve transform (KLT). These are evaluated and compared to a linear predictive coding algorithm for data rates ranging from 150 to 550 bit/s. The results reveal that sinusoidal transforms are well-suited for robust, low-rate seismic signal representation. In particular, it is shown that a DCT coding scheme reproduces faithfully the seismic waveform at approximately one-third of the original rate. >

Transform methods for seismic data compression

This paper examines the utility of linear predictive coding in reducing the amount of data storage required for signals gathered in ocean bottom seismology. In this study, a set of 12 typical signals were repeatedly encoded with the storage allocated decreasing from an initial 12 bits per datum to 2. The error introduced was then compared to the performance achieved by simply rounding off the lowest bits of the data, and to estimates of the rate distortion limit. It was found that this scheme consistently introduced about 15 times (4 bits) less distortion both in terms of the root-mean-square (rms) error and in terms of the maximum error than rounding the data. Moreover, the rms distortion of the data were within a factor of 4 (2 bits) of the rate distortion bound on optimal encoding. Thus, the scheme was seen to be an effective approach to the problem of data compression in the marine environment.

http://ieeexplore.ieee.org/iel6/29/26178/01164144.pdf

Linear predictive coding of marine seismic data

The deployment lifetime of an in situ acquisition system is a function, primarily, of the efficient utilization of the energy source and data storage medium. Recent improvements in data and electrical energy storage will provide some improvement in deployment life and have certainly reduced the cost per bit of data storage but the greatest potential improvement is in the application of microcomputing technology to programmed data selection and compaction. Programmed or computed data selection implements, in situ, the decision making process of a data collector and results in categorization of data into high, medium, and low value groups. Data compression techniques are then used to preprocess the categories into compacted data sets with information being preserved in accordance with the designated value of the set. Computed data selection followed by efficient data compression provides, then, a powerful approach for optimizing remote data acquisition. This paper examines data categorization and compression techniques which have been applied successfully to marine and non-marine environmental measurements and discusses hardware implementation. These techniques are applied to actual time series oceanographic data and the effectiveness of each technique evaluated. Results of the evaluations, impact of each technique on system software, and a recommendation for a prototype hardware system are presented.

In Situ Data Selection And Compression As A Tool For Extending The Lifetime Of Remote Acquisiton Systems

A backward adaptive predictor followed by a precise forward adaptive encoder is proposed to perform data compression for high precision digital waveform recording. The original waveform can be reconstructed perfectly from the compressed data using the identical inverse predictor and a complimentary decoder.

Data compression with perfect reconstruction for high precision digital waveform recording

An efficient method for sending a large Seismic section from a mainframe computer to a dot matrix printer located at a remote site

A first-order predictor routine and an encoding routine were implemented on an Intel-8080-based microprocessor system to determine their ability to compress seismic data. The predictor routine obtained a maximum compression ratio of 2.33. It appears that higher order predictors offer no significant advantage over the first-order predictor in compressing seismic data. The encoder routine obtained a larger compression ratio of 3.94. The accuracy of reconstructed seismic traces is bounded by a maximum average error per point. For the predictor, the maximum average error per point is shown to be equal to the specified user tolerance. For the encoder, it is shown to be proportional to the maximum quantization error associated with the ranges used in the encoding algorithm. In general, the average error per point is found to be equal to the square root of the mean-squared error regardless of the compression technique used. A design for a basic data acquisition system utilizing data compression to reduce memory requirements is proposed. Examination of the design indicates that such a system can process no more than three separate data channels at a 1-ms sampling rate and that such a system is cost effective only when small numbers of data channels are to be processed.

Philip Panagos

Papers

Data Compression in Microprocessor-Based Data Acquisition Systems