King Fahd University of Petroleum and Minerals

Hamood Khan

Salam Adel Zummo

Seismic acquisition having high geophone densities is compressed based on Functional Quantization (FQ) for an infinite dimensional space. Using FQ, the entire sample path of the seismic waveform in a target function space is quantized. An efficient solution for the construction of a functional quantizer is given. It is based on Monte-Carlo simulation to circumvent the limitations of high dimensionality and avoids explicit construction of Voronoi regions to tessellate the function space of interest. The FQ architecture is then augmented with three different Vector Quantization (VQ) techniques which yield hybridized FQ strategies of 1) FQ-Classified VQ, 2) FQ-Residual/Multistage VQ and 3) FQ-Recursive VQ. Joint quantizers are obtained by replacing regular VQ codebooks in these hybrid quantizers by their FQ equivalents. Simulation results show that the FQ combined with any one of the different VQ techniques yields improved rate-distortion compared to either FQ or VQ techniques alone.

Functional quantization based data compression in seismic acquisition

A method of compressing data from seismic waves using Gabor frames utilizes a plurality of geophones positioned within a region of interest. Each of the plurality of geophones is communicably coupled with at least one remote server. Thus, a plurality of reflected-seismic signals received through the plurality of geophones can be transmitted to the at least one remote server for analyzing and calculations. The plurality of reflected-seismic signals is converted into a set of Gabor frames, wherein the Gabor frames is generated via a plurality of prolate spheroidal wave functions (PSWF). A Gabor frame-generating calculation module utilizes the plurality of PSWF to generate the set of Gabor frames. A dual frame for each of the set of Gabor frames is derived and used for quantization purposes. Preferably, a tree structured vector quantization process is followed.

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Dhahran, Saudi Arabia

Hamood Khan