Implementation of an acoustic-logging-data compression algorithm on DSP and FPGA platforms.

Currently, notable difficulties exist regarding the real-time uploading of data and fast logging in remote-detection acoustic logging, which can be mitigated via downhole data compression. This study systematically analyzed a wavelet transform-based data compression method and developed hardware platforms based on a digital signal processor (DSP) and field programmable gate array (FPGA). The wavelet transform-based acoustic-logging-data compression algorithm was executed on both the hardware platforms, and the corresponding decompression algorithm was implemented on the host computer. The performance and applicability of the algorithm were evaluated using actual acoustic logging data. Results indicated that the compression ratio and distortion rate of the single-layer wavelet transform-based data compression algorithm exhibited minimal relation with the two hardware platforms. The compression ratio was ∼50%; the reconstructed full waveform effectively preserved the overall morphology of the original signal; and distortions at individual positions exerted negligible impact on the extraction of the sliding longitudinal wave, sliding transverse wave, and reflected wave in the full-waveform data. The wavelet transform-based data compression algorithm occupied less memory in the FPGA platform for processing 2048-word acoustic logging full-waveform data. The execution time was ∼42 μs, which was substantially less than the millisecond-scale runtime required by the DSP platform. This study provides an idea of data compression at the receiver node in a remote-detection acoustic logging tool, which can reduce the workload of the master controller and improve cable transmission and logging efficiencies, serving as a reference for designing next-generation remote-detection acoustic logging tools.