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

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Summary
This summary is machine-generated.

Downhole data compression using wavelet transforms significantly improves remote-detection acoustic logging. This method achieves a 50% compression ratio with minimal distortion, enhancing data transmission and logging efficiency.

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Area of Science:

  • Geophysics
  • Signal Processing
  • Data Compression

Background:

  • Remote-detection acoustic logging faces challenges in real-time data uploading and fast logging.
  • Downhole data compression is a key strategy to overcome these limitations.

Purpose of the Study:

  • To systematically analyze a wavelet transform-based data compression method for acoustic logging data.
  • To develop and evaluate hardware platforms (DSP and FPGA) for implementing this compression algorithm.

Main Methods:

  • Developed and executed a wavelet transform-based data compression algorithm on DSP and FPGA hardware platforms.
  • Implemented the decompression algorithm on a host computer.
  • Evaluated performance using actual acoustic logging data, focusing on compression ratio, distortion, and execution time.

Main Results:

  • Achieved a compression ratio of approximately 50% with minimal impact on signal morphology and wave extraction.
  • The algorithm showed minimal relation to the specific hardware platform (DSP vs. FPGA) in terms of compression ratio and distortion.
  • FPGA implementation was significantly faster (42 μs) than DSP (milliseconds) and occupied less memory.

Conclusions:

  • Wavelet transform-based data compression is effective for remote-detection acoustic logging, preserving essential waveform information.
  • FPGA offers superior performance for real-time processing compared to DSP.
  • This approach enhances logging efficiencies and reduces controller workload, providing a reference for future tool designs.