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Deep Learning-Based Denoising of Noisy Vibration Signals from Wavefront Sensors Using BiL-DCAE.

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Summary

This study introduces a novel BiLSTM denoising convolutional self-encoder (BiL-DCAE) to improve seismic wave detection. The method effectively reduces noise, enhancing signal quality and geophysical exploration accuracy.

Keywords:
neural networksignal processingvibration signalwavefront sensor

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Area of Science:

  • Geophysics
  • Signal Processing
  • Optical Sensing

Background:

  • Laser remote sensing using wavefront sensors is crucial for geophysical exploration.
  • Environmental factors like light and vibration introduce unpredictable noise, degrading signal quality and detection accuracy.

Purpose of the Study:

  • To develop a robust method for denoising and enhancing seismic vibration signals in geophysical exploration.
  • To mitigate the impact of complex, unpredictable noise on laser-based seismic wave detection.

Main Methods:

  • Collected extensive data from single-point vibration detection experiments.
  • Analyzed the relationship between signal amplitude and spot centroid offset.
  • Employed a BiLSTM denoising convolutional self-encoder (BiL-DCAE) for noise suppression and signal enhancement.

Main Results:

  • Successfully suppressed irregular and complex noise in vibration signals.
  • Achieved an average signal-to-noise ratio improvement of 13.90 dB.
  • Reduced noise power by 95.93%, significantly enhancing detection accuracy.

Conclusions:

  • The BiL-DCAE model effectively denoises and enhances seismic vibration signals.
  • This approach substantially improves the accuracy of laser remote sensing for geophysical exploration.