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Deep Denoising of Wavefront Sensor Signals via Physics-Aware Dual-Channel Decoupled Network (PRISM).

Jianbao Ma1, Yun Pan1, Yiyou Fan1

  • 1Key Laboratory of Signal Intelligent Capture and New Generation Communication Technology, School of Electronic Engineering, Yili Normal University, 448 Jiefang Road, Yining 835000, China.

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|June 26, 2026
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Summary
This summary is machine-generated.

Laser remote sensing for seismic wave detection faces noise challenges. A new physics-aware network (PRISM) significantly improved signal quality and accuracy by reducing noise in vibration signals.

Keywords:
deep learningsignal enhancementsubmillimeter-scale vibration signalswavefront sensors

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Area of Science:

  • Geophysics
  • Optical Sensing
  • Signal Processing

Background:

  • Laser remote sensing using wavefront sensors offers high sensitivity for detecting minute vibrations.
  • High sensitivity makes these sensors vulnerable to environmental and instrument noise, degrading signal quality and detection accuracy.

Purpose of the Study:

  • To develop and validate a noise reduction method for laser remote sensing of seismic vibrations.
  • To enhance the accuracy and reliability of seismic wave detection in noisy environments.

Main Methods:

  • Collected over 60,000 vibration signal data samples under diverse amplitude and frequency conditions.
  • Utilized a laser remote sensing seismic wave detection system.
  • Applied a physics-aware dual-channel decoupled network (PRISM) for noise reduction.

Main Results:

  • Achieved significant improvements in signal quality across various real-world noise conditions.
  • Demonstrated an average signal-to-noise ratio improvement of 12.16 dB.
  • Showed an average signal distortion ratio improvement of 6.35 dB, preserving faint signals.

Conclusions:

  • The PRISM network effectively reduces noise in laser remote sensing vibration signals.
  • This method enhances the preservation of faint seismic signals, improving detection accuracy.
  • PRISM shows promise for robust seismic wave detection in challenging environments.