A Novel Denoising Method for Mud Continuous-Wave Signals Based on Selective Ensemble Strategy with Particle Swarm...

Area of Science:

• Geophysics
• Signal Processing
• Drilling Engineering

Background:

• Mud continuous-wave signals in drilling are distorted by complex background noise.
• Traditional noise cancellation methods lack effectiveness and generalization capabilities.

Purpose of the Study:

• To develop an advanced noise reduction strategy for continuous-wave signals in drilling.
• To improve the precision and robustness of signal reconstruction in challenging environments.

Main Methods:

• Proposed a particle swarm optimization (PSO)-based selective ensemble filtering strategy.
• Adaptively selected optimal filtering algorithms and assigned weights for complementary strengths.
• Applied PSO to identify optimal weights and perform selective ensemble of candidate signals.

Main Results:

• The reconstructed signal demonstrated superior quality metrics and robustness compared to single-filter methods.
• The selective ensemble approach enhanced generalization capability in complex drilling environments.
• Achieved high-precision reconstruction of continuous-wave signals.

Conclusions:

• The PSO-based selective ensemble filtering strategy is effective for continuous-wave signal reconstruction in drilling.
• This method offers significant improvements over traditional noise cancellation techniques.
• The approach provides a robust solution for noisy drilling signal processing.