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

In geophysical exploration, laser remote sensing detection of seismic waves based on wavefront sensors can be used for geological detection and geophysical exploration. However, due to the high sensitivity of the wavefront sensor, it is easy to be affected by the environmental light and vibration, resulting in random noise, which is difficult to predict, thus significantly reducing the quality of the vibration signal and the detection accuracy. In this paper, a large amount of data is collected through a single-point vibration detection experiment, and the relationship between amplitude and spot centroid offset is analyzed and calculated. The real noisy vibration signal is denoised and signal enhanced by using a BiLSTM denoising convolutional self-encoder (BiL-DCAE). The irregular and unpredictable noise generated by various complex noise mixing is successfully suppressed, and its impact on the vibration signal is reduced. The signal-to-noise ratio of the signal is increased by 13.90 dB on average, and the noise power is reduced by 95.93%, which greatly improves the detection accuracy.