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Abstract

Summary

In vertical seismic profile (VSP) exploration, distributed optical fiber acoustic sensing (DAS) technology uses optical fiber to simultaneously complete formation strain detection and optical signal transmission. Compared with traditional electronic detectors, this technology has the advantages of higher spatiotemporal accuracy, smaller sampling interval and lower layout cost. However, the borehole seismic recordings explored by DAS are usually contaminated by multi-types of noise with strong energy, such as ringing noise, fading noise, background noise, and optical noise. These noises of different characteristics are caused by the instrument systems or the underground environment, and seriously interfere with the signal continuity of the seismic waves.

Among these noises, the optical noise generated during acquisition is usually manifested as a large-area, strong-energy black and white plaques in the recording, which will completely cover the overlapping seismic reflected signals, causing problems for signal recovery. Obtaining a prior knowledge is the first step in suppressing noise, but at present, not much has been revealed about the production mechanism and characteristics of the optical noise.

Therefore, we try to establish a priori about this noise. Specifically, we study the mathematical modeling method (multiplicative or additive) of this noise based on its statistical distribution, and take the different-trace optical noise sampled from the real borehole recording as examples to draw the conclusion. In order to prove the conclusions obtained, we further use secondary spectrum analysis (complex cepstrum), which is suitable for multiplicative noise analysis, to reflect the frequency differences between seismic waves and optical noise for filtering. According to the frequency standards established by the complex cepstrum, we filter the seismic data interfered by the optical noise with the frequency division point. The filtering results effectively prove the conclusion of the mathematical model analysis we obtained.

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/content/papers/10.3997/2214-4609.202376020
2023-11-15
2025-02-19
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References

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