DAS data de-noising method based on wavelet base tensor sparse representation

Abstract

The invention discloses a DAS data de-noising method based on wavelet base tensor sparse representation, which is applied to the field of seismic data processing and capable of effectively retaining structural information in DAS data and effectively removing noise. By proposing a new sparse representation model, the DAS data is expressed as a sparse tensor form. In the calculation process, an iterative compression threshold algorithm based on tensor product is used to calculate and reduce the computational complexity.

Description

technical field [0001] The invention belongs to the field of seismic data processing, in particular to a DAS data denoising technology. Background technique [0002] Distributed optical fiber sensing technology is a revolutionary new technology, which has been developed rapidly in the past two years. There are still few researches on the noise generated by optical fiber acquisition at home and abroad. In the field of signal processing, noise reduction technology has been relatively mature after long-term development. From the perspective of signal processing, the present invention divides noise into Gaussian noise and non-Gaussian noise according to types for processing. [0003] Gaussian noise is a common random noise whose n-dimensional probability density function obeys Gaussian distribution. Gaussian distribution, also known as normal distribution. For a random variable X, its probability density function is as follows figure 1 As shown, its distribution is denoted a...

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Problems solved by technology

Although there are currently many optional models in theory, the selected model needs to be well matched with the noise distribution, otherwise an unsatisfactory denoising effect will be obtained

It is precisely because of the strict requirements of model selection that statistical denoising methods are not suitable for complex noise sources. Therefore, this method also has certain limitations in practical research.

[0020] Due to the complex noise interference of seismic signals during data acquisition, the geological structure is complicated, which brings errors to the later seismic interpretation

Therefore, we need a method that can effectively suppress these noises. Although some existing methods can basically meet these requirements, there are still many deficiencies, and we still have a lot of room for improvement.

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