A Compressed Sensing Method for Adaptive Microseismic Data Based on Dictionary Learning

A dictionary learning and data compression technology, applied in the field of signal processing, can solve problems such as unsatisfactory effects, peak deviation of microseismic signals, and inability to adjust adaptively, achieving good technical value and application prospects, ideal effects, and reducing storage and transmission pressure. Effect

Active Publication Date: 2020-11-10
SHANDONG UNIV OF SCI & TECH
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Problems solved by technology

The commonly used sparse transformation methods include discrete cosine transform (DCT), Fourier transform (FFT), wavelet transform, etc., which cannot be adaptively adjusted according to the characteristics of the data itself, resulting in deviation of the peak value of the microseismic signal and the reconstructed effect. not ideal

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  • A Compressed Sensing Method for Adaptive Microseismic Data Based on Dictionary Learning
  • A Compressed Sensing Method for Adaptive Microseismic Data Based on Dictionary Learning
  • A Compressed Sensing Method for Adaptive Microseismic Data Based on Dictionary Learning

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Embodiment Construction

[0062] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0063] Such as figure 1 As shown, an adaptive microseismic data compression sensing method based on dictionary learning, specifically includes the following steps:

[0064] Step 1: Read the microseismic signal monitoring data time series sequence X(t), t=1,2,...,N;

[0065] Step 2: Construct an adaptive redundant dictionary D according to the characteristics of microseismic signals;

[0066] Commonly used sparse transformation methods include discrete cosine transform (DCT), Fourier transform (FFT), wavelet transform, etc., which cannot be adaptively adjusted according to the characteristics of the data itself, resulting in deviation of the peak value of the microseismic signal, such as figure 2 As shown, while the trained dictionary can overcome this defect, the process of K-SVD training dictionary can be expressed as:

[0067]

[0068] ...

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Abstract

The invention discloses an adaptive microseismic data compression sensing method based on dictionary learning, which belongs to the technical field of signal processing. The invention constructs an adaptive redundant dictionary, and determines the number of samples according to the energy of the signal and the sparse decomposition coefficient on the adaptive dictionary , and then the signal is compressed and sampled according to the compressed sensing technology, and the signal is reconstructed after being stored and transmitted to the terminal. The present invention adopts the K-SVD algorithm to construct an adaptive redundant dictionary according to the characteristics of the microseismic signal, which ensures that the peak value of the signal will not be deviated after sparse decomposition and reconstruction, and then adaptively determines the number of samples according to the energy and sparsity of the signal to reduce the number of samples , which increases the effective sampling rate and reduces the pressure of storage and transmission. This algorithm is simple and easy to implement, and the effect is ideal. It can effectively compress and sample mine microseismic signals, and has good technical value and application prospects.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to an adaptive microseismic data compression sensing method based on dictionary learning. Background technique [0002] Mine microseismic monitoring is mainly to monitor the vibration signal generated by the rock mass rupture during excavation in the mining area. Real-time monitoring needs to transmit a large amount of data, so it is necessary to sample the real-time signal with compressed sensing method to transmit as little data as possible, and then reconstruct the collected data at the terminal. [0003] Compressed sensing theory points out that the sparser the representation coefficient of the signal under the sparse basis (dictionary), the better the reconstruction quality of the signal, so the signal sparse decomposition method will directly affect the performance of signal reconstruction. The commonly used sparse transformation methods include discrete...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03M7/30G01V1/28G01V1/22
CPCG01V1/22G01V1/28H03M7/3062
Inventor 彭延军田赛王元红卢新明贾瑞生
Owner SHANDONG UNIV OF SCI & TECH
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