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Transient weak signal detection method based on nonlinear time extrusion time-frequency transformation

A technology of weak signal detection and time-frequency transformation, which is applied in seismic signal processing, measuring devices, geophysical measurement, etc., can solve the problem that nonlinear squeezing time-frequency transformation is not suitable for detecting strong frequency signals, etc., and achieves easy implementation and reliable Good operability and improved time resolution

Active Publication Date: 2020-07-31
CHINA NAT OFFSHORE OIL CORP +1
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Problems solved by technology

[0006] (1) Non-linear squeezing time-frequency transformation is not suitable for detecting signals with strong frequency;
[0007] (2) Time synchronization squeeze transformation is not suitable for signals with weak components

Method used

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  • Transient weak signal detection method based on nonlinear time extrusion time-frequency transformation
  • Transient weak signal detection method based on nonlinear time extrusion time-frequency transformation
  • Transient weak signal detection method based on nonlinear time extrusion time-frequency transformation

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Embodiment 1

[0048] In this embodiment, the synthetic shock signal is used to test the effectiveness of the method of the present invention.

[0049] As shown in Fig. 1(a), an impulse response occurs at 0.25s and 0.75s, and their magnitudes are 2 and 0.02, respectively. The sampling frequency of the signal is 200Hz, and the sampling time is 1s. This example presents the time-frequency results of the short-time Fourier transform (Fig. 1(b)), the time-synchronously squeezed transform (Fig. 1(c)) and the nonlinear time-squeezed transform (Fig. 1(d)) . Through observation, it can be obtained that the energy in the time direction of the time-frequency spectrum of the time-synchronous squeeze transform is more concentrated than that of the short-time Fourier transform, but the existence of weak signals cannot be detected. However, as a result of the method of the present invention, components with weak energy can be accurately and clearly detected.

[0050] Then this embodiment provides a sim...

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Abstract

The invention relates to a weak signal detection method based on nonlinear time extrusion transformation. The method comprises the following steps: 1) transforming a transient signal f (t) to be detected by using a short-time Fourier transform formula to obtain a short-time Fourier transform result; 2) performing group delay operator estimation on the basis of the impulse signal to obtain a groupdelay operator; and 3) calculating a nonlinear time extrusion transformation time-frequency result by utilizing the group delay operator in the step 2) and the short-time Fourier transformation coefficient of the to-be-detected transient signal in the step 1), and obtaining a time-frequency coefficient of the to-be-detected transient signal at the occurrence moment according to the time-frequencyresult. According to the nonlinear time extrusion time-frequency transformation, a signal component with weaker energy can be clearly detected in a time-frequency domain. The technical scheme is easyto implement and high in operability. Therefore, the method can be widely applied to the field of transient weak signal detection.

Description

technical field [0001] The invention belongs to the field of signal processing in geophysical prospecting, and relates to a detection method for transient weak signals, in particular to a detection method for transient weak signals based on nonlinear time-squeezing time-frequency transformation. Background technique [0002] The phenomenon of weak signal is very common in practical engineering, and its detection and identification is an important task. Aiming at the detection of weak signals, there have been many theoretical and applied researches. The more commonly used methods include wavelet transform, curvelet transform and so on. However, the wavelet threshold method lacks effective protection of the signal amplitude when suppressing random noise, and its detection effect on weak signals is not ideal; while the curvelet threshold method can effectively detect weak signals, but the results often contain some " Artifacts". [0003] In the new technology, some scholars ...

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Application Information

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IPC IPC(8): G01V1/30
CPCG01V1/307G01V2210/63
Inventor 王清振高静怀张金淼姜秀娣朱振宇翁斌丁继才孙文博桑淑云李振赵小龙李超郑颖
Owner CHINA NAT OFFSHORE OIL CORP
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