Curvelet domain statistics self-adaptive threshold ground penetrating radar data de-noising method and system

Abstract

The invention belongs to the technical field of the data de-noising, and discloses a curvelet domain statistics self-adaptive threshold ground penetrating radar data de-noising method and system. Themethod comprises the following steps: importing a massive complex domain threshold function algorithm, analyzing a change rule of the traditional threshold function curvelet conversion de-noising effect along a threshold function control coefficient so as to be used for the subsequent curvelet domain statistics self-adaptive threshold contrast; performing correlation superposition on the curveletconversion coefficient on the scale and direction by utilizing high-order statistics theory, and statistically and self-adaptively determining the distribution scale and rotational direction of an effective signal on the curvelet conversion coefficient through the correlation; determining a noise removing component threshold range, constructing a statistics self-adaptive threshold function curvelet conversion de-noising algorithm. Compared with the prior art, the processing result on the synthetic ground penetrating radar data containing random noise and related noise and the actually-measuredground penetrating radar data has guidance significance on the precise inference interpretation on the complex ground penetrating radar data.

Description

technical field [0001] The invention belongs to the technical field of data denoising, and in particular relates to a method and system for denoising data of a curvelet domain statistic adaptive threshold value ground penetrating radar. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] Ground penetrating radar uses antennas to transmit electromagnetic waves of different frequencies, and uses the difference in electromagnetic properties of the underground medium to infer the spatial and physical distribution of the target medium according to the kinematic and dynamic characteristics of the received echoes such as amplitude and waveform. It is widely used in geological disaster monitoring, engineering and environmental geological survey, hydrogeological survey and military reconnaissance and other fields. However, as the exploration environment of ground penetrating radar becomes more complex and the tar...

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Problems solved by technology

[0010] (3) At present, curvelet transform configuration has developed many methods for calculating the threshold value, such as piecewise linear filtering method, L2 standard deviation method, curvelet forward transformation method, diagonal real number and complex number threshold value method, etc., which need to be based on actual data The selection of noise types generally cannot meet the purpose of adaptive fidelity denoising

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