China Aero Geophysical Survey and Remote Sensing Center for Natural ResourcesHost
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2022 Vol. 46, No. 2
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XIE Xing-Long, MA Xue-Mei, LONG Hui, MING Yuan-Yuan, SUN Sheng. 2022. Curvelet transform-based denoising of resonance interference induced by electrical poles in seismic exploration. Geophysical and Geochemical Exploration, 46(2): 474-481. doi: 10.11720/wtyht.2022.2411
Citation: XIE Xing-Long, MA Xue-Mei, LONG Hui, MING Yuan-Yuan, SUN Sheng. 2022. Curvelet transform-based denoising of resonance interference induced by electrical poles in seismic exploration. Geophysical and Geochemical Exploration, 46(2): 474-481. doi: 10.11720/wtyht.2022.2411

Curvelet transform-based denoising of resonance interference induced by electrical poles in seismic exploration

  • The resonance interference induced by electrical poles is a common type of noise in middle-shallow seismic exploration,especially for shallow data.However,relevant studies are scarce since it is rarely involved in petroleum and coalfield exploration.The Curvelet transform allows for a sparse representation of smooth regions and edges in images and can meet the requirements of time-varying signal processing,thus achieving good effects in the processing of seismic data.Based on the characteristics of the electrical pole-induced resonance interference in seismic data,this paper proposes a new method that utilizes the Curvelet transform to remove the resonance interference in original data and the steps are as follows.First,analyze the differences between the characteristics of the resonance interference induced by electrical poles and effective information in the Curvelet domain.Based on this,conduct wavefield separation according to the multi-scale and multi-direction characteristics of the Curvelet transform.Then,further attenuate the interference factors using the nonlinear threshold function designed in this paper.According to the application and analysis of actual data,this method can effectively remove the resonance interference induced by electrical poles while properly protecting effective signals and can significantly improve the signal-to-noise ratio and resolution of the denoised data.Therefore,the method proposed in this paper is effective.
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