In the paper, we propose a surface wave suppression method in time-frequency domain based on the wavelet transform, considering the characteristic difference of polarization attributes, amplitude energy and apparent v...In the paper, we propose a surface wave suppression method in time-frequency domain based on the wavelet transform, considering the characteristic difference of polarization attributes, amplitude energy and apparent velocity between the effective signals and strong surface waves. First, we use the proposed method to obtain time-frequency spectra of seismic signals by using the wavelet transform and calculate the instantaneous polarizability at each point based on instantaneous polarization analysis. Then, we separate the surface wave area from the signal area based on the surface-wave apparent velocity and the average energy of the signal. Finally, we combine the polarizability, energy, and frequency characteristic to identify and suppress the signal noise. Model and field data are used to test the proposed filtering method.展开更多
基金supported by the National Science and Technology Major Project(No.2011ZX05002-004-002)the National Natural Science Foundation of China(No.41304111 and 41704132)+3 种基金Key Project of Science&Technology Department of Sichuan Province(No.2016JY0200)Natural Science project of Education Department of Sichuan Province(Nos.17ZA0025,16ZB0101 and 18CZ0008)the Sichuan Provincial Youth Science&Technology Innovative Research Group Fund(No.2016TD0023)the Cultivating Program of Excellent Innovation Team of Chengdu University of Technology(No.KYTD201410)
文摘In the paper, we propose a surface wave suppression method in time-frequency domain based on the wavelet transform, considering the characteristic difference of polarization attributes, amplitude energy and apparent velocity between the effective signals and strong surface waves. First, we use the proposed method to obtain time-frequency spectra of seismic signals by using the wavelet transform and calculate the instantaneous polarizability at each point based on instantaneous polarization analysis. Then, we separate the surface wave area from the signal area based on the surface-wave apparent velocity and the average energy of the signal. Finally, we combine the polarizability, energy, and frequency characteristic to identify and suppress the signal noise. Model and field data are used to test the proposed filtering method.
