Extraction Research Of Weak Signal Under Strong Ambient Noise In Air Gun Source

Seismic signal denoising is a key step in seismic data processing.When calculating the change of wave velocity in underground media,the higher the signal-to-noise ratio of the data,the more favorable it is for the analysis of the calculation results.During the propagation of seismic signals,the scattering and attenuation of the signal makes the signal-to-noise ratio of the recorded signal lower and lower,Therefore,it is necessary to realize the identification and extraction of weak seismic signals in a strong background noise environment.For the research of using active source signals to change the wave velocity of underground media,the effective signal includes the change of the signal waveform and the change of the travel time difference between the signals.Curvelet transform is a multi-scale geometric analysis method developed on the basis of Wavelet transform and Ridgelet transform.This article will applied Curvelet transform analysis method to the onshore active air gun source signal denoising in the study,the general Curvelet transform method to estimate the threshold calculation rarely took into account the characteristics of the known signal and result in signal processing has a certain roughness,distinguish between signal and noise characteristic is not obvious,the denoising effect is not ideal,is easily confused noise and signal.In this paper,the threshold determination method of Curvelet denoising is refined,and the determination of the Curvelet threshold is optimized for the active source air gun signal.At the same time,the frequency band information of the known active source signal is combined to make a purposeful distinction and retention in the threshold determination,Improve the ability to identify and extract effective signals in the process of Curvelet threshold denoising.At the same time,the Template matching filtering technology for signal identification and extraction is discussed,and the Curvelet transform method is applied to it.In the process of recovering the one-dimensional Template matching filter signal,two-dimensional Curvelet processing is added,and the template matching filtering method is reduced to reconstruct the signal.Noise.With the aid of Curvelet transform,this data processing method can effectively improve the ability to identify and extract weak seismic signals,reduce noise interference,and improve the accuracy of calculation of wave velocity changes in underground media.