Study on the Deconvolution Method and Processing Flow of Airgun Source Data

With its high repeatability,the airgun source has been used to monitor the temporal variations of subsurface structures. However,under different working conditions,there will be subtle differences in the airgun source signals. To some extent,deconvolution can eliminate changes of the recorded signals due to source variations. Generally speaking,in order to remove the airgun source wavelet signal and obtain the Green’s functions between the airgun source and stations,we need to select an appropriate method to perform the deconvolution process for seismic waveform data. Frequency domain water level deconvolution and time domain iterative deconvolution are two kinds of deconvolution methods widely used in the field of receiver functions,etc. We use the Binchuan( in Yunnan Province,China) airgun data as an example to compare the performance of these two deconvolution methods in airgun source data processing. The results indicate that frequency domain water level deconvolution is better in terms of computational efficiency;time domain iterative deconvolution is better in terms of the signal-to-noise ratio( SNR),and the initial motion of P-wave is also clearer. We further discuss the sequence issue of deconvolution and stack for multiple-shot airgun data processing. Finally,we propose a general processing flow for the airgun source data to extract the Green ’s functions between the airgun source and stations.