Based on marine single-component seismic data acquired by conventional acquisition method, the vertical component of velocity (pseudo-Vz) at the same position is estimated. The pseudo-Vz is then combined with the single component of pressure data to get the aim of deghosting. By applying the sparsity constraint deconvolution method as an intermediate procedure, improvements of shallow sea single-component seismic data in resolution as well as in filling the frequency notches, which are caused by the interference of the up- and downgoing waves at the receivers are achieved. In addition, the energy of low-frequency signal is enhanced. The proposed method can be used for re-processing the existing single-component data through which can broaden the frequency bandwidth.
In order to solve the problems such as large computation and high requirement of geometry in the traditional 2D ISS (inverse scattering series) method, the 1.5D ISS deghosting method is proposed after dimensionality reduction from 2D ISS method. The examples of data study show that the 1.5D method can save lots of calculation and also degrade the requirement of geometry. 1.5D ISS method does not need any subsurface information and wavelet estimation, and it is suitable for different kinds of complex structure and low signal to noise ratio seismic data. Besides, the 1.5D ISS method can remove the ghosts from marine seismic data effectively and enhance resolution; simultaneously it will expand the frequency band and compensate the null frequencies energy.
Generally, a cluster of seismic events which share similar source locations and focal mechanisms will show similar waveforms on the record. Based on this assumption, a method have been developed for microseismic event detection and arrival picking based on waveform cross-correlation. This method achieves moveout correction for the seismic records based on cross-correlation functions, then calculates a multi-channel semblance coefficient to identify the microseismic events. Meanwhile, the seismic records after moveout correction are superposed. The STA/LTA method is adopt to pick the arrivals for the stacked traces, the arrival times of the microseismic events are then automatically obtained. The performance of the method is evaluated using both synthetic and real datasets. Analysis of the results demonstrates that the proposed method can not only detect the microseismic events, but also obtain relatively accurate arrival picks at the same time.
When strong noise exists on local seismic traces with low signal-to-noise ratio (SNR), super-virtual interferometry (SVI) method can be used to increase the SNR of first breaks on far-offset traces, but may decrease the SNR of first breaks around the noisy traces. To solve this problem, the similarity-weighted super-virtual interferometry is developed. Correlation and convolution are applied to stack the first arrivals on neighboring traces in common phase, and consequently increase the SNR of first arrivals. The introduction of similarityweighted function improves the ability to suppress strong local abnormal noise. Both the synthetic and field data examples demonstrate the effectiveness of the proposed method to enhance seismic first breaks. At last, a discussion about the applicabilities and the anti-noise abilities of the proposed method is included.
