Abstract
At present, a primary challenge of seismic data processing is the ability to recognize and identify first-break seismic signals with low signal-to-noise ratio (SNR) in mountainous areas. Correlation-based supervirtual interferometry (SVI) can improve the SNR of refractions and diffractions to achieve high-quality results in velocity model construction and diffraction imaging from low SNR data. However, SVI is susceptible to coherent Gaussian noise and is limited to 2-D cases. This paper develops the cumulant-based coherent integration (CCI) method to enhance the first-break signals by using cumulant functions and multiple convolutions for both 2-D and 3-D land seismic data. The 2-D synthetic data example demonstrates that CCI can suppress coherent Gaussian noise and obtain results with higher SNR than SVI. The 3-D synthetic data example demonstrates the effectiveness of the 3-D CCI. Its application to 3-D field exploration seismic data measured in the mountainous areas of western China illustrates that the SNR of the first-break signals is much higher than that obtained by bandpass filtering, which is commonly employed in commercial software today.
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