Weak signal enhancement using adaptive local similarity and neighboring super-virtual trace for first arrival picking.

Accurate traveltime of first arrivals is of great importance in investigating subsurface velocity information. A significant challenge preventing the picking of the first arrival, however, is that the recorded traces in complex mountain areas are often characterised by weak energy, strong noise and dramatic phase variation. The method of super-virtual refraction interferometry (SVI) is capable of retrieving and enhancing the weak first arrivals from those traces and attenuating the random noise. Unfortunately, the conventional SVI has equal-weighted stacking, and is susceptible to strong local noise. This paper introduces adaptive data-driven weights based on local similarity into SVI to solve this problem. Both near- and far-offset reference traces of high quality are technically selected for better preservation of useful information. Next, we develop some neighboring super-virtual traces in the stacking process for further enhancement of weak signals, which is a further extension and theoretically superior to conventional SVI in increasing the total stacking number. The successful applications of model and field data show the great advantages of our improved method. Compared with conventional SVI, our method has a better local noise suppression effect and stronger enhancement ability, especially at weak refractions. More importantly, it can provide a significant guarantee of higher quality data, thus distinctly achieving a more accurate and reliable traveltime in first arrival picking. [ABSTRACT FROM AUTHOR]