  Continuous microseismic monitoring of hydraulic fracturing is commonly used
in many engineering, environmental, mining, and petroleum applications.
Microseismic signals recorded at the surface, suffer from excessive noise that
complicates first-break picking and subsequent data processing and analysis.
This study presents a new first-break picking algorithm that employs concepts
from seismic interferometry and time-frequency (TF) analysis. The algorithm
first uses a TF plot to manually pick a reference first-break and then iterates
the steps of cross-correlation, alignment, and stacking to enhance the
signal-to-noise ratio of the relative first breaks. The reference first-break
is subsequently used to calculate final first breaks from the relative ones.
Testing on synthetic and real data sets at high levels of additive noise shows
that the algorithm enhances the first-break picking considerably. Furthermore,
results show that only two iterations are needed to converge to the true first
breaks. Indeed, iterating more can have detrimental effects on the algorithm
due to increasing correlation of random noise.
