  Migration-based earthquake location methods may encounter the polarity
reversal issue due to the non-explosive components of seismic source, leading
to an unfocused migration image. Various methods have been proposed, yet
producing an ideally focused migration source image is still a challenge. In
this study, by taking advantage of the general pattern recognition ability of
convolutional neural network, we propose a novel Deep Learning Image Condition
(DLIC) to address this issue. The proposed DLIC measures the goodness of
waveform alignments for both P- and S-waves after correcting their traveltime
moveouts and it follows the geophysical principle of seismic imaging that the
best-aligned waveforms utterly represent a best-imaged source location. Both a
synthetic test and real data application are used to show the effectiveness and
merits of the proposed DLIC. A test on synthetic data shows that the DLIC can
effectively overcome the polarity reversal issues in the data. Real data
application to southern California shows that the DLIC can greatly enhance the
focusing of migrated source image over the widely used source scanning
algorithm. Further tests show that the DLIC is applicable to continuous seismic
data, to regions with few historical earthquakes, and has the potential to
locate small earthquakes. The proposed DLIC shall benefit the migration-based
source location methods.
