Earthquake migration patterns are important to reveal various triggering
mechanisms, including the tectonic process and those caused by
anthropogenic activities. Mapping out the spatial-temporal seismicity
pattern is traditionally conducted using reference marks either in
spatial or time. However, such mapping is particularly challenging for
induced earthquakes because most industrial records that provide
reference marks are unavailable to the public. Moreover, advances in
earthquake detection techniques proliferate earthquake catalogs and thus
require labor-intensive investigation. Therefore, a new methodology is
demanded to automatically investigate spatial-temporal patterns of
seismicity without reference marks. Here, we present a deep
learning-based method to automatically identify the timings and
locations of anomalous seismicity, defined by the sudden change of
earthquakes in a region. We first rasterize multi-dimensional earthquake
catalogs into 2-D distribution maps. Then, we identify the maps with
anomalous seismicities and extract their timings and locations to
generate condensed catalogs to reduce the manual effort in further
investigation. We choose Changning and Weiyuan in Sichuan Basin as our
study areas due to their high seismicity rates in recent years. We use
the Changning catalog to train the method and the Weiyuan catalog to
test the method's spatial transferability. Our approach successfully
condenses both the Changning and Weiyuan catalogs with the accuracy of
0.87 based on the F1 score. The anomalous seismicities identified by our
network include both earthquakes associated with hydraulic fracturing
and aftershocks following strong quakes. As such, our method could be
applied to broader areas with more complex migration patterns, including
natural earthquake sequences.