  Microseismic event detection and location are two primary components in
microseismic monitoring, which offers us invaluable insights into the
subsurface during reservoir stimulation and evolution. Conventional approaches
for event detection and location often suffer from manual intervention and/or
heavy computation, while current machine learning-assisted approaches typically
address detection and location separately; such limitations hinder the
potential for real-time microseismic monitoring. We propose an approach to
unify event detection and source location into a single framework by adapting a
Convolutional Neural Network backbone and an encoder-decoder Transformer with a
set-based Hungarian loss, which is applied directly to recorded waveforms. The
proposed network is trained on synthetic data simulating multiple microseismic
events corresponding to random source locations in the area of suspected
microseismic activities. A synthetic test on a 2D profile of the SEAM Time
Lapse model illustrates the capability of the proposed method in detecting the
events properly and locating them in the subsurface accurately; while, a field
test using the Arkoma Basin data further proves its practicability, efficiency,
and its potential in paving the way for real-time monitoring of microseismic
events.
