  We present a series of new open source deep learning algorithms to accelerate
Bayesian full waveform point source inversion of microseismic events. Inferring
the joint posterior probability distribution of moment tensor components and
source location is key for rigorous uncertainty quantification. However, the
inference process requires forward modelling of microseismic traces for each
set of parameters explored by the sampling algorithm, which makes the inference
very computationally intensive. In this paper we focus on accelerating this
process by training deep learning models to learn the mapping between source
location and seismic traces, for a given 3D heterogeneous velocity model, and a
fixed isotropic moment tensor for the sources. These trained emulators replace
the expensive solution of the elastic wave equation in the inference process.
We compare our results with a previous study that used emulators based on
Gaussian Processes to invert microseismic events. We show that all of our
models provide more accurate predictions and $\sim 100$ times faster
predictions than the method based on Gaussian Processes, and a
$\mathcal{O}(10^5)$ speed-up factor over a pseudo-spectral method for waveform
generation. For example, a 2-s long synthetic trace can be generated in $\sim
10$ ms on a common laptop processor, instead of $\sim$ 1 hr using a
pseudo-spectral method on a high-profile Graphics Processing Units card. We
also show that our inference results are in excellent agreement with those
obtained from traditional location methods based on travel time estimates. The
speed, accuracy and scalability of our open source deep learning models pave
the way for extensions of these emulators to generic source mechanisms and
application to joint Bayesian inversion of moment tensor components and source
location using full waveforms.
