  Sunquakes are seismic emissions visible on the solar surface, associated with
some solar flares. Although discovered in 1998, they have only recently become
a more commonly detected phenomenon. Despite the availability of several manual
detection guidelines, to our knowledge, the astrophysical data produced for
sunquakes is new to the field of Machine Learning. Detecting sunquakes is a
daunting task for human operators and this work aims to ease and, if possible,
to improve their detection. Thus, we introduce a dataset constructed from
acoustic egression-power maps of solar active regions obtained for Solar Cycles
23 and 24 using the holography method. We then present a pedagogical approach
to the application of machine learning representation methods for sunquake
detection using AutoEncoders, Contrastive Learning, Object Detection and
recurrent techniques, which we enhance by introducing several custom
domain-specific data augmentation transformations. We address the main
challenges of the automated sunquake detection task, namely the very high noise
patterns in and outside the active region shadow and the extreme class
imbalance given by the limited number of frames that present sunquake
signatures. With our trained models, we find temporal and spatial locations of
peculiar acoustic emission and qualitatively associate them to eruptive and
high energy emission. While noting that these models are still in a prototype
stage and there is much room for improvement in metrics and bias levels, we
hypothesize that their agreement on example use cases has the potential to
enable detection of weak solar acoustic manifestations.
