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However, the forward modelling of these microseismic events, which is necessary to perform Bayesian source inversion, can be prohibitively expensive in terms of computational resources. A viable solution is to train a surrogate model based on machine learning techniques to emulate the forward model and thus accelerate Bayesian inference. In this paper, we substantially enhance previous work, which considered only sources with isotropic moment tensors. We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows complete and fast event locations for any source mechanism. Moreover, we show that our approach is computationally inexpensive, as it can be run in less than 1\u00a0hr on a commercial laptop, while yielding accurate results using less than 104 training seismograms. We additionally demonstrate how the trained emulators can be used to identify the source mechanism through the estimation of the Bayesian evidence. Finally, we demonstrate that our approach is robust to real noise as measured in field data. This work lays the foundations for efficient, accurate future joint determinations of event location and moment tensor, and associated uncertainties, which are ultimately key for accurately characterizing human-induced and natural earthquakes, and for enhanced quantitative seismic hazard assessments.<\/jats:p>","DOI":"10.1093\/gji\/ggac385","type":"journal-article","created":{"date-parts":[[2022,10,6]],"date-time":"2022-10-06T23:57:14Z","timestamp":1665100634000},"page":"1219-1235","source":"Crossref","is-referenced-by-count":6,"title":["Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism"],"prefix":"10.1093","volume":"232","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9836-2661","authenticated-orcid":false,"given":"D","family":"Piras","sequence":"first","affiliation":[{"name":"Department of Physics and Astronomy, University College London , Gower Street, London WC1E 6BT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5698-0990","authenticated-orcid":false,"given":"A","family":"Spurio\u00a0Mancini","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy, University College London , Gower Street, London WC1E 6BT, UK"},{"name":"Mullard Space Science Laboratory, University College London , Holmbury St. Mary, Dorking, Surrey RH5 6NT, UK"},{"name":"Astrophysics Group, Cavendish Laboratory , J. J. Thomson Avenue, Cambridge CB3 0HE, UK"}]},{"given":"A M G","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Deptartment of Earth Sciences, Faculty of Mathematical & Physical Sciences, University College London , London WC1E 6BT, UK"}]},{"given":"B","family":"Joachimi","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy, University College London , Gower Street, London WC1E 6BT, UK"}]},{"given":"M P","family":"Hobson","sequence":"additional","affiliation":[{"name":"Astrophysics Group, Cavendish Laboratory , J. J. 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