14 citations · 32 across the 7 of their papers we have counts for
8 papers
Large-scale bioacoustic detection using semantic segmentation: a deep learning framework applied to fin whale calls in ocean-bottom seismometer recordings
Jocelyn Japnanto, Alex A. Saoulis, Miriam Romagosa +5
Ocean-bottom seismometers (OBS), originally deployed for geophysical research, continuously record low-frequency sound for months to years across broad areas of ocean, offering a l…
Field-level weak lensing cosmology with simulations using multifidelity simulation-based inference
Alex A. Saoulis, Kiyam Lin, Niall Jeffrey +5
We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 -body simulations. The weak lensing shear field…
Improving moment tensor solutions under Earth structure uncertainty with simulation-based inference
A. A. Saoulis, T. -S. Pham, A. M. G. Ferreira
Bayesian inference represents a principled way to incorporate Earth structure uncertainty in full-waveform moment tensor inversions, but traditional approaches generally require si…
Transfer learning for multifidelity simulation-based inference in cosmology
Alex A. Saoulis, Davide Piras, Niall Jeffrey +3
Simulation-based inference (SBI) enables cosmological parameter estimation when closed-form likelihoods or models are unavailable. However, SBI relies on machine learning for neura…
Full-waveform earthquake source inversion using simulation-based inference
A. A. Saoulis, D. Piras, A. Spurio Mancini +2
This paper presents a novel framework for full-waveform seismic source inversion using simulation-based inference (SBI). Traditional probabilistic approaches often rely on simplify…
Sparse Bayesian mass-mapping using trans-dimensional MCMC
Augustin Marignier, Thomas Kitching, Jason D. McEwen +1
Uncertainty quantification is a crucial step of cosmological mass-mapping that is often ignored. Suggested methods are typically only approximate or make strong assumptions of Gaus…
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism
Davide Piras, Alessio Spurio Mancini, Ana M. G. Ferreira +2
Bayesian inference applied to microseismic activity monitoring allows the accurate location of microseismic events from recorded seismograms and the estimation of the associated un…
Accelerating Bayesian microseismic event location with deep learning
A. Spurio Mancini, D. Piras, A. M. G. Ferreira +2
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 pr…