39 citations · 55 across the 6 of their papers we have counts for
6 papers
Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty
Stephen Brown, William L. Rodi, Marco Seracini +5
We consider the application of machine learning to the evaluation of geothermal resource potential. A supervised learning problem is defined where maps of 10 geological and geophys…
Bayesian inference under model misspecification using transport-Lagrangian distances: an application to seismic inversion
Andrea Scarinci, Michael Fehler, Youssef Marzouk
Model misspecification constitutes a major obstacle to reliable inference in many inverse problems. Inverse problems in seismology, for example, are particularly affected by misspe…
Deep learning for low-magnitude earthquake detection on a multi-level sensor network
Ahmed Shaheen, Umair bin Waheed, Michael Fehler +2
Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe.…
Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands
Umair bin Waheed, Ahmed Shaheen, Mike Fehler +1
Deep learning is fast emerging as a potential disruptive tool to tackle longstanding research problems across the sciences. Notwithstanding its success across disciplines, the rece…
Characterizing the nonlinear interaction of S- and P-waves in a rock sample
Thomas Gallot, Alison Malcolm, Thomas L. Szabo +3
The nonlinear elastic response of rocks is known to be caused by the rocks' microstructure, particularly cracks and fluids. This paper presents a method for characterizing the nonl…
Scholte waves generated by seafloor topography
Yingcai Zheng, Xinding Fang, Jing Liu +1
Seafloor topography can excite strong interface waves called Scholte waves that are often dispersive and characterized by slow propagation but large amplitude. This type of wave ca…