58 citations · 69 across the 4 of their papers we have counts for
4 papers
DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean data
Sacha Lapins, Antony Butcher, J. -Michael Kendall +5
This article presents a weakly supervised machine learning method, which we call DAS-N2N, for suppressing strong random noise in distributed acoustic sensing (DAS) recordings. DAS-…
Ranking earthquake forecasts using proper scoring rules: Binary events in a low probability environment
Francesco Serafini, Mark Naylor, Finn Lindgren +2
Operational earthquake forecasting for risk management and communication during seismic sequences depends on our ability to select an optimal forecasting model. To do this, we need…
High Resolution Long- and Short-Term Earthquake Forecasts for California
M. J. Werner, A. Helmstetter, D. D. Jackson +1
We present two models for estimating the probabilities of future earthquakes in California, to be tested in the Collaboratory for the Study of Earthquake Predictability (CSEP). The…
Comment on "Analysis of the Spatial Distribution between Successive Earthquakes" by Davidsen and Paczuski
M. J. Werner, D. Sornette
By analyzing a southern California earthquake catalog, Davidsen and Paczuski [Phys. Rev. Lett. 94, 048501 (2005)] claim to have found evidence contradicting the theory of aftershoc…