output
20052022
most citedWhich picker fits my data? A quantitative evaluation of deep learning based seismic pickers

213 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2019Show all

5 papers · 1 filter

cs.NE2019★ 134 cited

Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations

Arnaud Mignan, Marco Broccardo

In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Eart…

stat.ML2019★ 1 cited

A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis

Ziqi Wang, Marco Broccardo

This paper proposes an active learning-based Gaussian process (AL-GP) metamodelling method to estimate the cumulative as well as complementary cumulative distribution function (CDF…

physics.geo-ph2019★ 27 cited

A generalized theory for full microtremor horizontal-to-vertical [H/V (z, f)] spectral ratio interpretation in offshore and onshore environments

Agostiny Marrios Lontsi, Antonio García-Jerez, Juan Camilo Molina-Villegas +6

Advances in the field of seismic interferometry have provided a basic theoretical interpretation to the full spectrum of the microtremor horizontal-to-vertical spectral ratio [H/V(…

physics.geo-ph2019★ 30 cited

One neuron is more informative than a deep neural network for aftershock pattern forecasting

Arnaud Mignan, Marco Broccardo

29 August 2018: "Artificial intelligence nails predictions of earthquake aftershocks". This Nature News headline is based on the results of DeVries et al. (2018) who forecasted the…

physics.geo-ph2019★ 27 cited

Forecasting the rates of future aftershocks of all generations is essential to develop better earthquake forecast models

Shyam Nandan, Guy Ouillon, Didier Sornette +1

Currently, one of the best performing and most popular earthquake forecasting models rely on the working hypothesis that: "locations of past background earthquakes reveal the proba…