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Dario Jozinović
Dario Jozinović
Verified email at sed.ethz.ch
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Cited by
Year
An investigation of rapid earthquake characterization using single‐station waveforms and a convolutional neural network
A Lomax, A Michelini, D Jozinović
Seismological Research Letters 90 (2A), 517-529, 2019
1292019
Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network
D Jozinović, A Lomax, I Štajduhar, A Michelini
Geophysical Journal International 222 (2), 1379-1389, 2020
962020
Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers
J Münchmeyer, J Woollam, A Rietbrock, F Tilmann, D Lange, T Bornstein, ...
Journal of Geophysical Research: Solid Earth 127 (1), e2021JB023499, 2022
942022
The new ShakeMap in Italy: Progress and advances in the last 10 yr
A Michelini, L Faenza, G Lanzano, V Lauciani, D Jozinović, R Puglia, ...
Seismological Research Letters 91 (1), 317-333, 2020
672020
SeisBench—A toolbox for machine learning in seismology
J Woollam, J Münchmeyer, F Tilmann, A Rietbrock, D Lange, T Bornstein, ...
Seismological Society of America 93 (3), 1695-1709, 2022
622022
INSTANCE–the Italian seismic dataset for machine learning
A Michelini, S Cianetti, S Gaviano, C Giunchi, D Jozinović, V Lauciani
Earth System Science Data 13 (12), 5509-5544, 2021
552021
Graph neural networks for multivariate time series regression with application to seismic data
S Bloemheuvel, J van den Hoogen, D Jozinović, A Michelini, ...
International Journal of Data Science and Analytics 16 (3), 317-332, 2023
40*2023
Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data
D Jozinović, A Lomax, I Štajduhar, A Michelini
Geophysical Journal International 229 (1), 704-718, 2022
402022
Intra-domain and cross-domain transfer learning for time series data—How transferable are the features?
E Otović, M Njirjak, D Jozinović, G Mauša, A Michelini, I Stajduhar
Knowledge-Based Systems 239, 107976, 2022
372022
Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale
F Magrini, D Jozinović, F Cammarano, A Michelini, L Boschi
Artificial Intelligence in Geosciences 1, 1-10, 2020
322020
The Choice of Time–Frequency Representations of Non-Stationary Signals Affects Machine Learning Model Accuracy: A Case Study on Earthquake Detection from LEN-DB Data
M Njirjak, E Otović, D Jozinović, J Lerga, G Mauša, A Michelini, ...
Mathematics 10 (6), 965, 2022
42022
Introducing a European integrated ShakeMap system
C Cauzzi, J Clinton, L Faenza, S Heimers, MR Koymans, V Lauciani, ...
Seismol. Res. Lett 89 (926), 10.1785, 2018
42018
Which picker fits my data
J Münchmeyer, J Woollam, A Rietbrock, F Tilmann, D Lange, T Bornstein, ...
A quantitative evaluation of deep learning based seismic pickers 127, 2022
22022
Rapid prediction of ground shaking intensity with graph neural networks
S Bloemheuvel, J van den Hoogen, D Jozinović, A Michelini, ...
Proceedings of the Third European Conference on Earthquake Engineering and …, 2022
12022
Seismogram waveform datasets for ConvNetQuake_INGV
A Lomax, D Jozinović, E Otović, A Michelini
12021
Improving template matching detections using a Convolutional Neural Network
D Jozinović, T Toledo, V Simon, T Kraft
EGU24, 2024
2024
Exploring the Potential of Deep Learning models in Focal Mechanism Computation: A Case Study from Switzerland
M Mesimeri, D Jozinovic, MA Meier, T Diehl, J Clinton, S Wiemer
EGU24, 2024
2024
Towards a dynamic earthquake risk framework for Switzerland
M Böse, L Danciu, A Papadopoulos, J Clinton, C Cauzzi, I Dallo, L Mizrahi, ...
Natural Hazards and Earth System Sciences 24 (2), 583-607, 2024
2024
Modeling the Asymptotic Behavior of Higher-Order Aftershocks with Deep Learning
L Mizrahi, D Jozinović
arXiv preprint arXiv:2401.06075, 2024
2024
Seismological Oriented Machine lEarning (SOME) project
C Giunchi, M Bagagli, S Cianetti, S Gaviano, D Jozinović, V Lauciani, ...
EGU General Assembly Conference Abstracts, EGU-15105, 2023
2023
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