2008 - Fellow of American Geophysical Union (AGU)
His primary areas of investigation include Seismology, Shear, Episodic tremor and slip, Fault and Subduction. His Seismology study is mostly concerned with Aftershock, Focal mechanism, Seismic moment, Seismogram and Hypocenter. His Seismic moment study incorporates themes from Upper and lower bounds, Stress drop, Bandwidth and Scaling.
In Episodic tremor and slip, Gregory C. Beroza works on issues like Seismic hazard, which are connected to Rake, Earthquake hazard and Earthquake swarm. He has included themes like Wave propagation, Geometry and Scale invariance in his Fault study. His studies deal with areas such as Slipping and Plate tectonics as well as Subduction.
Gregory C. Beroza mainly investigates Seismology, Artificial intelligence, Waveform, Induced seismicity and Aftershock. His study in Seismology concentrates on Fault, Subduction, Seismogram, Seismic moment and Seismic wave. Gregory C. Beroza studies Episodic tremor and slip, a branch of Subduction.
His Episodic tremor and slip study frequently links to other fields, such as Shear. His Seismogram study frequently intersects with other fields, such as Geodesy. His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Earthquake detection and Pattern recognition.
His main research concerns Artificial intelligence, Seismology, Deep learning, Artificial neural network and Waveform. His Artificial intelligence research integrates issues from Machine learning, Earthquake detection, Seismogram and Pattern recognition. Gregory C. Beroza integrates several fields in his works, including Seismology and Nucleation.
His Deep learning study also includes
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Waveform, Artificial neural network and Deep learning. The various areas that Gregory C. Beroza examines in his Artificial intelligence study include Earth science, Background noise and Seismogram. His Background noise research is multidisciplinary, relying on both Tectonics, Detector, Hydraulic fracturing, Template matching and Residual.
His study in Seismogram is interdisciplinary in nature, drawing from both Seismic wave, Geodesy, Uncertainty quantification, Epicenter and Mean squared error. Gregory C. Beroza has researched Pattern recognition in several fields, including Colors of noise and Feature. As a part of the same scientific study, he usually deals with the Deep learning, concentrating on Standard deviation and frequently concerns with Earthquake location, Algorithm, Amplitude and Magnitude.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Seismicity Remotely Triggered by the Magnitude 7.3 Landers, California, Earthquake
D. P. Hill;P.A. Reasenberg;A. Michael;W.J. Arabaz.
Non-volcanic tremor and low-frequency earthquake swarms
David R. Shelly;Gregory C. Beroza;Satoshi Ide.
Low-frequency earthquakes in Shikoku, Japan, and their relationship to episodic tremor and slip
David R. Shelly;Gregory C. Beroza;Satoshi Ide;Sho Nakamula.
A scaling law for slow earthquakes
Satoshi Ide;Gregory C. Beroza;David R. Shelly;Takahiko Uchide.
Shallow Dynamic Overshoot and Energetic Deep Rupture in the 2011 Mw 9.0 Tohoku-Oki Earthquake
Satoshi Ide;Annemarie Baltay;Gregory C. Beroza.
A spatial random field model to characterize complexity in earthquake slip
P. Martin Mai;Gregory C. Beroza.
Journal of Geophysical Research (2002)
Does apparent stress vary with earthquake size
Satoshi Ide;Gregory C. Beroza.
Geophysical Research Letters (2001)
Seismic Evidence for an Earthquake Nucleation Phase
W. L. Ellsworth;G. C. Beroza.
Slow Earthquakes and Nonvolcanic Tremor
Gregory C. Beroza;Satoshi Ide.
Annual Review of Earth and Planetary Sciences (2011)
Machine learning for data-driven discovery in solid Earth geoscience
Karianne J. Bergen;Karianne J. Bergen;Paul A. Johnson;Maarten V. de Hoop;Gregory C. Beroza.
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