What is he best known for?
The fields of study he is best known for:
- Seismology
- Artificial intelligence
- Statistics
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.
His most cited work include:
- Seismicity Remotely Triggered by the Magnitude 7.3 Landers, California, Earthquake (695 citations)
- Low-frequency earthquakes in Shikoku, Japan, and their relationship to episodic tremor and slip (588 citations)
- Non-volcanic tremor and low-frequency earthquake swarms (581 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Seismology (62.93%)
- Artificial intelligence (12.74%)
- Waveform (10.81%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (12.74%)
- Seismology (62.93%)
- Deep learning (4.63%)
In recent papers he was focusing on the following fields of study:
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
- Earthquake location which is related to area like Tracking,
- Algorithm which connect with Recurrent neural network, Magnitude, Warning system and Amplitude,
- Uncertainty quantification that intertwine with fields like Epicenter, Mean squared error, Geodesy and Seismic wave.
His Artificial neural network study combines topics in areas such as Real-time computing and Training.
The concepts of his Intermediate depth study are interwoven with issues in Green S and Subduction.
Between 2017 and 2021, his most popular works were:
- Machine learning for data-driven discovery in solid Earth geoscience. (177 citations)
- PhaseNet: a deep-neural-network-based seismic arrival-time picking method (123 citations)
- CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection (68 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Seismology
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.
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