Automated determination of P-phase arrival times at regional and local distances using higher order statistics

By L. Küperkoch, T. Meier, J. Lee, W. Friederich. 2010

150 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/05e361c80977e4f1e5b247af0822e8a1a664435e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gregory C. Beroza
2. Weiqiang Zhu
3. S. M. Mousavi
4. Jianwei Ma
5. Siwei Yu
6. Y. Sheng

In Geophysics

1. Gregory C. Beroza
2. Weiqiang Zhu
3. S. M. Mousavi
4. Siwei Yu
5. Jianwei Ma
6. Y. Sheng
7. P. Martin Mai
8. M. Guatteri

Citations in the corpus

• PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method (2018, methodology): “Approaches based on higher-order statistics (HOS), including kurtosis and skewness, were developed to identify the transition from Gaussianity to non-Gaussianity, which coincides with the onset of the seismic event, even in the presence of noise (Saragiotis et al. 2002; K¨uperkoch et al. 2010).”