Search Results for author:
Found 6 papers, 3 papers with code
Earthquake Phase Association with Graph Neural Networks
The advent of deep learning pickers, which provide high rates of picks from closely overlapping small magnitude earthquakes, motivates revisiting the phase association problem and approaching it using the methods of deep learning.
Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations
The network estimates epicentral distance and P travel time with absolute mean errors of 0. 23 km and 0. 03 s respectively, along with their epistemic and aleatory uncertainties.
A Machine-Learning Approach for Earthquake Magnitude Estimation
In this study we develop a single-station deep-learning approach for fast and reliable estimation of earthquake magnitude directly from raw waveforms.
Seismic Signal Denoising and Decomposition Using Deep Neural Networks
We demonstrate the effect of our method on improving earthquake detection.
Geophysics Signal Processing
CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on a single station.
PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method
As the number of seismic sensors grows, it is becoming increasingly difficult for analysts to pick seismic phases manually and comprehensively, yet such efforts are fundamental to earthquake monitoring.
Geophysics Applications
