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Found 5 papers, 3 papers with code
Earthquake transformer—an attentive deeplearning model for simultaneous earthquake detection and phase picking
Earthquake signal detection and seismic phase picking are challenging tasks in the processing of noisy data and the monitoring of microearthquakes.
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.
