sigproseismology Goto Github PK

ambition

ambition-noise

Extract Love and Rayleigh waves from ambient-noise data

crustal-data-gulf-of-guinea

data_deep_learning_ghana

data_dl_ghana

gji

GJI paper on Monte Carlo

ipiml

IPIML: A deep-scan earthquake detection and location workflow Integrating Pair-Input deep learning model and Migration Location method

siameseearthquaketransformer

Tutorials and updated codes for the research paper: 'Siamese Earthquake Transformer: A pair-input deep-learning model for earthquake detection and phase picking on a seismic array.'

sp-tff

SP-TFF code package aims to present a methodology for high-resolution polarization analysis and filtering of seismic signals in the TF-domain. The main developments in this research work are: (a) reformulation of the eigenvalue decomposition polarization analysis (EDPA) in TF-domain, (b) combining the SP-TFR to the formulation to obtain high-resolution TF-domains polarization parameters for discriminating nearby seismic phases, and (c) incorporating TF-domain directivity, rectilinearity, and amplitude attributes to extract (or eliminate) different seismic phases. The main focus is to discriminate between Love and Rayleigh from the body and coda waves.