Treffer: Beyond traditional seismology
Titel:
Beyond traditional seismology : harnessing deep learning to analyze seismological data / von Megha Chakraborty
Darin enthalten:
A study on the effect of input data length on a deep-learning-based magnitude classifier / Megha Chakraborty, Wei Li, Johannes Faber, Georg Rümpker, Horst Stoecker and Nishtha Srivastavaeorg
CREIME—A Convolutional Recurrent Model for Earthquake Identification and Magnitude Estimation / Megha Chakraborty, Darius Fenner, Wei Li, Johannes Faber, Kai Zhou, Georg Rümpker, Horst Stoecker, and Nishtha Srivastavaeorg
PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms / Megha Chakraborty, Claudia Quinteros Cartaya, Wei Li, Johannes Faber, Georg Rümpker, Horst Stoecker, Nishtha Srivastavaeorg
A Python package for single-station earthquake monitoring using deep learning / Wei Li, Megha Chakraborty, Claudia Quinteros Cartaya, Jonas Köhler, Johannes Faber, Men-Andrin Meier, Georg Rümpker, Nishtha Srivastava
Feasibility of deep learning in shear wave splitting analysis using synthetic-data training and waveform deconvolution / Megha Chakraborty, Georg Rümpker, Wei Li, Johannes Faber, Frederik Link, Nishtha Srivastava
CREIME—A Convolutional Recurrent Model for Earthquake Identification and Magnitude Estimation / Megha Chakraborty, Darius Fenner, Wei Li, Johannes Faber, Kai Zhou, Georg Rümpker, Horst Stoecker, and Nishtha Srivastavaeorg
PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms / Megha Chakraborty, Claudia Quinteros Cartaya, Wei Li, Johannes Faber, Georg Rümpker, Horst Stoecker, Nishtha Srivastavaeorg
A Python package for single-station earthquake monitoring using deep learning / Wei Li, Megha Chakraborty, Claudia Quinteros Cartaya, Jonas Köhler, Johannes Faber, Men-Andrin Meier, Georg Rümpker, Nishtha Srivastava
Feasibility of deep learning in shear wave splitting analysis using synthetic-data training and waveform deconvolution / Megha Chakraborty, Georg Rümpker, Wei Li, Johannes Faber, Frederik Link, Nishtha Srivastava
Beteiligt:
Veröffentlicht:
Frankfurt am Main, 2024
Vertrieb:
Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg
Umfang:
1 Online-Ressource (xii, 148 Seiten) : Illustrationen, Diagramme
Publikationstyp:
Sprache:
Englisch
Hochschulschrift:
Dissertation, Johann Wolfgang Goethe-Universität in Frankfurt am Main, 2024
Andere Ausgaben:
Erscheint auch als Druck-Ausgabe: Chakraborty, Megha, 1996-. Beyond traditional seismology : harnessing deep learning to analyze seismological data
Anmerkungen:
Literaturverzeichnis: Seite 103-118
Enthält 5 Sonderabdrucke
Enthält 5 Sonderabdrucke
Schlagworte:
DOI:
10.21248/gups.88026
Open Access Rechte:
Open Access
CC BY-NC-ND 4.0
CC BY-NC-ND 4.0