2020
DOI: 10.31223/osf.io/g2dt8
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Machine learning and fault rupture: a review

Abstract: Geophysics has historically been a data-driven field, however in recent years the exponential increase of available data has lead to increased adoption of machine learning techniques and algorithm for analysis, detection and forecasting applications to faulting. This work reviews recent advances in the application of machine learning in the study of fault rupture ranging from the laboratory to Solid Earth.

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“…Previous studies have shown that the presence of the red emitter species in the EL is mainly due to electron-hole recombination in a trap state found in the emissive layer. Among all the possibilities, the most frequently observed factors are attributed to grain boundaries between the nanocrystals [44], or the unbalanced charge carrier mobility (which is usually induced by the presence of deep electronic trap states in the studied materials) [45].…”
Section: Luminescent Thermometer: Stability Tests Of Pellets and Filmsmentioning
confidence: 99%
“…Previous studies have shown that the presence of the red emitter species in the EL is mainly due to electron-hole recombination in a trap state found in the emissive layer. Among all the possibilities, the most frequently observed factors are attributed to grain boundaries between the nanocrystals [44], or the unbalanced charge carrier mobility (which is usually induced by the presence of deep electronic trap states in the studied materials) [45].…”
Section: Luminescent Thermometer: Stability Tests Of Pellets and Filmsmentioning
confidence: 99%