Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

2022 ◽  
Vol 114 ◽  
pp. 103562
Author(s):  
Omotayo A. Omosebi ◽  
Curtis M. Oldenburg ◽  
Matthew Reagan
Keyword(s):  
Deep Learning ◽  
Carbon Storage ◽  
Proxy Models

Reviews

2020 ◽  
Vol 39 (3) ◽  
pp. 214-216
Author(s):  
William R. Green ◽  
Patrick Taylor ◽  
Sven Treitel ◽  
Moritz Fliedner
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Carbon Storage ◽  
Ambient Noise ◽  
Cambridge University ◽  
Seismic Ambient Noise ◽  
Caprock Integrity ◽  
American Geophysical ◽  
American Geophysical Union

Geophysics and Geosequestration, edited by Thomas L. Davis, Martin Landr⊘, and Malcolm Wilson, ISBN 978-1-107-13749-3, 2019, Cambridge University Press, 388 p. Geological Carbon Storage: Subsurface Seals and Caprock Integrity, edited by Stéphanie Vialle, Jonathan Ajo-Franklin, and J. William Carey, ISBN 978-1-119-11864-0, 2018, American Geophysical Union and Wiley, 364 p. Introduction to Deep Learning, by Eugene Charniak, ISBN 978-0-262-03951-2, 2019, MIT Press, 192 p. Foundations of Machine Learning, second edition, by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar, ISBN 978-0-262-03940-6, 2018, MIT Press, 504 p. Seismic Ambient Noise, edited by Nori Nakata, Lucia Gualtieri, and Andreas Fichtner, ISBN 978-1-108-41708-2, 2019, Cambridge University Press, 370 p.

PROcess-guided deep learning and DAta-driven modelling (PRODA) uncovers key mechanisms underlying global soil carbon storage

2021 ◽  
Author(s):  
Feng Tao ◽  
Yuanyuan Huang ◽  
Bruce A. Hungate ◽  
Xingjie Lu ◽  
Toby D. Hocking ◽  
...  
Keyword(s):  
Deep Learning ◽  
Organic Carbon ◽  
Spatial Variation ◽  
Soil Carbon ◽  
Carbon Storage ◽  
Data Driven ◽  
Soil Carbon Storage ◽  
Community Land Model ◽  
Global Soil ◽  
Primary Driver

<p>Soil carbon storage is a vital ecosystem service. Yet mechanisms that regulate global soil organic carbon (SOC) dynamics remain elusive. Here we explicitly retrieve the spatial patterns of key biogeochemical mechanisms and their regulation pathways on SOC storage using the novel PROcess-guided deep learning and Data-driven modelling (PRODA) approach. PRODA integrates data assimilation, deep learning, big data with 54,073 globally distributed vertical SOC profiles, and the Community Land Model version 5 (CLM5) to best represent and understand global soil carbon cycle. The PRODA-optimised CLM5 can represent 56±2% spatial variation of SOC across the world. Among all the mechanisms we explored in this study, microbial carbon use efficiency (CUE) emerges as the most critical regulator of global SOC storage. Increasing CUE, where more carbon flow is channelled into stabilisation, coincides with decreasing temperature and favours SOC accrual. Global sensitivity analysis further confirms the CUE, surpassing carbon input and decomposition, as the primary driver to SOC storage and its spatial variation. An increase of CUE by 1% from its standing value will lead to an additional 76±3 petagrams global SOC accumulation. We conclude that how efficiently soil microorganisms utilise organic carbon in metabolism is central to global SOC stabilisation. Understanding detailed processes underlying CUE and its environmental dependence will be critical in accurately describing soil carbon dynamics and its feedbacks to climate change.</p>

scholarly journals Plant Structure and Carbon Storage Assessment Utilizing Drone-Borne Lidar and Deep Learning Technologies in a Danish Agricultural Expanse.

2021 ◽  
Author(s):  
Katerina Trepekli ◽  
Jaime Caballer Revenga ◽  
Stefan Oehmcke ◽  
Fabian Gieseke ◽  
Rasmus Jensen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carbon Storage ◽  
Learning Technologies ◽  
Plant Structure

scholarly journals Plant Structure and Carbon Storage Assessment Utilizing Drone-Borne Lidar and Deep Learning Technologies in a Danish Agricultural Expanse.

2021 ◽  
Author(s):  
Katerina Trepekli ◽  
Jaime Caballer Revenga ◽  
Stefan Oehmcke ◽  
Fabian Gieseke ◽  
Rasmus Jensen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carbon Storage ◽  
Learning Technologies ◽  
Plant Structure

scholarly journals A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage

2021 ◽  
Author(s):  
Varvara Zemskova ◽  
Tai-Long He ◽  
Zirui Wan ◽  
Nicolas Grisouard
Keyword(s):  
Deep Learning ◽  
Southern Ocean ◽  
Carbon Storage ◽  
Ocean Carbon

scholarly journals A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage

2021 ◽  
Vol 112 ◽  
pp. 103488
Author(s):  
Hewei Tang ◽  
Pengcheng Fu ◽  
Christopher S. Sherman ◽  
Jize Zhang ◽  
Xin Ju ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Assimilation ◽  
Carbon Storage ◽  
Geologic Carbon Storage ◽  
Commercial Scale

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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