scholarly journals AI oriented prospectivity mapping to study relationships between Sb mineralization and geological framework

2021 ◽  
Author(s):  
Alex Vella ◽  
Charles Gumiaux ◽  
Guillaume Bertrand ◽  
Bruno Tourlière ◽  
Eric Gloaguen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Scales ◽  
Mineral Exploration ◽  
Data Driven ◽  
Automated Classification ◽  
Geological Features ◽  
Data Driven Approach ◽  
Statistical Relationships ◽  
Using Data ◽  
Prospectivity Mapping

<p>Prospectivity mapping aims at producing favorability maps, outlining areas with the highest likelihood to host mineralization. This process can be done using data-driven approaches, based on statistical and spatial analyses on geological features and known mineral occurences. Besides, such approach contributes to better understand metallogenic processes by highlighting specific and systematic associations between deposits and geological features (structures, lithologies, contacts, geophysical anomalies, etc).</p><p>As part of the AUREOLE project, prospectivity maps of Sb throughout the West European Variscan Range are being produced using CBA (“Cell-Based Associations”). CBA is a prospectivity tool developped for mineral prospectivity mapping by the French Geological Survey (BRGM). This method divide at first space into a regular cells grid. Inside each cell, the associations of geological factors, such as lithological, structural, geophysical or geochemical features, are grouped together and define the geological framework in the vicinity of the given cell. This project aims at developing and improving this method by the addition of new machine learning methods and statistical and spatial analysis tools for the automated classification and the calculation of favorability score.</p><p>Application of this approach to the Ibero-Armorican Arc, relying heavily on Artificial Intelligence to process the data, will highlight statistical relationships between the Sb deposits and their surrounding geological framework. Computations will be performed at multiple scales and in different areas trough the Arc, in order to observe the influence of scale in the consistency of the results and to bring out general laws from local specificities in the metallogenic models. Results from this almost purely data-driven approach will be compared to the metallogenical models traditionnaly proposed for Sb deposits in the studied areas. We infer this new multiscale and multidomains study  would improve our understanding of the genetic processes resulting in  Sb deposits through the Variscan Range and give new metallotects or specify the common ones, to be used for mineral exploration purpose.</p><p>This Phd work is funded by the ERA-MIN2 AUREOLE project (ANR-19-MIN2-0002, https://aureole.brgm.fr).</p><p><strong>Keywords: </strong>Antimony, Prospective Mapping, Machine Learning, Data-Driven.</p>

Data-Driven Approach for Analyzing Hydrogeology and Groundwater Quality Across Multiple Scales

Ground Water ◽  
2017 ◽  
Vol 56 (3) ◽  
pp. 377-398 ◽  
Author(s):  
Zachary K. Curtis ◽  
Shu-Guang Li ◽  
Hua-Sheng Liao ◽  
David Lusch
Keyword(s):  
Groundwater Quality ◽  
Multiple Scales ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

2020 ◽  
Vol 214 ◽  
pp. 01023
Author(s):  
Linan (Frank) Zhao
Keyword(s):  
Machine Learning ◽  
Age Groups ◽  
Learning Models ◽  
Public Authorities ◽  
Ensemble Machine Learning ◽  
European Public ◽  
Data Driven Approach ◽  
Using Data ◽  
Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

A note on no-free-lunch theorem

2021 ◽  
Author(s):  
Lidong Wu
Keyword(s):  
Machine Learning ◽  
Theoretical Result ◽  
Data Driven ◽  
Np Hard ◽  
Free Lunch ◽  
No Free Lunch Theorem ◽  
Data Driven Approach ◽  
Hard Problems ◽  
No Free Lunch ◽  
Np Hard Problems

The No-Free-Lunch theorem is an interesting and important theoretical result in machine learning. Based on philosophy of No-Free-Lunch theorem, we discuss extensively on the limitation of a data-driven approach in solving NP-hard problems.

scholarly journals Predicting the state of charge and health of batteries using data-driven machine learning

2020 ◽  
Vol 2 (3) ◽  
pp. 161-170 ◽  
Author(s):  
Man-Fai Ng ◽  
Jin Zhao ◽  
Qingyu Yan ◽  
Gareth J. Conduit ◽  
Zhi Wei Seh
Keyword(s):  
Machine Learning ◽  
The State ◽  
State Of Charge ◽  
Data Driven ◽  
Using Data

scholarly journals Data-driven studies of magnetic two-dimensional materials

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Trevor David Rhone ◽  
Wei Chen ◽  
Shaan Desai ◽  
Steven B. Torrisi ◽  
Daniel T. Larson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Density Functional ◽  
Magnetic Coupling ◽  
Magnetic Ordering ◽  
Magnetic Behavior ◽  
Data Driven ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Data Driven Approach

Abstract We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form $$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$ A 2 B 2 X 6 , based on the known material $$\hbox {Cr}_2\hbox {Ge}_2\hbox {Te}_6$$ Cr 2 Ge 2 Te 6 , using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.

Modeling the Gas Exchange Processes of a Modern Diesel Engine With an Integrated Physics-Based and Data-Driven Approach

2019 ◽  
Author(s):  
Jorge Pulpeiro Gonzalez ◽  
King Ankobea-Ansah ◽  
Elena Escuder Milian ◽  
Carrie M. Hall
Keyword(s):  
Diesel Engine ◽  
Gas Exchange ◽  
High Efficiency ◽  
Direct Injection ◽  
Data Driven ◽  
Model Framework ◽  
Variable Valve Actuation ◽  
Exchange Processes ◽  
Data Driven Approach ◽  
Using Data

Abstract The gas exchange processes of engines are becoming increasingly complex since modern engines leverage technologies including variable valve actuation, turbochargers, and exhaust gas recirculation. Control of these many devices and the underlying gas flows is essential for high efficiency engine concepts. If these processes are to be controlled and estimated using model-based techniques, accurate models are required. This work explores a model framework that leverages a data-driven model of the turbocharger along with submodels of the intercooler, intake and exhaust manifolds and engine processes to provide cylinder-specific predictions of the pressure and temperatures of the gases across the system. This model is developed and validated using data from a 2.0 liter VW turbocharged, direct-injection diesel engine and shown to provide accurate prediction of critical gas properties.

A Data-Driven Approach using Machine Learning to Enable Real-Time Flight Path Planning

2020 ◽  
Author(s):  
Jung-Hyun Kim ◽  
Simon I. Briceno ◽  
Cedric Y. Justin ◽  
Dimitri Mavris
Keyword(s):  
Machine Learning ◽  
Path Planning ◽  
Real Time ◽  
Flight Path ◽  
Data Driven ◽  
Data Driven Approach
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