Geochemical Discrimination of Monazite Source Rock Based on Machine Learning Techniques and Multinomial Logistic Regression Analysis

Itano, Keita; Ueki, Keisuke; Iizuka, Tsuyoshi; Kuwatani, Tatsu

doi:10.3390/geosciences10020063

Cited by 41 publications

(17 citation statements)

References 56 publications

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“…Recently, various studies demonstrated the potentials of machine learning (ML) in the solution of petro‐volcanological problems (Bolton et al, 2020; Caricchi et al, 2020; Itano et al, 2020; Li et al, 2020; Petrelli et al, 2017; Petrelli & Perugini, 2016; Ren et al, 2019; Ueki et al, 2020). One common feature for ML models is that they do not need to solve complex problems using an a priori defined conceptual model (e.g., the definition of a thermodynamic reaction) but they can follow a data‐driven approach (Caricchi et al, 2020; Hazen, 2014; Hazen et al, 2019; Morrison et al, 2017) and unravel complexities in large data sets through a so‐called learning process (Shai & Shai, 2014).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas

2020

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We introduce a new approach, based on machine learning, to estimate pre-eruptive temperatures and storage depths using clinopyroxene-melt pairs and clinopyroxene-only chemistry. The model is calibrated for magmas of a wide compositional range, it complements existing models, and it can be applied independently of tectonic setting. Additionally, it allows the identification of the main chemical exchange mechanisms occurring in response to pressure and temperature variations on the base of experimental data without a priori assumptions. After the validation process, performances are assessed with test data never used during the training phase. We estimate the uncertainty using the root-mean-square error (RMSE) and the coefficient of determination (R 2). The application of the best performing algorithm (trained in the range 0-40 kbar and 952-1882 K) to clinopyroxene-melt pairs from primitive to extremely differentiated magmas of both subalkaline and alkaline systems returns a RMSE on the order of 2.6 kbar and 40 K for pressure and temperature, respectively. We additionally present a melt-and temperature-independent clinopyroxene barometer in the range 0-40 kbar, characterized by a RMSE of the order of 3 kbar. Tested for tholeiitic compositions in the range 0-10 kbar, the melt-and temperature-independent clinopyroxene barometer has a RMSE of 1.7 kbar. We finally apply the proposed approach to clinopyroxenes from Iceland, providing new, independent, insights about pre-eruptive storage depths of Icelandic volcanoes. The general applicability of this model will promote the comparison between the architecture of plumbing systems across tectonic settings and facilitate the comparison between petrologic and geophysical studies.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas

2020

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“…The key mechanical properties of the native articular cartilages were extracted from the literature and summarized in table 3. The tensile strength, tensile modulus and elongation of the natural cartilages reported in table 3 are 35 MPa, 3-100 MPa and 2-140%, respectively 59 . However, under 15% less strain, the tensile modulus reaches only up to 5 to 10 MPa [44].…”

Section: Database Creationmentioning

confidence: 94%

Accelerated Discovery of the Polymer Matrix for Cartilage Repair Through Machine Learning Algorithms

Mairpady

Mourad

Mozumder

2021

Preprint

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Cartilage repair is one of the most challenging tasks for the orthopedic surgeons and researchers. The primary challenge lies on the fact that the development of the extracellular matrixes requires specialized cells known as chondrocytes which are sparse in numbers. Chondrocytes’ minimal self-renewal capacity makes it further troublesome and expensive to repair the cartilages. In designing successful substitutes for the cartilages, the selection of materials used for the scaffold fabrication plays the central role among several other important factors in order to ensure the success of the survival and proliferation of any biomaterial substitutes. Since last few decades, polymer and polymers' combination have been extensively used to fabricate such scaffolds and have shown promising results in terms of mechanical integrity and biocompatibility. In an empirical approach, the selection of the most appropriate polymer(s) for cartilage repair is an expensive and time-consuming affair, as traditionally, it requires numerous trials. Moreover, it is humanly impossible to go through the huge library of literature available on the potential polymer(s) and to correlate their physical, mechanical and biological properties that might be suitable for cartilage tissue engineering. With the advancement of machine learning, material design may experience a significant reduction in experimental time and cost. The objective of this study is to implement an inverse design approach to select the best polymer(s) or composites for cartilage repair by using the machine learning algorithms, such as random forest regression (i.e., regression trees) and the multinomial logistic regression. In these algorithms, the mechanical properties of the polymers, which are similar to the cartilages, are considered as the input and the polymer(s)/composites are the predicted output. According to the random forest regression and multinomial logistic regression, the polymer(s)/composites (i.e., the output) having the closest characteristics of the articular cartilages were found to be a composite of polycaprolactone and poly(bisphenol A carbonate) and a blend of polyethylene/polyethylene-graft-poly(maleic anhydride), respectively. These composites exhibit similar biomechanical properties of the natural cartilages and initiate only minimal immune responses in the body environment.

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“…The chemical composition of the monazite mineral determined using ED-XRF is given in Table [67]. It was proved that monazite chemical composition can be used as proxy of provenance and source rock type [68,69]. The variation in rare earth, Th, and U content is due to the presence of rocks act as provenance for heavy minerals, particularly monazite mineral.…”

Section: Geochemistry Of Monazitementioning

confidence: 99%

Monazite chemistry and its distribution along the coast of Neendakara–Kayamkulam belt, Kerala, India

et al. 2020

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The beach sands along the coasts of India contain large reserves of strategic minerals like ilmenite, monazite, zircon, etc. Monazite mineral is primarily used as an ore for extracting rare earths particularly cerium and lanthanum. In the present study, the chemistry and distribution of monazite found in the beach sands of Neendakara-Kayamkulam belt in Kerala, south India, is studied using advanced techniques and standardized methods. Beach sediments were collected and analyzed for the texture, and mineralogy reveals that the study area contains characteristically fine sand with maximum 98.9% total heavy minerals (THM) content with ilmenite as predominant mineral species. The content of monazite ranges from 0.1 to 1.4%. A combination of unit operations like gravity separation, magnetic separation and electrostatic separation techniques were applied to the beach sand to recover monazite mineral. The data indicate that samples show a maximum yield of about 81% for THM and 0.5% for monazite. The ED-XRF, XRD and SEM-EDS analysis gives reliable results on the chemistry of monazite. The major chemical constituents of monazite mineral like Ce 2 O 3 , La 2 O 3 , P 2 O 5 corresponds to 26.658, 13.421 and 23.649%, respectively. The SEM reveals the weathering mechanism, both mechanical and chemical, occurred in monazite mineral. Advanced characterization of monazite mineral will influence positively the efficiency of determining their potential applications.

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Geochemical Discrimination of Monazite Source Rock Based on Machine Learning Techniques and Multinomial Logistic Regression Analysis

Cited by 41 publications

References 56 publications

Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas

Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas

Accelerated Discovery of the Polymer Matrix for Cartilage Repair Through Machine Learning Algorithms

Monazite chemistry and its distribution along the coast of Neendakara–Kayamkulam belt, Kerala, India

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