2017
DOI: 10.1111/jmg.12282
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Progressive evolution of whole‐rock composition during metamorphism revealed by multivariate statistical analyses

Abstract: The geochemical evolution of metamorphic rocks during subduction-related metamorphism is described on the basis of multivariate statistical analyses. The studied data set comprises a series of mapped metamorphic rocks collected from the Sanbagawa metamorphic belt in central Shikoku, Japan, where metamorphic conditions range from the pumpellyite-actinolite to epidote-amphibolite facies. Recent progress in computational and information science provides a number of algorithms capable of revealing structures in la… Show more

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Cited by 18 publications
(12 citation statements)
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“…Correspondingly, researchers in other disciplines may read the original article or the citations of the algorithm, and computer scientists may collaborate with researchers from other disciplines and pass different algorithms to them. For example, in a recent study (Yoshida, Kuwatani, Hirajima, Iwamori, & Akaho, 2018) about the evolution of whole‐rock composition during metamorphism, LDA was introduced into geology and used to find endmembers based on the frequencies of elements that make up the rock of interest. Most of the authors, Yoshida, Kuwatani, Hirajima, and Iwamori, are scientists from geology or earth science, while one of them, Shotaro Akaho, is from computer science.…”
Section: Resultsmentioning
confidence: 99%
“…Correspondingly, researchers in other disciplines may read the original article or the citations of the algorithm, and computer scientists may collaborate with researchers from other disciplines and pass different algorithms to them. For example, in a recent study (Yoshida, Kuwatani, Hirajima, Iwamori, & Akaho, 2018) about the evolution of whole‐rock composition during metamorphism, LDA was introduced into geology and used to find endmembers based on the frequencies of elements that make up the rock of interest. Most of the authors, Yoshida, Kuwatani, Hirajima, and Iwamori, are scientists from geology or earth science, while one of them, Shotaro Akaho, is from computer science.…”
Section: Resultsmentioning
confidence: 99%
“…The topic model [16] is a ubiquitous learning machine used in many research areas, including text mining [9,17], computer vision [25], marketing research [40], and geology [51]. The topic model is also known as latent Dirichlet allocation (LDA) [9] in the Bayesian terminology.…”
Section: Topic Modelmentioning
confidence: 99%
“…Latent Dirichlet allocation (LDA) [7] is one of topic models [10] which is a ubiquitous statistical model used in many research areas. Text mining [7,11], computer vision [18], marketing research [24], and geology [34] are such examples. LDA had been originally proposed for natural language processing and it can extract an essential information from documents by defining the topics of the words.…”
Section: Introductionmentioning
confidence: 99%
“…Multivariate statistical analysis can be a powerful tool for detecting the characteristic features of multidimensional geochemical datasets, and it is employed in areas of study including environmental sciences, petrolo-gy, and resource exploration (e.g., Templ et al, 2008;Kuwatani et al, 2014;Ofner et al, 2015;Yasukawa et al, 2016;Ueki and Iwamori, 2017;Kuwatani et al, 2018;Ueki et al, 2018;Yoshida et al, 2018). Geochemical data including chemical composition, petrographic description, and location coordinates are increasingly provided in the literature, making them available for analysis approaches that deal with large-scale, multidimensional data (Igarashi et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…Most geologic samples have their sample location as potential information that is generally provided as two-dimensional longitude/latitude-coordinates. Yoshida et al (2018) performed geocoding of the sampling map from the literature and statistical analyses on the corresponding geochemical data, comparing the clustering results from the geochemical data and its areal distributions. Recently, Haraguchi et al (2017) performed geocoding of published geochemical data since the 1980s and constructed a large location-based geochemical database called 'DODAI'.…”
Section: Introductionmentioning
confidence: 99%