Subcompositional Patterns in Cenozoic Volcanic Rocks of Hungary

Martín‐Fernández, Josep Antoni; Barceló‐Vidal, C.; Pawlowsky‐Glahn, Vera; Kovács, Lajos Ó.; Kovács, Gábor

doi:10.1007/s11004-005-7377-5

Cited by 21 publications

(15 citation statements)

References 25 publications

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“…Cluster analysis was performed and Ward's method adapted to compositional data (Aitchison, 1992;Martín-Fernández et al, 1998;Aitchison et al, 2000) was used for the comparison of the assemblages recovered from the MAF and the RTF. Data matrices were first re-expressed using the centered logratio transformation (clr), the null values were replaced using a multiplicative replacement strategy with 0.01 (Martín-Fernández et al, 2003). The clusters are identified by their center and total variance.…”

Section: Compositional Statistical Analysismentioning

confidence: 99%

Correlation and paleoenvironments of middle Paleogene marine beds based on dinoflagellate cysts in southwestern Patagonia, Argentina

Guerstein

Estebenet

Alperín

et al. 2014

Journal of South American Earth Sciences

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Section: Compositional Statistical Analysismentioning

confidence: 99%

Correlation and paleoenvironments of middle Paleogene marine beds based on dinoflagellate cysts in southwestern Patagonia, Argentina

Guerstein

Estebenet

Alperín

et al. 2014

Journal of South American Earth Sciences

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“…chemical elements in the sample being less than the minimum detection value of the detection The existence of a zero value has an important influence on evaluating parameters and analyzing the structure of data, and it can destroy the ability to determine a real relationship between components or variables. In particular, it is not possible to conduct a subsequent logarithmic transformation of data [52,53]. Methods used to manage the effects of zero values (missing values) in data mainly include the correlation variable correction method, the maximum expectation algorithm, and the empirical value method.…”

Section: Data Processingmentioning

confidence: 99%

Application of Chemical Sequence Stratigraphy to the Prediction of Shale Gas Sweet Spots in the Wufeng and Lower Longmaxi Formations within the Upper Yangtze Region

et al. 2022

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Effective shale gas exploration is hindered by the need for obtaining high-resolution correlations between shale strata and the need for classifying shale facies. To address these issues, chemostratigraphy, sequence stratigraphy, and shale gas geology methods were integrated to develop a new method known as “chemical sequence stratigraphy,” which was successfully applied to the Wufeng–Lower Longmaxi Formations in the upper Yangtze region. Well Huadi 1 was used as a case study, and detailed data were acquired. Multivariate statistical analyses were applied to three defined indices having different genetic significance, namely: terrigenous input intensity (TII), authigenic precipitation intensity (API), and organic matter adsorption and reduction intensity (OARI). By analyzing the trends of these three indices, the Wufeng–Lower Longmaxi Formations were divided into five fourth-order chemical sequences (from bottom to top): LCW, MCL1-1, MCL1-2, MCL1-3, and MCL1-4. The geochemical facies were named and classified using the chemical sequence stratigraphic framework. The enrichment factor (EF) transformation of elements was conducted to determine whether an element is rich or deficient. The results showed that the favorable geochemical facies in the well were EF-Al deficient, EF-Ca rich, and EF-V rich. The organic matter content and rock brittle strength were then used as chemical parameters, and it was predicted that the LCW and MCL1-1 chemical sequences most likely comprised shale gas sweet spots. This conclusion is consistent with the drilling results and indicates that our proposed method is effective and reliable. This method is further applied to the Changning Shuanghe section, the Shizhu Liutang section, and sections in the Xindi 1 well in the upper Yangtze region. The comparative study of these four sections showed that LCW and MCL1-1 are the key chemical sequences for shale gas exploration and development in the Wufeng–Lower Longmaxi Formations within the Upper Yangtze region.

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“…Finally, the scores s y and loadings l y from the PC analysis are back-transformed into the clrspace by s z = s y .v' and l z = v.l y for obtaining the compositional biplot representation, as usual. Details on the interpretation of a compositional biplot are discussed in von Eynatten et al (2003) and Martín-Fernández et al (2005).…”

Section: Compositional Biplotmentioning

confidence: 99%