FUZZY QMODEL?A new approach for linear unmixing

Full, William E.; Ehrlich, Robert; Bezdek, James C.

doi:10.1007/bf01032888

Cited by 106 publications

(33 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tefend et al [18] used PVA to evaluate a complex volcanic system involving multiple mixed magma batches; Deering et al [57] used PVA to document the evolution of rhyolites in the Taupo Volcanic Zone in New Zealand; Barclay et al [17] used PVA to evaluate recharge events in the erupting Soufrière Hills Volcano, Montserrat, as evidenced in chemically diverse mafic enclaves. A more comprehensive discussion of the technique and its development can be found in [18,[52][53][54]. In brief, PVA is an oblique factor analysis procedure that uses all available geochemical analytes to simultaneously evaluate a group of samples that result from mixing (or "unmixing," such as the case of fractional crystallization [19]).…”

Section: Polytopic Vector Analysis (Pva)mentioning

confidence: 99%

“…Polytopic vector analysis (PVA) is a multivariate statistical program for analyzing sample suites that result from mixing of two or more end members [51][52][53]. Although PVA has been used for several decades to evaluate source mixing in cases of environmental contamination [54][55][56], its application to igneous petrology is fairly new.…”

Section: Polytopic Vector Analysis (Pva)mentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Complex Magma Mixing via Polytopic Vector Analysis (PVA) in the Papagayo Tuff, Northern Costa Rica: Processes that Form Continental Crust

et al. 2013

View full text Add to dashboard Cite

Over the last forty years, research has revealed the importance of magma mixing as a trigger for volcanic eruptions, as well as its role in creating the diversity of magma compositions in arcs. Sensitive isotopic and microchemical techniques can reveal subtle evidence of magma mixing in igneous rocks, but more robust statistical techniques for bulk chemical data can help evaluate complex mixing relationships. Polytopic vector analysis (PVA) is a multivariate technique that can be used to evaluate suites of samples that are produced by mixing of two or more magma batches. The Papagayo Tuff of the Miocene-Pleistocene Bagaces Formation in northern Costa Rica is associated with a segment of the Central American Volcanic Arc. While this segment of the arc is located on oceanic plateau, recent (<8 Ma) ignimbrites bear the chemical signatures of upper continental crust, marking the transition from oceanic to continental crust. The Papagayo Tuff contains banded pumice fragments consistent with one or more episodes of mixing/mingling to produce a single volcanic deposit. The PVA solution for the sample set is consistent with observations from bulk chemistry, microchemistry and petrographic data from the rocks. However, without PVA, the unequivocal identification of the three end-member solution would not have been possible

show abstract

Section: Polytopic Vector Analysis (Pva)mentioning

confidence: 99%

Section: Polytopic Vector Analysis (Pva)mentioning

confidence: 99%

Evaluating Complex Magma Mixing via Polytopic Vector Analysis (PVA) in the Papagayo Tuff, Northern Costa Rica: Processes that Form Continental Crust

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Principal component analysis has also been used to select 'end-member' spectra from the higher order principal components for subsequent use in the linear spectral unmixing model (Full et al 1982;Bateson and Curtiss 1996;Rossel et al 2006). An end-member can be defined as a spectrally pure pixel that is used to describe other mixed pixels in the scene (Tompkins et al 1997).…”

Section: Principal Component Analysismentioning

confidence: 99%

Estimating soil organic carbon from soil reflectance: a review

et al. 2009

View full text Add to dashboard Cite

Soil organic carbon (SOC) concentration is a useful soil property with which to guide agricultural applications of chemical inputs. To enable this, simple, accurate, rapid and inexpensive methods are needed to produce maps of surface SOC concentrations. Researchers have investigated estimates of soil surface properties from remotely sensed information as a means of rapidly quantifying and monitoring some surface soil properties, such as SOC. The objective of this paper is to review the potential and limitations of remotely sensed data for mapping and evaluating SOC. Several statistical methods including simple regression models, the 'soil line' approach, principal component analysis and geostatistics have been applied to data to investigate the accuracy of such estimates. A review of the literature shows that predictive equations are not universal and require new regression models for every scene. An important benefit of remotely sensed data is to suggest a sampling strategy that can lead to improved representation of spatial heterogeneity in SOC.

show abstract

“…The exploratory data analysis method used in this study is Polytopic Vector Analysis (PVA) (Full et al, 1981(Full et al, , 1982Johnson et al, 2007) which is coded into the program SAWVEC. This method has been used in a variety of geologic and environmental chemical studies, including petroleum geochemistry (Collister et al, 2004).…”

Section: Unmixing Evaluationmentioning

confidence: 99%