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Mutual complement between statistical and neural network approaches for rock magnetism data analysis

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Mutual complement between statistical and neural network approaches for rock magnetism data analysis
นักวิจัย : Guo, Wanwu. , Li, Michael M. , Whymark, Greg. , Li, Zheng-Xiang.
คำค้น : Neural networks (Computer science) , Computer science. , Computer simulation. , Magnetite. , Applied research. , 890205 Information Processing Services (incl. Data Entry and Capture) , 080108 Neural, Evolutionary and Fuzzy Computation. , 080110 Simulation and Modelling. , 080199 Artificial Intelligence and Image Processing not elsewhere classified. , Neural networks -- Multilayer perceptron -- Correlation analysis -- Rock magnetism
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2552
อ้างอิง : http://hdl.cqu.edu.au/10018/40625 , http://dx.doi.org/10.1016/j.eswa.2008.11.045 , cqu:5173
ที่มา : Guo, WW, Li, M, Whymark, G & Li, Z 2009, 'Mutual complement between statistical and neural network approaches for rock magnetism data analysis', Expert Systems with Applications, vol. 36, no. 6, pp.9678-9682.http://dx.doi.org/10.1016/j.eswa.2008.11.045 (viewed 12/2/10)
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Expert systems with applications. Netherlands. : Elsevier, 2009. Vol. 36, issue 6 (August 2009), p. 9678-9682 5 pages Refereed 0957-4174 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

Interpretation of magnetic phenomena in rock magnetism requires a good understanding in relationship between magnetic susceptibility and magnetic minerals, particularly magnetite, contained in rocks. Previous studies emphasized on describing such a correlation using a sole expression through statistical analysis. The resultant correlations are generally useful only in qualitative interpretation, but too coarse to simulate quantitative solutions. In this paper, we combine the correlation analysis with neural network techniques to not only identify the correlations between susceptibility and magnetite in rocks but also simulate accurate susceptibilities with respect to the magnetite contents provided. Our study has demonstrated that multilayer perceptron models are capable of producing accurate mappings between susceptibility and magnetite in rocks. However, correlation analysis provides qualitative interpretation for rock magnetism data in identifying the patterns of magnetic behaviours of the rocks. In quantitative simulation, if the required accuracy is not restricted, a general MLP model with existence of noises in training data is the first choice because it does not require statistical data pre-processing for establishing the NN model. If the simulation is to provide solutions as accurate as possible, the MLP model must be trained by noise-filtered datasets. The noise filtering is based on the preliminary correlation analysis. Therefore, these two approaches are mutually complementary, rather than competitive to each other.

บรรณานุกรม :
Guo, Wanwu. , Li, Michael M. , Whymark, Greg. , Li, Zheng-Xiang. . (2552). Mutual complement between statistical and neural network approaches for rock magnetism data analysis.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Guo, Wanwu. , Li, Michael M. , Whymark, Greg. , Li, Zheng-Xiang. . 2552. "Mutual complement between statistical and neural network approaches for rock magnetism data analysis".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Guo, Wanwu. , Li, Michael M. , Whymark, Greg. , Li, Zheng-Xiang. . "Mutual complement between statistical and neural network approaches for rock magnetism data analysis."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2552. Print.
Guo, Wanwu. , Li, Michael M. , Whymark, Greg. , Li, Zheng-Xiang. . Mutual complement between statistical and neural network approaches for rock magnetism data analysis. กรุงเทพมหานคร : Central Queensland University, Australia; 2552.