Lithology identification using kernel Fisher discriminant analysis with well logs
Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature s...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dong, Shaoqun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Iterated Gilbert mosaics - Baccelli, Francois ELSEVIER, 2019, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:143 ; year:2016 ; pages:95-102 ; extent:8 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.petrol.2016.02.017 |
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ELV024582506 |
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| 520 | |a Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. | ||
| 520 | |a Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. | ||
| 650 | 7 | |a Lithology identification |2 Elsevier | |
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| 650 | 7 | |a Kernel Fisher discriminant analysis |2 Elsevier | |
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| 700 | 1 | |a Zeng, Lianbo |4 oth | |
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10.1016/j.petrol.2016.02.017 doi GBVA2016014000015.pica (DE-627)ELV024582506 (ELSEVIER)S0920-4105(16)30061-4 DE-627 ger DE-627 rakwb eng 660 660 DE-600 510 VZ 31.70 bkl Dong, Shaoqun verfasserin aut Lithology identification using kernel Fisher discriminant analysis with well logs 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. Lithology identification Elsevier Linear discriminant analysis Elsevier Kernel trick Elsevier Kernel Fisher discriminant analysis Elsevier Wang, Zhizhang oth Zeng, Lianbo oth Enthalten in Elsevier Science Baccelli, Francois ELSEVIER Iterated Gilbert mosaics 2019 Amsterdam [u.a.] (DE-627)ELV008094314 volume:143 year:2016 pages:95-102 extent:8 https://doi.org/10.1016/j.petrol.2016.02.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.70 Wahrscheinlichkeitsrechnung VZ AR 143 2016 95-102 8 045F 660 |
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10.1016/j.petrol.2016.02.017 doi GBVA2016014000015.pica (DE-627)ELV024582506 (ELSEVIER)S0920-4105(16)30061-4 DE-627 ger DE-627 rakwb eng 660 660 DE-600 510 VZ 31.70 bkl Dong, Shaoqun verfasserin aut Lithology identification using kernel Fisher discriminant analysis with well logs 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. Lithology identification Elsevier Linear discriminant analysis Elsevier Kernel trick Elsevier Kernel Fisher discriminant analysis Elsevier Wang, Zhizhang oth Zeng, Lianbo oth Enthalten in Elsevier Science Baccelli, Francois ELSEVIER Iterated Gilbert mosaics 2019 Amsterdam [u.a.] (DE-627)ELV008094314 volume:143 year:2016 pages:95-102 extent:8 https://doi.org/10.1016/j.petrol.2016.02.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.70 Wahrscheinlichkeitsrechnung VZ AR 143 2016 95-102 8 045F 660 |
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10.1016/j.petrol.2016.02.017 doi GBVA2016014000015.pica (DE-627)ELV024582506 (ELSEVIER)S0920-4105(16)30061-4 DE-627 ger DE-627 rakwb eng 660 660 DE-600 510 VZ 31.70 bkl Dong, Shaoqun verfasserin aut Lithology identification using kernel Fisher discriminant analysis with well logs 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. Lithology identification Elsevier Linear discriminant analysis Elsevier Kernel trick Elsevier Kernel Fisher discriminant analysis Elsevier Wang, Zhizhang oth Zeng, Lianbo oth Enthalten in Elsevier Science Baccelli, Francois ELSEVIER Iterated Gilbert mosaics 2019 Amsterdam [u.a.] (DE-627)ELV008094314 volume:143 year:2016 pages:95-102 extent:8 https://doi.org/10.1016/j.petrol.2016.02.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.70 Wahrscheinlichkeitsrechnung VZ AR 143 2016 95-102 8 045F 660 |
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10.1016/j.petrol.2016.02.017 doi GBVA2016014000015.pica (DE-627)ELV024582506 (ELSEVIER)S0920-4105(16)30061-4 DE-627 ger DE-627 rakwb eng 660 660 DE-600 510 VZ 31.70 bkl Dong, Shaoqun verfasserin aut Lithology identification using kernel Fisher discriminant analysis with well logs 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. Lithology identification Elsevier Linear discriminant analysis Elsevier Kernel trick Elsevier Kernel Fisher discriminant analysis Elsevier Wang, Zhizhang oth Zeng, Lianbo oth Enthalten in Elsevier Science Baccelli, Francois ELSEVIER Iterated Gilbert mosaics 2019 Amsterdam [u.a.] (DE-627)ELV008094314 volume:143 year:2016 pages:95-102 extent:8 https://doi.org/10.1016/j.petrol.2016.02.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT 31.70 Wahrscheinlichkeitsrechnung VZ AR 143 2016 95-102 8 045F 660 |
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Lithology identification using kernel Fisher discriminant analysis with well logs |
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Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. |
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Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. |
| abstract_unstemmed |
Lithology identification is a key step in reservoir characterization. Linear discriminant analysis (LDA) is a widely used method for lithology identification. However, LDA suffers from the disadvantage that it can only extract linear features, whereas nonlinear features in the lithological feature space often play a role in lithology identification. In this paper, we introduce kernel Fisher discriminant analysis (KFD), an improved LDA with kernel trick, to overcome the shortcoming of LDA for lithology identification. It includes two processes: raising dimensions to get nonlinear information and reducing dimensions to get classification features. By these processes, it can obtain nonlinear classification features efficiently. To examine the effect of KFD for lithology identification, experiments are implemented on a field data set by KFD and auxiliary methods, namely LDA and traditional nonlinear discriminant analysis (quadratic discriminant analysis, QDA). By comparisons from different aspects, the results show that KFD outperforms LDA and QDA and it is a practicable method for lithology identification. |
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