Eurasian Journal of Soil Science

Volume 5, Issue 3, Jul 2016, Pages 231 - 240
DOI: 10.18393/ejss.2016.3.231-240
Stable URL: http://ejss.fess.org/10.18393/ejss.2016.3.231-240
Copyright © 2016 The authors and Federation of Eurasian Soil Science Societies



An application of Embedded Markov chain for soil sequences: Case study in North Western part of Algeria

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Kazi-Tani,L., Gaouar,A., 2016. An application of Embedded Markov chain for soil sequences: Case study in North Western part of Algeria. Eurasian J Soil Sci 5(3):231 - 240. DOI : 10.18393/ejss.2016.3.231-240
Kazi-Tani,L.,,& Gaouar,A. An application of Embedded Markov chain for soil sequences: Case study in North Western part of Algeria Eurasian Journal of Soil Science, DOI : 10.18393/ejss.2016.3.231-240
Kazi-Tani,L.,, and ,Gaouar,A."An application of Embedded Markov chain for soil sequences: Case study in North Western part of Algeria" Eurasian Journal of Soil Science, DOI : 10.18393/ejss.2016.3.231-240
Kazi-Tani,L.,, and ,Gaouar,A. "An application of Embedded Markov chain for soil sequences: Case study in North Western part of Algeria" Eurasian Journal of Soil Science, DOI : 10.18393/ejss.2016.3.231-240
LM,Kazi-Tani.A,Gaouar "An application of Embedded Markov chain for soil sequences: Case study in North Western part of Algeria" Eurasian J. Soil Sci, vol., no., pp., DOI : 10.18393/ejss.2016.3.231-240
Kazi-Tani,Lotfi ;Gaouar,Abdelaziz An application of Embedded Markov chain for soil sequences: Case study in North Western part of Algeria. Eurasian Journal of Soil Science,. DOI : 10.18393/ejss.2016.3.231-240

How to cite

Kazi-Tani, L., M. Gaouar, A., M.2016. An application of Embedded Markov chain for soil sequences: Case study in North Western part of Algeria. Eurasian J. Soil Sci. 5(3): 231 - 240. DOI : 10.18393/ejss.2016.3.231-240

Author information

Lotfi Kazi-Tani , Department of Agronomy, Faculty of Nature and Life Sciences, Earth and Universe, University of Tlemcen, Algeria
Abdelaziz Gaouar , Department of Agronomy, Faculty of Nature and Life Sciences, Earth and Universe, University of Tlemcen, Algeria

Publication information

Issue published online: 01 Jul 2016
Article first published online : 05 Mar 2016
Manuscript Accepted : 15 Feb 2016
Manuscript Received: 10 Jan 2016
DOI: 10.18393/ejss.2016.3.231-240
Stable URL: http://ejss.fesss.org/10.18393/ejss.2016.3.231-240

Abstract

Embedded Markov chain (EMC) has long history in geological domains, particularly to define the most representative sequences from statigraphic logs. In other words, what is viewed as a meaningless and disordered stratigraphic layer stack can be reorganized in a meaningful sequence by using EMC. This method was transposed in this paper to obtain soil sequences from data retrieved from soil map made by authors, covering a part of the region of Traras (N.W. of Algeria) and containing 13 major soil types. Each major soil type occupies at least one polygon in the map and allow to establish soil adjacencies, which have been tabulated in a matrix regardless to the direction. Three EMC methods have been tested, Walker, Harper and Türk using Strati-signal software and to erect soil relationship diagrams (SRD) representing the most significant links between soils. Significant test is the main difference between the above mentioned three EMC methods. It has been shown that Harper method is quite insensitive to small number of transitions. Besides, all three methods agreed for one soil sequence made by four soils: lithics leptosols- cambisols chormics- cambisols calcarics- fluvisols representing theoretical catena the most representative to the study area. This soil sequence is relevant to the study region and even to the whole Mediterranean region, and is commanded by the topography and the Mediterranean bioclimate. Walker SRD is the most realistic but the most difficult to interpret because of the high number of soil links, Harper SRD gives interesting results. Although the results didn’t bring something new to the soil interpretation and soil pedogensis but EMC applied to a finer scale may highlights other hidden relationships between soils.

Keywords

Adjacency matrix, catena, markov chain, randomness test, soil map, soil relationship diagram

Corresponding author

References

Baize, D., Girard, M. C., (coord.), 2008.Référentiel Pédologique. Editions : Quӕ. 405p.

Birkeland,P. W., 1999.Soils and Geomorphology, 3rd edition. New York: Oxford University Press. New York, USA 430p.

Burgess, T. M., Webster, R., 1984a. Optimal sampling strategiesfor mapping soil types: I. Distribution of boundary spacing. European Journal of Soil Science 35(4): 655-665.

Burgess, T. M, Webster, R., 1984b. Optimal sampling strategiesfor mapping soil types: II. Risk functions and sampling intervals. European journal Soil Science 35(4): 641-654.

C.P.C.S., (Commission de Pédologie et de la Cartographie des Sols). 1967. Classification des sols, INRA, Grignon. 96p.

FAO, 2006. World reference base of soil resources. A framework for international classification, correlation and communication. Food and Agriculture Organization of The United Nations World Soil Resources Reports No. 103, Rome, Italy.

Gingerich, P. D., 1969.Markov analysis of cyclic alluvial sediments. Journal of Sedimentary Research, 39(1),330-332.

Harper, C. W. Jr.., 1984a. . Improved methods of facies sequence analysis. In : Facies models: Response to sea level change. Walker, R.G. (Ed.). Geological Association of Canada, Reprint Series No.1, 2nd Edition. pp.11-13.

Harper, C. W. Jr., 1984b. Facies models revisited: An examination of quantitative methods. Geoscience Canada: Journal of The Geological Association of Canada 11(4): 203-207.

Jenny, H., 1941. Factors of soil formation. A system of quantitative pedology. MacGraw-Hill, New York, 281 p.

Labat, L., 2004. Simulations stochastiques de faciès par la méthode des membership functions. PhD dissertation Institut National Polytechnique de Lorraine. p.105.

Li, W., Li, B., Shi, Y., 1999. Markov-chain simulation of textural profiles. Geoderma 92(1-2): 37-53.

Li, W., Zhang, C., Burt, J. E., Zhu, A. X., Feyen, J., 2004. Two-dimensional Markov chain simulation of soil type spatial distribution. Soil Science Society of America Journal 68(5): 1479-1490.

Li, W., Zhang, C., 2006. A generalized markov chain approach for conditional simulation of categorical variables from grid samples. Transactions in GIS 10(4): 651–669.

Mastej, W., 2002. An application of Markov chain analysis to studies on lithofacies sequences in the alluvial fans from the „Bełchatów” lignite deposit (Poland). Annales Societatis Geologorum Poloniae 72(3): 271-282.

Mathieu, C., 2009. Les principaux sols du monde. Voyage à travers l'épiderme vivant de la planète Terre. Tec & Doc Editions. Paris, 221p.

Ndiaye, M., Davaud, E., Jorry, S.J., 2014. A computer-assisted method for depositional model determination. International Journal of Geosciences 5(2): 178-183.

Ndiaye, M., 2007. A multipurpose software for stratigraphic signal analysis. Doc. ès Sciences dissertation, Faculté des sciences, Université de Genève. p. 118.

Powers, D. W., Easterling, R.G., 1982. Improved methodology for using embedded Markov chains to describe cyclical sediments. Journal of Sedimentary Petrology 52(3): 913-923.

Read, W. A., 1969. Analysis and simulation of namurian sediments in central Scotland using a Markov-process model. Journal of the International Association for Mathematical Geology 1(2): 199-219.

Sarmah, R.K., 2013. Study of cyclic pattern and lithofacies variability by application of Markov chains and entropy analysis to Tikak Parbat formation, Makum coal basin, North Eastern India. European International Journal of Science and Technology 2(6): 41-55.

Selley, R. C., 1969. Studies of sequence in sediments using a simple mathematical device. Quarterly Journal of The Geological Society 125: 557-581.

Sun, X.L., Wu, S.C., Wang, H.L., Zhao, Y.G., Zhang, G.L., Man, Y.B., Wong, M.H., 2013. Dealing with spatial outliers and mapping uncertainty for evaluating the effects of urbanization on soil: A case study of soil pH and particle fractions in Hong Kong. Geoderma 195-196: 220-233.

Tobler, W.R., 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46: 234-240.

Türk, G., 1979. Transition analysis of structural sequences: Discussion and reply. Geological Society of American Bulletin 90(10): 989–991.

Türk, G., 1982. Letter to the editor: Markov chain analysis. Journal of the International Association for Mathematical Geology 14 (5): 539-542.

Türk, G., 2002. Comment on “Markov chain analysis of vertical facies sequences using a computer software package (SAVFS): Courtmacsherry formation”, by H. Xu and I.A.J. MacCarthy. Computer & Geosciences 28(3): 427-429.

Walker, R. G., 1979. Facies and Facies Models: General Introduction. In : Facies models: Response to sea level change. Walker, R.G. (Ed.). Geological Association of Canada, Reprint Series No.1, 2nd Edition. pp.1-7.

Xu, H., Maccarthy, I.A.J., 1998. Markov chain analysis of vertical facies sequences using a computer software package (SAVFS): Courtmacsherry formation (Tournaisian), southern Ireland. Computer and Geosciences 24(2): 131-139.

Abstract

Embedded Markov chain (EMC) has long history in geological domains, particularly to define the most representative sequences from statigraphic logs. In other words, what is viewed as a meaningless and disordered stratigraphic layer stack can be reorganized in a meaningful sequence by using EMC. This method was transposed in this paper to obtain soil sequences from data retrieved from soil map made by authors, covering a part of the region of Traras (N.W. of Algeria) and containing 13 major soil types. Each major soil type occupies at least one polygon in the map and allow to establish soil adjacencies, which have been tabulated in a matrix regardless to the direction.  Three EMC methods have been tested, Walker, Harper and Türk using Strati-signal software and to erect soil relationship diagrams (SRD) representing the most significant links between soils. Significant test is the main difference between the above mentioned three EMC methods. It has been shown that Harper method is quite insensitive to small number of transitions. Besides, all three methods agreed for one soil sequence made by four soils: lithics leptosols- cambisols chormics- cambisols calcarics- fluvisols representing theoretical catena the most representative to the study area. This soil sequence is relevant to the study region and even to the whole Mediterranean region, and is commanded by the topography and the Mediterranean bioclimate. Walker SRD is the most realistic but the most difficult to interpret because of the high number of soil links, Harper SRD gives interesting results. Although the results didn’t bring something new to the soil interpretation and soil pedogensis but EMC applied to a finer scale may highlights other hidden relationships between soils.

Key words: Adjacency matrix, catena, markov chain, randomness test, soil map, soil relationship diagram

References

Baize, D., Girard, M. C., (coord.), 2008.Référentiel Pédologique. Editions : Quӕ. 405p.

Birkeland,P. W., 1999.Soils and Geomorphology, 3rd edition. New York: Oxford University Press. New York, USA 430p.

Burgess, T. M., Webster, R., 1984a. Optimal sampling strategiesfor mapping soil types: I. Distribution of boundary spacing. European Journal of Soil Science 35(4): 655-665.

Burgess, T. M, Webster, R., 1984b. Optimal sampling strategiesfor mapping soil types: II. Risk functions and sampling intervals. European journal Soil Science 35(4): 641-654.

C.P.C.S., (Commission de Pédologie et de la Cartographie des Sols). 1967. Classification des sols, INRA, Grignon. 96p.

FAO, 2006. World reference base of soil resources. A framework for international classification, correlation and communication. Food and Agriculture Organization of The United Nations World Soil Resources Reports No. 103, Rome, Italy.

Gingerich, P. D., 1969.Markov analysis of cyclic alluvial sediments. Journal of Sedimentary Research, 39(1),330-332.

Harper, C. W. Jr.., 1984a. . Improved methods of facies sequence analysis. In : Facies models: Response to sea level change. Walker, R.G. (Ed.). Geological Association of Canada, Reprint Series No.1, 2nd Edition. pp.11-13.

Harper, C. W. Jr., 1984b. Facies models revisited: An examination of quantitative methods. Geoscience Canada: Journal of The Geological Association of Canada 11(4): 203-207.

Jenny, H., 1941. Factors of soil formation. A system of quantitative pedology. MacGraw-Hill, New York, 281 p.

Labat, L., 2004. Simulations stochastiques de faciès par la méthode des membership functions. PhD dissertation Institut National Polytechnique de Lorraine. p.105.

Li, W., Li, B., Shi, Y., 1999. Markov-chain simulation of textural profiles. Geoderma 92(1-2): 37-53.

Li, W., Zhang, C., Burt, J. E., Zhu, A. X., Feyen, J., 2004. Two-dimensional Markov chain simulation of soil type spatial distribution. Soil Science Society of America Journal 68(5): 1479-1490.

Li, W., Zhang, C., 2006. A generalized markov chain approach for conditional simulation of categorical variables from grid samples. Transactions in GIS 10(4): 651–669.

Mastej, W., 2002. An application of Markov chain analysis to studies on lithofacies sequences in the alluvial fans from the „Bełchatów” lignite deposit (Poland). Annales Societatis Geologorum Poloniae 72(3): 271-282.

Mathieu, C., 2009. Les principaux sols du monde. Voyage à travers l'épiderme vivant de la planète Terre. Tec & Doc Editions. Paris, 221p.

Ndiaye, M., Davaud, E., Jorry, S.J., 2014. A computer-assisted method for depositional model determination. International Journal of Geosciences 5(2): 178-183.

Ndiaye, M., 2007. A multipurpose software for stratigraphic signal analysis. Doc. ès Sciences dissertation, Faculté des sciences, Université de Genève. p. 118.

Powers, D. W., Easterling, R.G., 1982. Improved methodology for using embedded Markov chains to describe cyclical sediments. Journal of Sedimentary Petrology 52(3): 913-923.

Read, W. A., 1969. Analysis and simulation of namurian sediments in central Scotland using a Markov-process model. Journal of the International Association for Mathematical Geology 1(2): 199-219.

Sarmah, R.K., 2013. Study of cyclic pattern and lithofacies variability by application of Markov chains and entropy analysis to Tikak Parbat formation, Makum coal basin, North Eastern India. European International Journal of Science and Technology 2(6): 41-55.

Selley, R. C., 1969. Studies of sequence in sediments using a simple mathematical device. Quarterly Journal of The Geological Society 125: 557-581.

Sun, X.L., Wu, S.C., Wang, H.L., Zhao, Y.G., Zhang, G.L., Man, Y.B., Wong, M.H., 2013. Dealing with spatial outliers and mapping uncertainty for evaluating the effects of urbanization on soil: A case study of soil pH and particle fractions in Hong Kong. Geoderma 195-196: 220-233.

Tobler, W.R., 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46: 234-240.

Türk, G., 1979. Transition analysis of structural sequences: Discussion and reply. Geological Society of American Bulletin 90(10): 989–991.

Türk, G., 1982. Letter to the editor: Markov chain analysis. Journal of the International Association for Mathematical Geology 14 (5): 539-542.

Türk, G., 2002. Comment on “Markov chain analysis of vertical facies sequences using a computer software package (SAVFS): Courtmacsherry formation”, by H. Xu and I.A.J. MacCarthy. Computer & Geosciences 28(3): 427-429.

Walker, R. G., 1979. Facies and Facies Models: General Introduction. In : Facies models: Response to sea level change. Walker, R.G. (Ed.). Geological Association of Canada, Reprint Series No.1, 2nd Edition. pp.1-7.

Xu, H., Maccarthy, I.A.J., 1998. Markov chain analysis of vertical facies sequences using a computer software package (SAVFS): Courtmacsherry formation (Tournaisian), southern Ireland. Computer and Geosciences 24(2): 131-139.



Eurasian Journal of Soil Science