Calculating slope gradient variations in the submarine landforms by R and Python statistical libraries

This research focuses on the analysis of the submarine geomorphology in the Mariana Trench located in west Pacific Ocean. The research question is to identify variations in the geomorphic form and bathymetry in different segments of the trench. Technically, the paper applies Python and R programming statistical libraries for geospatial modelling of the data sets. The methodological approach of the statistical data analysis by scripting libraries aimed to visualize geomorphic variations in the 25 transect profiles of the trench. Multiple factors affect submarine geomorphology causing variations in the gradient slope: geological settings (rock composition, structure, permeability, erodibility of the materials), submarine erosion, gravity flows of water streams, tectonics, sediments from the volcanic arcs, transported by transverse submarine canyons. Understanding changes in geomorphic variations is important for the correct geospatial analysis. However, modelling such a complex structure as hadal trench requires numerical computation and advanced statistical analysis of the data set. Such methods are proposed by R and Python programming languages. Current research presented usage of statistical libraries for the data processing: Matplotlib, NumPy, SciPy, Pandas, Seaborn, StatsModels by Python. The research workflow includes following steps: Partial least squares regression analysis; Ordinary Least Square (OLS); Violin plots and Bar plots for analysis of ranges of the bathymetric data; Isotonic Regression by StatsModels library; Data distribution analysis by Bokeh and Matplotlib libraries; Circular bar plots for sorting data by R; Euler-Venn diagrams for visualizing overlapping of attributes and factors by Python. As a result of the data analysis, the geomorphology of the trench slopes in 25 transecting profiles was modelled. The results achieved by the statistical data modelling show differences in the gradient slope in various segments of the trench depending on its spatial location. This shows complex geological structure of the trench. The paper contributes towards the methodological development of the data analysis in marine geology through the stepwise workflow explanations with a case study of Python and R applications

Kaynakça

[1]. Jamieson A.J., Fujii T., Mayor D.J., Solan M., Priede, I. G. “Hadal trenches: the ecology of the deepest places on Earth”, Trends in Ecology and Evolution, 25 (3), (2009), 190-197. doi: 10.1016/j.tree.2009.09.009

[2]. Lemenkova P. “Factor Analysis by R Programming to Assess Variability Among Environmental Determinants of the Mariana Trench”, Turkish Journal of Maritime and Marine Sciences, 4, (2018), 146–155. doi: 10.6084/m9.figshare.7358207

[3]. Lemenkova P. “R scripting libraries for comparative analysis of the correlation methods to identify factors affecting Mariana Trench formation”, Journal of Marine Technology and Environment, 2, (2018), 35-42. doi: 10.6084/m9.figshare.7434167

[4]. Duineveld G. “Activity and composition of the benthic fauna in the Whittard Canyon and the adjacent continental slope (NE Atlantic)”, Oceanologica Acta, 24, (2001), 69–83.

[5]. McCullagh P. Slopes, in: FitzGerald, B.P. (ed.), Modern Concepts in Geomorphology, (1988).

[6]. Ritter D.F., Kochel C.R., Miller J.R. Process Geomorphology (3rd Edition): Wm. C. Brown Publishers, Dubuque, IA, (2002), 544.

[7]. Chorley R.J., Schumm S.A., Sugden D.E. Geomorphology. London: Methuen and Co. Ltd. (1984).

[8]. Summerfield M.A. Global Geomorphology. New York: John Wiley and Sons, (1991).

[9]. Easterbrook D.J. Surface Processes and Landforms: Macmillan Pub. Co., (1993).

[10]. Bloom A. Geomorphology, A systematic analysis of Late Cenozoic landforms, 3rd ed. Prentice Hall, Upper Saddle River, N.J., (1998).

[11]. Harris P. T., Macmillan-Lawler M., Rupp J., Baker E.K. Geomorphology of the oceans. Marine Geology, 352, (2014), 4–24.

[12]. Wilson M.F.J., O'Connell,B., Brown,C., Guinan,J.C., Grehan A.J. “Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope”, Marine Geodesy 30, (2007), 3–35.

[13]. Lemenkova P. “Hierarchical Cluster Analysis by R language for Pattern Recognition in the Bathymetric Data Frame: a Case Study of the Mariana Trench, Pacific Ocean. Virtual Simulation, Prototyping and Industrial Design”. Proceedings of the 5th Int’l Sci.-Pract. Conference, 2(5), (2018), Ed. M. N. Krasnyansky, Tambov: TSTU Press, 147–152. doi: 10.6084/m9.figshare.7531550

[14]. Pickering K.T., Hiscott R.N. Deep marine systems: processes, deposits, environments, tectonics and sedimentation. Wiley, Chichester, (2015).

[15]. Wu S., Takahashi N., Tokuyama H., Wong H.K. “Geomorphology, sedimentary processes and development of the Zenisu deep-sea channel, northern Philippine Sea”, Geo-Marine Letters, 25(4), (2005), 230- 240. doi: 10.1007/s00367-005-0210-9

[16]. Lemenkova P. “An Empirical Study of R Applications for Data Analysis in Marine Geology”, Marine Science and Technology Bulletin, 8(1), (2019), 1-9. doi: 10.33714/masteb.486678

[17]. Okada H. “Anatomy of trench-slope basins: examples from the Nankai trough”, Palaeogeography, Palaeoclimatology, Palaeoecology, 71 (1-2), (1989), 3- 13. doi: 10.1016/0031-0182(89)90026-6

[18]. Yu H.S. “Geological characteristics and distribution of submarine physiographic features in the Taiwan region”, Marine Georesources and Geotechnology, 21 (3-4), (2003), 139–153. doi: 10.1080/ 713773391

[19]. VanderPlas, J. Python Data Science Handbook. Essential Tools for Working with Data. O’Reilly, (2016).

[20]. McKinney W. PyData Development Team. Pandas: powerful Python data analysis toolkit Release 0.24.0. [Online] http://www.python.org [Accessed: 10 April 2019].

[21]. Lemenkova P. “Processing oceanographic data by Python libraries NumPy, SciPy and Pandas”, Aquatic Research, 2, (2019), 73-91. doi: 10.3153/AR19009

[22]. Duchesnay E., Löfstedt T. Statistics and Machine Learning in Python Release 0.2, 2019. [Online] http://www.python.org [Accessed: 10 April 2019].

[23]. R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, (2014). http://www.R-project.org [Accessed: 13 March. 2019].

[24]. Lemenkova P. “K-means Clustering in R Libraries {cluster} and {factoextra} for Grouping Oceanographic Data”, International Journal of Informatics and Applied Mathematics, 2(1), (2019), 1-26. doi: 10.6084/m9.figshare.9891203

[25]. Rossum, G. van. Python Programming Language, 2011. https://www.python.org/ [Accessed: 10 April 2019].

[26]. Rossetier D.G. Tutorial: An example of statistical data analysis using the R environment for statistical computing, (2017).

[27]. NumPy community. NumPy Reference. Release 1.16.1, (2019), https://www.python.org/ [Accessed: 10 April 2019].

[28]. Lemenkova P. “Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation”, Aquatic Sciences and Engineering, 34, (2019), 51–60. doi: 10.26650/ASE2019547010

[29]. Crozier M. Slope Evolution, in Goudie, A.S., ed., Encyclopedia of Geomorphology, Volume 2, Routledge, New York, NY, (2004).

[30]. Savage L.J. The Foundations of Statistics, 2nd revised ed., Dover, New Yor, (1972).

[31]. Kruskal J.B. “Nonmetric Multidimensional Scaling: A numerical method”, Psychometrika, 29 (2), (1964), 115– 129. doi:10.1007/BF02289694

[32]. Best M.J. Chakravarti N. “Active set algorithms for isotonic regression; a unifying framework”, Mathematical Programming, 47 (1–3), (1990), 425–439. doi:10.1007/BF01580873

[33]. Wu W.B., Woodroofe M., Mentz G. “Isotonic regression: Another look at the changepoint problem”, Biometrika, 88 (3), (2001), 793–804. doi:10.1093/biomet/88.3.793

[34]. Leeuw J. de Hornik K., Mair P. “Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods”, Journal of Statistical Software, 32 (5), (2009), 1–24. doi:10.18637/jss.v032.i05.

[35]. Lemenkova P. Scatterplot Matrices of the Geomorphic Structure of the Mariana Trench at Four Tectonic Plates (Pacific, Philippine, Mariana and Caroline): a Geostatistical Analysis by R. In: Problems of Tectonics of Continents and Oceans. Proceedings of the 51st Tectonics Meeting, Ed. Degtyarev, K. E. (1) RAS Institute of Geology. Moscow: GEOS, (2019), 347–352. doi: 10.6084/m9.figshare.7699787.v1

[36]. Bretz F., Hothorn T. aWestfall P. Multiple Comparisons Using R. Taylor and Francis Group, LLC. (2011).

[37]. Lemenkova P. “Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language”, Geodesy and Cartography, 45(2), (2019), 57–84. doi: 10.3846/gac.2019.3785

[38]. Gardner J.V., Armstrong A.A., Calder B.R. Beaudoin J. So. “How Deep Is the Mariana Trench?”, Marine Geodesy, 37, (2014), 1-13, doi: 10.1080/01490419.2013.837849

[39]. Lemenkova P. “Regression Models by Gretl and R Statistical Packages for Data Analysis in Marine Geology”, International Journal of Environmental Trends, 3(1), (2019), 39–59. doi: 10.6084/m9.figshare.8313362.v1

[40]. Lemenkova P. “Numerical Data Modelling and Classification in Marine Geology by the SPSS Statistics”, International Journal of Engineering Technologies, 5(2), (2019), 90–99. doi: 10.6084/m9.figshare.8796941

[41]. Covault J.A., Fildani A., Romans B.W., McHargue T. “The natural range of submarine canyon-and-channel longitudinal profiles”, Geosphere, 7, (2011), 313–332. doi:10.1130/GES00610.1

[42]. Clark M.J., Small R.J. Slopes and weathering: Cambridge University Press, Cambridge, England, (1982).

[43]. Jones O.P., Simons R.R., Jones E.J.W., Harris J.M. “Influence of seabed slope and Coriolis effects on the development of sandbanks near headlands”, Journal of Geophysical Research, 111, (2006), 1–23. doi: 10.1029/2005JC002944

[44]. Dadson S., Hovius N., Pegg S., Dade W.B., Horng M.J., Chen H. “Hyperpycnal river flows from an active mountain belt”, Journal of Geophysical Research Earth Surface, 110 (F4), (2005). doi: 10.1029/2004JF000244

[45]. Karig D.E., Sharman G.F. “Subduction and accretion in trenches”, Geological Society of America Bulletin, 86 (3), (1975), 377–389.

[46]. Mayer L. Introduction to Quantitative Geomorphology: Prentice Hall, Englewood Cliffs, NJ, (1990).

[47]. Lemenkova P., Promper C., Glade T. Economic Assessment of Landslide Risk for the Waidhofen a.d. Ybbs Region, Alpine Foreland, Lower Austria. In E. Eberhardt, C. Froese, A. K. Turner, & S. Leroueil (Eds.), Protecting society through improved understanding, (2012), 279-285. doi: 10.6084/m9.figshare.7434230

[48]. Schenke H.W., Lemenkova P. “Zur Frage der Meeresboden-Kartographie: Die Nutzung von AutoTrace Digitizer für die Vektorisierung der Bathymetrischen Daten in der Petschora-See”, Hydrographische Nachrichten, 25(81), (2008), 16–21. doi: 10.6084/m9.figshare.7435538.v2

[49]. Myers J.L. Well A.D. Research Design and Statistical Analysis. Ed. 2, Lawrence Erlbaum, U.S., (2003).

[50]. Cowan G. Statistical Data Analysis. Oxford Science Publications. Clarendon Press, Oxford, UK, (1998).

[51]. Brownlee K.A. Statistical theory and methodology in science and engineering. 2nd ed., New York: John Wiley & Sons, (1965).

[52]. Klaučo M., Gregorová B., Stankov U., Marković,V., Lemenkova P. “Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area”, Central European Journal of Geosciences, 5(1), (2013), 28-42. doi: 10.2478/s13533-012-0120-0

[53]. Suetova I.A., Ushakova L.A., Lemenkova P. Geoinformation mapping of the Barents and Pechora Seas. Geography and Natural.

Kaynak Göster