TY  - JOUR
T1  - PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS
AU  - Lemenkova, Polina
PY  - 2019
DA  - April
DO  - 10.3153/AR19009
JF  - Aquatic Research
JO  - Aquat Res
PB  - Nuray ERKAN ÖZDEN
WT  - DergiPark
SN  - 2618-6365
SP  - 73
EP  - 91
VL  - 2
IS  - 2
LA  - en
AB  - Thestudy area is located in western Pacific Ocean, Mariana Trench. The aim of thedata analysis is to analyze the potential influence of how various geologicaland tectonic factors may affect the geomorphological shape of the MarianaTrench. Statistical analysis of the dataset in marine geology and oceanography requires an adequate strategy on bigdata processing. In this context, current research proposes a combination ofthe Python-based methodology that couples GIS geospatial data analysis. TheQuantum GIS part of the methodology produces an optimized representativesampling dataset consisting of 25 cross-section profiles having in total 12,590bathymetric observation points. The sampling of the geospatial dataset arelocated across the Mariana Trench. The second part of the methodology consistsof statistical data processing by means of high-level programming languagePython. Current research uses libraries Pandas, NumPy and SciPy. The dataprocessing also involves the subsampling of two auxiliary masked data framesfrom the initial large data set that only consists of the target variables:sediment thickness, slope angle degrees and bathymetric observation pointsacross four tectonic plates: Pacific, Philippine, Mariana, and Caroline.Finally, the data were analyzed by several approaches: 1) Kernel DensityEstimation (KDE) for analysis of the probability of data distribution; 2)stacked area chart for visualization of the data range across various segmentsof the trench; 3) spacial series of radar charts; 4) stacked bar plots showingthe data distribution by tectonic plates; 5) stacked bar charts for correlationof sediment thickness by profiles, versus distance from the igneous volcanicareas; 6) circular pie plots visualizing data distribution by 25 profiles; 7)scatterplot matrices for correlation analysis between marine geologicvariables. The results presented a distinct correlation between the geologic,tectonic and oceanographic variables. Six Python codes are provided in full forrepeatability of this research.
KW  - Mariana Trench
KW  - Pacific Ocean
KW  - Python
KW  - SciPy
KW  - NumPy
KW  - Pandas
KW  - Programming language
KW  - Statistics
KW  - Data analysis
CR  - Beazley, D.M. (2009). Python essential reference. Addison-Wesley Professional. Available at www.python.org  (accessed 18.03.2019)
CR  - Bogdanov, I., Huaman, D., Thovert, J.-F, Pierre, G., Adler, P.M. (2011). Tectonic stresses seaward of an aseismic ridge Trench collision zone. A remote sensing approach on the Loyalty Islands, SW Pacific. Tectonophysics, 499, 77-91.
CR  - Hunter, J.D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90-95. 
Bogolepov, K. V., Chikov, B. M. Geologiya dna okeanov (Geology of the ocean floor). Russian. Ed. by Saks, V. N., Fotiadi, E. E. Moscow: Nauka, 1976, 246.
CR  - Brune, S. (2016). Plate Boundaries and Natural Hazards. AGU Geophysical Monograph 219. ed. by Duarte, J. C. & Schellart, W. P. Sydney, Australia: AGU. Chap. Rifts and rifted margins: A review of geodynamic processes and natural hazards, 1-21.
CR  - Jones, E., Oliphant, T., Peterson, P. (2014). SciPy: open source scientific tools for Python. Available at www.scipy.org  (accessed 23.03.2019)
CR  - NumPy community (2019). NumPy Reference. Release 1.16.1. 1372 p.
CR  - Contreras-Reyes, E., Carrizo, D. (2011). Control of high oceanic features and subduction channel on earthquake ruptures along the Chile-Peru subduction zone. Physics of the Earth and Planetary Interiors 186, 49-58.
CR  - Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation and StagLab 3.0. Geoscientific Model Development, 11, 2541-2562.
CR  - Cui, W., Hu, Y., Guo, W., Pan, B., Wang, F. Reprint of a preliminary design of a movable laboratory for hadal trenches. Methods in Oceanography, 10(2014), 178-193.
 
Cui, W., X., Wu (2018). A Chinese strategy to construct a comprehensive investigation system for hadal trenches. Deep-Sea Research Part II 155, 27–33.
CR  - Dierssen, H.M., Theberge, A.E.J. (2014). Encyclopedia of Natural Resources. Taylor & Francis. Chap. Bathymetry: Features and Hypsography, 1–7. https://doi.org/10.1081/E-ENRW-120048589
CR  - Doglioni, C. (2009). Comment on ’The potential influence of subduction zone polarity on overriding plate deformation, trench migration and slab dip angle’ by W.P. Schellart. Tectonophysics, 463, 208-213.
CR  - Dokht, R.M.H., Gu, Y.J., Sacchi, M.D. (2016). Waveform inversion of SS precursors: An investigation of the northwestern Pacific subduction zones and intraplate volcanoes in China. Gondwana Research, 40, 77-90.
CR  - Fernandez, M.O., Marques, A.C. (2018). Combining bathymetry, latitude, and phylogeny to understand the distribution of deep Atlantic hydroids (Cnidaria). Deep-Sea Research Part I, 133, 39-48.
CR  - Gorbatov, A., Fukao, Y., Widiyantoro, S., Gordeev, E. (2001). Seismic evidence for a mantle plume oceanwards of the Kamchatka Aleutian trench junction. Geophysical Journal International, 146, 282-288.
CR  - Hubble, T., Webster, J., Yu, P., Fletcher, M., Airey, D., Clarke, S., Mitchell, D., Voelker, D., Puga-Bernabeu, A., Howard, F., Gallagher, S., Martin, T. (2016). Submarine Mass Movements and their Consequences. Advances in Natural and Technological Hazards Research. ed. by G. Lamarche. Switzerland: Springer International Publishing. Chap. Chapter 12. Submarine Landslides and Incised Canyons of the Southeast Queensland Continental Margin, 125-134. https://doi.org/10. 1007/978-3-319-20979-1_12
CR  - Ikari, M.J., Kameda, J., Saffer, D.M., Kopf, A.J. (2015). Strength characteristics of Japan Trench borehole samples in the high-slip region of the 2011 Tohoku-Oki earthquake. Earth and Planetary Science Letters, 412, 35-41.
CR  - Jamieson, A.J. (2018). A contemporary perspective on hadal science. Deep-Sea Research Part II, 155, 4-10.
CR  - Kong, X., Li, S., Wang, Y., Suo, Y., Dai, L., Géli, L., Zhang, Y., Guo, L., Wang, P. (2017). Causes of earthquake spatial distribution beneath the Izu-Bonin-Mariana Arc. Journal of Asian Earth Sciences, 151, 90-100.
CR  - Lemoine, A., Madariaga, R., Campos, J. (2002). Slab-pull and slab-push earthquakes in the Mexican, Chilean and Peruvian subduction zones. Physics of the Earth and Planetary Interiors, 132, 157-175.
CR  - Litvin, V.M. (1987). Morfostruktura dna okeanov (Morphostructure of the ocean floor). In Russian. Ed. by A. N. Lastochkin. Leningrad: Nedra. 275.
CR  - Loher, M., Marcon, Y., Pape, T., Römer, M., Wintersteller, P., Santos Ferreira, C. dos, Praeg, D., Torres, M., Sahling, H., Bohrmann, G. (2018). Seafloor sealing, doming, and collapse associated with gas seeps and authigenic carbonate structures at Venere mud volcano, Central Mediterranean. Deep-Sea Research Part I, 137, 76-96.
CR  - Luo, M., Algeo, T.J., Tong, H., Gieskes, J., Chen, L., Shi, X., Chen, D. (2018). More reducing bottom-water redox conditions during the Last Glacial Maximum in the southern Challenger Deep (Mariana Trench, western Pacific) driven by enhanced productivity. DeepSea Research Part II, 155, 70-82.
CR  - Mao, X., Zhang, B., Deng, H., Zou, Y., Chen, J. (2016). Three-dimensional morphological analysis method for geologic bodies and its parallel implementation. Computers & Geosciences, 96, 11-22.
CR  - Masson, D.G. (1991). Fault Patterns at Outer Trench Walls. Marine Geophysical Researches, 13, 209-225.
CR  - Oliphant, T.E. (2007). Python for scientific computing. Computing in Science & Engineering 9(3), 10-20.
CR  - Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-Learn: machine learning in Python. The Journal of Machine Learning Research, 12, 2825-2830.
CR  - Perez, F., Granger, B.E. (2007). IPython: a system for interactive scientific computing. Computing in Science & Engineering 9(3), 21-29.
CR  - R Core Team. (2014). R: a language and environment for statistical computing. Vienna. Available at http://www.R-project.org (accessed 14.12.2018)
UR  - https://doi.org/10.3153/AR19009
L1  - https://dergipark.org.tr/en/download/article-file/690837
ER  -