Research Article
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Year 2019, , 73 - 91, 01.04.2019

Polina Lemenkova

https://doi.org/10.3153/AR19009

Abstract

References

  • Beazley, D.M. (2009). Python essential reference. Addison-Wesley Professional. Available at www.python.org (accessed 18.03.2019)
  • 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.
  • 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.
  • 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.
  • Jones, E., Oliphant, T., Peterson, P. (2014). SciPy: open source scientific tools for Python. Available at www.scipy.org (accessed 23.03.2019)
  • NumPy community (2019). NumPy Reference. Release 1.16.1. 1372 p.
  • 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.
  • Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation and StagLab 3.0. Geoscientific Model Development, 11, 2541-2562.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • Jamieson, A.J. (2018). A contemporary perspective on hadal science. Deep-Sea Research Part II, 155, 4-10.
  • 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.
  • 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.
  • Litvin, V.M. (1987). Morfostruktura dna okeanov (Morphostructure of the ocean floor). In Russian. Ed. by A. N. Lastochkin. Leningrad: Nedra. 275.
  • 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.
  • 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.
  • 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.
  • Masson, D.G. (1991). Fault Patterns at Outer Trench Walls. Marine Geophysical Researches, 13, 209-225.
  • Oliphant, T.E. (2007). Python for scientific computing. Computing in Science & Engineering 9(3), 10-20.
  • 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.
  • Perez, F., Granger, B.E. (2007). IPython: a system for interactive scientific computing. Computing in Science & Engineering 9(3), 21-29.
  • R Core Team. (2014). R: a language and environment for statistical computing. Vienna. Available at http://www.R-project.org (accessed 14.12.2018)

PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS

Year 2019, , 73 - 91, 01.04.2019

Polina Lemenkova

https://doi.org/10.3153/AR19009

Abstract

The
study area is located in western Pacific Ocean, Mariana Trench. The aim of the
data analysis is to analyze the potential influence of how various geological
and tectonic factors may affect the geomorphological shape of the Mariana
Trench.  Statistical analysis of the data
set in marine geology and oceanography requires an adequate strategy on big
data processing. In this context, current research proposes a combination of
the Python-based methodology that couples GIS geospatial data analysis. The
Quantum GIS part of the methodology produces an optimized representative
sampling dataset consisting of 25 cross-section profiles having in total 12,590
bathymetric observation points. The sampling of the geospatial dataset are
located across the Mariana Trench. The second part of the methodology consists
of statistical data processing by means of high-level programming language
Python. Current research uses libraries Pandas, NumPy and SciPy. The data
processing also involves the subsampling of two auxiliary masked data frames
from the initial large data set that only consists of the target variables:
sediment thickness, slope angle degrees and bathymetric observation points
across four tectonic plates: Pacific, Philippine, Mariana, and Caroline.
Finally, the data were analyzed by several approaches: 1) Kernel Density
Estimation (KDE) for analysis of the probability of data distribution; 2)
stacked area chart for visualization of the data range across various segments
of the trench; 3) spacial series of radar charts; 4) stacked bar plots showing
the data distribution by tectonic plates; 5) stacked bar charts for correlation
of sediment thickness by profiles, versus distance from the igneous volcanic
areas; 6) circular pie plots visualizing data distribution by 25 profiles; 7)
scatterplot matrices for correlation analysis between marine geologic
variables. The results presented a distinct correlation between the geologic,
tectonic and oceanographic variables. Six Python codes are provided in full for
repeatability of this research.

Keywords

Mariana Trench Pacific Ocean Python SciPy NumPy Pandas Programming language Statistics Data analysis

References

  • Beazley, D.M. (2009). Python essential reference. Addison-Wesley Professional. Available at www.python.org (accessed 18.03.2019)
  • 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.
  • 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.
  • 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.
  • Jones, E., Oliphant, T., Peterson, P. (2014). SciPy: open source scientific tools for Python. Available at www.scipy.org (accessed 23.03.2019)
  • NumPy community (2019). NumPy Reference. Release 1.16.1. 1372 p.
  • 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.
  • Crameri, F. (2018). Geodynamic diagnostics, scientific visualisation and StagLab 3.0. Geoscientific Model Development, 11, 2541-2562.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • Jamieson, A.J. (2018). A contemporary perspective on hadal science. Deep-Sea Research Part II, 155, 4-10.
  • 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.
  • 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.
  • Litvin, V.M. (1987). Morfostruktura dna okeanov (Morphostructure of the ocean floor). In Russian. Ed. by A. N. Lastochkin. Leningrad: Nedra. 275.
  • 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.
  • 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.
  • 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.
  • Masson, D.G. (1991). Fault Patterns at Outer Trench Walls. Marine Geophysical Researches, 13, 209-225.
  • Oliphant, T.E. (2007). Python for scientific computing. Computing in Science & Engineering 9(3), 10-20.
  • 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.
  • Perez, F., Granger, B.E. (2007). IPython: a system for interactive scientific computing. Computing in Science & Engineering 9(3), 21-29.
  • R Core Team. (2014). R: a language and environment for statistical computing. Vienna. Available at http://www.R-project.org (accessed 14.12.2018)
There are 28 citations in total.

Details

Primary Language English
Subjects Hydrobiology, Maritime Engineering
Journal Section Research Articles
Authors

Polina Lemenkova 0000-0002-5759-1089

Publication Date April 1, 2019
Submission Date March 24, 2019
Published in Issue Year 2019

Cite

APA Lemenkova, P. (2019). PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS. Aquatic Research, 2(2), 73-91. https://doi.org/10.3153/AR19009

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