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pygplates – a GPlates Python library for data analysis through geological time and space John Cannon, Simon Williams, Xiaodong Qin, and Dietmar Müller EarthByte Group, School of Geosciences,The University of Sydney Spatio-temporal data mining challenge • Challenge: spatio-temporal data analysis, i.e. testing hypotheses for geological processes quantitatively • Example: understanding associations between tectonic processes and mineral resource formation in a multi-parameter space • Hard for a human being to visually comprehend and analyse data in a 4D hyperspace • New tools are needed for spatio-temporal data mining – 4D machine learning Example: Spatiotemporal data analysis of Andean ore deposits Spatio-temporal data mining challenge pyGPlates versus GPlates o GPlates is a desktop program (executable) o pyGPlates enables access to GPlates functionality via Python programming language o pyGPlates is a Python library pyGPlates documentation http://www.gplates.org/docs/pygplates/ What can pyGPlates do ? • • • • • • • • • • • Reconstruct point, vector, raster data Reconstruct plate tessellation through time Calculate trench convergence rates Interpolate seafloor ages Compute hot spot trails Track subduction zone proximity through time Implement filter/query utilities Drive online paleomap maker on the cloud (GPlates Portal) PyGPlates ipython notebook “sandbox” on the cloud Generate plate-model independent (adaptable) paleogeographic maps Spatio-temporal data analysis Subduction convergence Subduction Zone convergence velocities (cm/yr) Subduction convergence Calculate plate convergence velocities at subducting tectonic boundaries: 1. 2. 3. 4. Reconstruct tectonic plate boundaries through geological time. Calculate plate convergence velocities at tessellated points along subducting plate boundaries. Save results as xyzw text file. Convert xyzw file to a GPlates file (GPML) and display in GPlates; export to GMT or ArcGIS-compatible file. GPlates Portal pyGPlates Examples portal.gplates.org IPython pyGPlates examples IPython (Jupyter) sample notebooks available on GPlates Portal Support for over 40 Programming languages IPython pyGPlates notebooks for teaching tectonics in high schools Cloud-based tectonic notebooks for high-school teachers (May 2016 workshop) Jupyter notebook for global earthquake plotting Earthquake locations and magnitudes relative to present-day tectonic plate boundaries Jupyter notebook for analysing supercontinent amalgamation and dispersal since 400 million years ago Plate reconstruction175 million years ago. Velocity arrows show speed and direction of plate motion Jupyter notebook for analysing mineral deposits in the context of plate motions and plate boundary evolution since 400 million years ago Locations and commodity types of mineral resources relative to present-day tectonic plate boundaries Combine pyGPlates, with machine learning Plate tectonic reconstructions since Pangea breakup + Time-encoded data describing Andean ore deposits by age, lithology, geochemistry + Machine Learning Investigate controlling factors on why, where, when porphyry copper-gold ore deposits form Investigate tectonic environments of Andean porphyry Au/Cu deposits through time • • • • • Porphyry deposits tend to form in suprasubduction environments (Rosenbaum et al., 2005) Porphyry deposits are most often associated with calcalkaline and adakitic magmatism in subduction zones and refertilisation of the sub-continental lithospheric mantle (Thieblemont et al., 1997, Griffin et al., 2013) Multiple tectono-magmagmetic parameters controls the distribution of arc-magmatism (Schutte et al., 2010) Associated with stocks/plutons (Sillitoe, 2010) Porphyry belts have lifetimes around 10-20 Myr (Kesler et al., 2008) Age-coded Andean Au/Cu deposits Things we don’t know • • • Links with strongly extensional but also contractional settings (e.g. Tosdal and Richards, 2001; Sillitoe, 1998). Suspected links between plate kinematics (Bertrand et al., 2014) – but nature of links uncertain Question: Can we statistically evaluate a suite of tectonic parameters, that spatially and temporally correlate with the formation of ore deposits that are associated with porphyry magmatism? Age-coded Andean Au/Cu deposits Age of subducting lithosphere through space and time Age of subducting lithosphere through space and time Age of subducting lithosphere through space and time Age of subducting lithosphere through space and time Age of subducting lithosphere through space and time Age of subducting lithosphere through space and time Age of subducting lithosphere through space and time Age of subducting lithosphere through space and time Age of subducting lithosphere through space and time Age of subducting lithosphere through space and time Time-space map of porphyry magmatism and ‘non-deposits’ • Multiple ways to pick “non-deposits”, with various pros and cons • Can capture multiple (13+) tectonomagmatic properties (features) Plate convergence rate and obliquity through space and time Convergence rates through time Convergence obliquity through time Machine Learning • Random Forests • Support Vector Machines • Multiple Kernel Learning (time series data) http://blog.yhathq.com/posts/random-forests-in-python.html http://www.fast-lab.org/kernelmethods.html Kernel function – transforms data into hyper-dimensional space for improved separation Most popular kernel function (also used here): Radial Basis Function (RBF) Tuning the parameters (13 kinds of herbs and spices) to minimise errors and over fitting (make chicken delicious) colnelsanders.com Hyper-dimensions • Hard to comprehend for us “flatlanders” • Flatland: An 1884 novel by English schoolmaster Edwin Abbott. • Describes a two-dimensional world occupied by geometric figures, where women are line-segments, while men are polygons with various numbers of sides. The narrator A Square guides the readers through life in two dimensions, where additional dimensions are unimaginable. Four parameters have predictive power and SVN works better than Random Forests Importance / Parameter 0.13 Seafloor Age 0.13 Distance to trench edge 0.08 Subducting Plate Normal Vel. 0.08 Subducting Plate Parallel Vel. 0.10 Overriding Plate Normal Vel. 0.08 Overriding Plate Parallel Vel. 0.10 Convergence Normal Vel. 0.06 Convergence Parallel Vel. 0.08 Subduction Polarity 0.09 Subduction Obliquity 0.07 Subduction Obliquity Signed Probability of Au/Cu ore formation through space and time When do porphyry copper/gold deposits form? N Rapid convergence rates (~100 km/Myr) Subduction obliquity of ~15°, Subducting plate age between ~2570 Myr old Location far (>2000 km) from the boundary (edge along strike) of a subducting trench • (i.e the closest triple junction) S • • • • N. Butterworth, D. Steinberg, R.D. Müller, S.Williams, A. Merdith, S. Hardy, Tectonic environments of South American porphyrycopper magmatism through time revealed by spatiotemporal data mining,Tectonics, in review. Conclusions • pyGPlates enables “deep-time” 4D data science • A large variety of geological data can be quantitatively analysed in the context of plate motions and plate boundary evolution Animation based on: Matthews, K.J., Maloney, K.T., Zahirovic, S., Williams, S.E., Seton, M. and Müller, R.D., 2016, Global plate boundary evolution and kinematics since the late Paleozoic, Global and Planetary Change, in review.