Download pygplates – a GPlates Python library for data analysis

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 ?
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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.
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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
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Time-encoded data describing
Andean ore deposits by age,
lithology, geochemistry
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Machine Learning
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Investigate controlling factors
on why, where, when porphyry
copper-gold ore deposits form
Investigate tectonic environments of Andean
porphyry Au/Cu deposits through time
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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
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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?
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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)
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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.