Christopher Alfonso

Christopher Alfonso is a PhD student with the EarthByte Group. He is currently researching the use of spatio-temporal data mining to better understand the formation of major mineral deposits, as well as potential implications for mineral exploration. In 2020, as an Honours student, he investigated the primary controls on the evolution of Northeast Africa’s Nile River using Badlands landscape evolution models. As a research assistant with EarthByte, he helps contribute to the development of the group’s plate tectonic reconstructions.

Spatio-temporal copper prospectivity in the American Cordillera predicted by positive-unlabeled machine learning

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Porphyry copper deposits contain the majority of the world’s discovered mineable reserves of copper. While these deposits are known to form in magmatic arcs along subduction zones, the precise contributions of different factors in the subducting and overriding plates to this process are not well constrained, making predictive prospectivity mapping difficult. Empirical machine learning-based approaches … Read more…

Scientific Reports: Kimberlite eruptions driven by slab flux and subduction angle

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Kimberlites are sourced from thermochemical upwellings which can transport diamonds to the surface of the crust. The majority of kimberlites preserved at the Earth’s surface erupted between 250 and 50 million years ago, and have been attributed to changes in plate velocity or mantle plumes. However, these mechanisms fail to explain the presence of strong … Read more…

Deep time spatio-temporal data analysis using pyGPlates with PlateTectonicTools and GPlately

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Plate Models

PyGPlates is an open-source Python library to visualize and edit plate tectonic reconstructions created using GPlates. The Python API affords a greater level of flexibility than GPlates to interrogate plate reconstructions and integrate with other Python workflows. GPlately was created to accelerate spatio-temporal data analysis leveraging pyGPlates and PlateTectonicTools within a simplified Python interface. This … Read more…

GPlately1.0 released

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GPlately

We have just released GPlately1.0 as a conda package. GPlately was created to accelerate spatio-temporal data analysis leveraging pyGPlates and PlateTectonicTools within a simplified Python interface. GPlately is a python package that enables the reconstruction of data through deep geologic time (points, lines, polygons and rasters), the interrogation of plate kinematic information (plate velocities, rates of subduction … Read more…

STELLAR – Spatio TEmporaL expLorAtion for Resources

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Project STELLAR (Spatio TEmporaL expLorAtion for Resources) is a collaboration between BHP and the EarthByte Group aimed at implementing big and complex spatio-temporal data analysis and modelling to support the needs of BHP in global resource exploration. Split into multiple phases over the next 3.5 years, the project will connect BHP’s warehouse of global resource knowledge with … Read more…