Rock samples are inherently complex, covering more orders of magnitude in scale and requiring a detailed understanding of texture and chemistry. Combined with analytical systems often designed for other sciences, this often results in a personal approach to interrogating our samples. Deep Learning Neural Networks (DLNN) techniques offer powerful opportunities for Geoscientists. These techniques can shift the requirement for our expertise to different parts of the workflow and allow far larger and more consistent datasets to be created.
In this webinar, Rich Taylor will explore how DLNN can now be seen across many aspects of light, electron, and X-ray microscopy that we use for rock and mineral characterisation. When combined with the collection of quantitative data, these DLNN allow for large datasets to be generated at far greater speed than ever before and herald the arrival of a powerful new generation of characterisation tools going beyond traditional automated mineral classification.
By Attending this Webinar, You Will Learn About:
- The latest generation of mineral characterisation techniques/software
- Mineral classification in light, electron, and X-ray microscopy
- Using quantitative data to build the best datasets
- How neural networks enhance data processing
- Building connected datasets across microscopy platforms
Meet the Webinar Speaker
Rich completed a PhD in Experimental Petrology at the University of Edinburgh in 2009, before moving to Curtin University in Western Australia as a SIMS laboratory specialist. He subsequently held research positions in the School of Earth and Planetary Sciences at Curtin, studying geochemistry and geochronology, specialising in imaging and microanalysis. In 2017, he moved to the University of Cambridge to study magnetic inclusions in Earth’s oldest materials using novel microscopy techniques. In 2019 Rich moved to Zeiss based in Cambourne, UK to take on the global Geosciences Applications Development role.