We are a team of focused and driven geoscientists working together to provide assistance to the exploration industry. We take particular pride in meeting our clients' specific technical and financial requirements whilst following the industry’s best practice procedures. We deliver innovative solutions for streamlining data collection and data analysis workflows, hence our name Streamline Geo.
We are a team of focused and driven geoscientists working together to provide assistance to the exploration industry. We take particular pride in meeting our clients' specific technical and financial requirements whilst following the industry’s best practice procedures. We deliver innovative solutions for streamlining data collection and data analysis workflows, hence our name Streamline Geo.
Leveraging data science to build supervised machine learning models for your litho-geochemical classification projects.
Machine learning has long been reserved to specialised data scientists. This now coming to an end thanks to open-source and graphical coding interfaces (code-free).
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We can help you lift the ‘black box’ over machine learning, design, implement, and train you model. We want you to be able to squeeze value out of your datasets and make your own predictive models.
Standard lithogeochemical fingerprinting methods are restricted to the use of two or three variables at a time.
Machine Learning-based methods allow for a multivariate approach to classification, potentially increasing classification success. More value can be extracted from multi-element geochemical data using such a method.
Furthermore, building reusable models saves you time and effort when you want to analyse more data. We can help you fingerprint rock type, alteration, and mineralisation with Machine Learning models that get better and better as you collect and feed more data into them.
