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Online
Dr Shuvajit Bhattacharya
Multivariate time series clustering and class-based machine learning (ML) are relatively new concepts in geosciences; they have an immense potential to improve our models and provide more geologic insights than traditional baseline ML models. Seismic and wireline logs are a form of time series or depth series that share interdependence or conditional dependence with each other, depending on the rock type. Moreover, seismic and log data are highly redundant from an ML modeling perspective. We often do not consider these fundamental features of our datasets in ML models. This results in reduced explainability and troubleshooting of ML models and our models’ failure when the boundary conditions change slightly. This talk will discuss the promises and challenges of semi-supervised time series clustering and class-based ML to solve these challenges. I will show an example of accurately and consistently predicting elastic properties of mudrocks using these concepts.
Past webinars can be found on the ASEG Youtube channel.
Contact secretary@aseg.org.au if you have any questions.
