Advances in machine learning for modelling and understanding in earth sciences

Document typeConference report
Defense date2020
PublisherBarcelona Supercomputing Center
Rights accessOpen Access
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Abstract
The Earth is a complex dynamic network system. Modelling and understanding the system
is at the core of scientific endeavour. We approach these problems with machine
learning algorithms. I will review several ML approaches we have developed in the last
years: 1) advanced Gaussian processes models for bio-geo-physical parameter estimation,
which can incorporate physical laws, blend multisensor data while providing credible
confidence intervals for the estimates and improved interpretability, 2) nonlinear
dimensionality reduction methods to decompose Earth data cubes in spatially-explicit
and temporally-resolved modes of variability that summarize the information content of
the data and allow for identifying relations with physical processes, and 3) advances in
causal inference that can uncover cause and effect relations from purely observational
data.
CitationCamps-Valls, G. Advances in machine learning for modelling and understanding in earth sciences. A: . Barcelona Supercomputing Center, 2020, p. 51-52.
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