- Dit evenement is voorbij.
Virtual: PGK monthly meeting
wo 17-06, 17:30–18:30
The monthly lecture will now be held as a virtual meeting. Our guest speaker will be Jaap Mondt (Breakaway) and he will give a talk about “Machine Learning, the way forward for Geophysical Applications?”. The lecture will start at 17:30 hrs and end about one hour later. You can join this lecture by clicking the link below shortly before the start of the meeting:
Join Microsoft Teams Meeting
Alternatively, dial +31 20 258 8601 and use conference ID: 175 471 526#
17:30-18:30 hrs: Lecture (Virtual)
More and more Machine Learning will play a role not only in society in general but also in the geosciences. Machine Learning resorts under the overall heading of Artificial Intelligence. In this domain often the word “Algorithms” is used to indicate that computer algorithms are used to obtain results. Also, “Big Data” is mentioned, indicating that these algorithms need a fast amount of training data to produce useful results.
Many scientists mention “Let the data speak for itself” when referring to Machine Learning, indicating that hidden or latent relationships between observations and classes of (desired) outcomes can be derived using these algorithms. Examples are not only in seismic acquisition, processing, and interpretation, but also in the non-seismic domain. When no clear theoretical model, in the form of equations, can be formulated to describe a geophysical phenomenon, Machine Learning might find useful statistical relationships. From a range of labelled data, we can derive a linear/nonlinear relationship (model in ML terminology) that predicts the label (supervised learning) of new data (instances in ML terminology). But sometimes it is already useful if an algorithm can define separate clusters, which then still need to be interpreted (unsupervised learning). Even more sophisticated is Semi-supervised learning: labelled and unlabelled data together are clustered whereby the unlabelled data receives the label of the dominant class present in the cluster.