AI: Deep Learning for Geophysical Applications and Agent-Based Modelling

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AI: Deep Learning for Geophysical Applications

Building a new course “Deep Learning for Geophysical Applications,” I dived “deeply” into the Artificial Intelligence literature and software. There I encountered Agent-Based-Modelling but found it difficult to understand its use.

Until yesterday, when I attended a presentation by Natalie Davis at Complex Systems at the University of Utrecht. She developed an “Agent-based model for exploring transitions to a sustainable food system”, a most relevant topic at the moment. In that model the agents or stakeholders, interacting with the environment (society), were consumers, supermarkets, farmers, and banks. Each individual consumer (agent) makes dietary decisions based on economic, taste, health, and ethical considerations, whereas farmers focus on land uses and management practices, among others. The degree of influence of these motivations can be programmed stochastically as decisions of each agent can vary. In the presentation it became clear to me how in the software the interaction between the stakeholders (agents) and their environment can be implemented.

I do not see an immediate application in Geophysics, unless it is how to model the requests for my courses in the geophysical community, which is how to model the motivations of geoscientists (agents) to stay in tune with the latest technology.


I guess you are intelligent enough to realize that you cannot survive in the world of geosciences without using Artificial Intelligence. Therefore, test whether you have sufficient background in Geophysics, Geology, Statistics and the basics of Artificial Intelligence to follow the courses “Machine learning for Geophysical Applications”, “Deep learning for Geophysical Applications” or “Geophysics for Data Scientists”. To test yourself go to