Data-Driven Discovery in Geosciences: Opportunities and Challenges

Chen et al (2023) published a very interesting special edition editorial for Springer’s Mathematical Geosciences recently.

“This special collection explores scientific research related to data-driven discoveries in geosciences and provides a timely presentation of progress in developments and/or applications of AI and big data approaches to multiple aspects of geosciences. “

I think this next section puts the state of play beautifully…

“Historically, geosciences have progressed using inductive, knowledge-driven (or theory-guided) models by first generating a hypothesis and then collecting evidence to prove or disprove these hypotheses (Agterberg 2020). “

“In geosciences, knowledge-driven models rely on logical reasoning based on prior knowledge gained by geologists, such as plate tectonics, evolutionary theory, and mineral deposit models. However, constructing prior geoscience knowledge is subject to the paucity of (preserved or exposed) rocks and limited observations, which hinder inferences and knowledge discovery.”

“Nonetheless, data-driven science, based on abduction with big data, offers an opportunity for discovering new knowledge through AI techniques (e.g., machine learning and knowledge graphs) without a specific hypothesis (or theory) in mind. “

“The advantages of data-driven discovery include transforming human learning by itself into an integration of both human learning and machine learning, as well as providing answers to known questions and formulating unknown answers to unknown questions (Cheng and Zhao 2020).”

https://link.springer.com/article/10.1007/s11004-023-10054-0