Towards Foundation Models: Evaluation of Geoscience Artificial Intelligence with Uncertainty

• URL: http://arxiv.org/abs/2501.14809v1
• Date: Wed, 15 Jan 2025 16:45:51 GMT
• Title: Towards Foundation Models: Evaluation of Geoscience Artificial Intelligence with Uncertainty
• Authors: Samuel Myren, Nidhi Parikh, Rosalyn Rael, Garrison Flynn, Dave Higdon, Emily Casleton,
• Abstract summary: geoscience foundation models (FMs) promise to accomplish multiple tasks within a workflow or replace the workflow altogether.<n>We design an evaluation framework that jointly incorporates performance uncertainty, learning efficiency, and overlapping training-test data.<n>Our framework helps practitioners choose the best model for their problem and set performance expectations by explicitly analyzing model performance at varying budgets of training data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) has transformed the geoscience community with deep learning models (DLMs) that are trained to complete specific tasks within workflows. This success has led to the development of geoscience foundation models (FMs), which promise to accomplish multiple tasks within a workflow or replace the workflow altogether. However, lack of robust evaluation frameworks, even for traditional DLMs, leaves the geoscience community ill prepared for the inevitable adoption of FMs. We address this gap by designing an evaluation framework that jointly incorporates three crucial aspects to current DLMs and future FMs: performance uncertainty, learning efficiency, and overlapping training-test data splits. To target the three aspects, we meticulously construct the training, validation, and test splits using clustering methods tailored to geoscience data and enact an expansive training design to segregate performance uncertainty arising from stochastic training processes and random data sampling. The framework's ability to guard against misleading declarations of model superiority is demonstrated through evaluation of PhaseNet, a popular seismic phase picking DLM, under 3 training approaches. Furthermore, we show how the performance gains due to overlapping training-test data can lead to biased FM evaluation. Our framework helps practitioners choose the best model for their problem and set performance expectations by explicitly analyzing model performance at varying budgets of training data.

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