Assessing Seismic AI Models with Uncertainty: A Framework

Published Date : 30/07/2025 

This article presents a robust evaluation framework for deep learning models (DLMs) in seismic data analysis, focusing on performance uncertainty and learning efficiency. 

Artificial intelligence has revolutionized the seismic community with deep learning models (DLMs) that are trained to perform specific tasks within complex workflows. However, there is still a significant gap in the robust evaluation and comparison of these models. In this article, we address this gap by designing a comprehensive evaluation framework that incorporates two crucial aspects: performance uncertainty and learning efficiency.

To achieve this, we meticulously construct the training, validation, and test splits using a clustering method tailored to seismic data. This ensures that the data is representative and diverse, which is essential for training models that generalize well to new, unseen data. Additionally, we implement an expansive training design to segregate performance uncertainty arising from stochastic training processes and random data sampling. This approach helps in identifying the true performance of the models and reduces the risk of overfitting to the training data.

The framework's ability to guard against misleading declarations of model superiority is demonstrated through the evaluation of PhaseNet (Zhu and Beroza, 2018), a popular seismic phase picking DLM, under three different training approaches. PhaseNet is a deep learning model designed to accurately identify seismic phases, which are critical for understanding the nature and location of seismic events. By evaluating PhaseNet under various conditions, we can better understand its strengths and limitations and provide valuable insights for practitioners in the field.

One of the key aspects of our evaluation framework is the analysis of model performance with uncertainty at varying budgets of training data. This is particularly important because it helps practitioners choose the best model for their specific problem and set realistic performance expectations. For example, in environments where data collection is expensive or time-consuming, it is crucial to know how much data is needed to achieve a certain level of performance. Our framework provides this information by explicitly analyzing the trade-off between data quantity and model performance.

Another important feature of our framework is its ability to handle the stochastic nature of deep learning training processes. Deep learning models often exhibit significant variability in performance due to the randomness in the training process, such as the initialization of weights and the selection of mini-batches. By systematically evaluating these variations, we can provide a more accurate and reliable assessment of the model's capabilities.

In conclusion, the evaluation framework presented in this article offers a robust and comprehensive approach to assessing deep learning models in seismic data analysis. By focusing on performance uncertainty and learning efficiency, we provide a valuable tool for researchers and practitioners to make informed decisions about the use of AI in seismic applications. This framework not only helps in choosing the best model for a given problem but also sets realistic performance expectations, which is crucial for the successful deployment of AI in the seismic community.

The Seismological Society of America (SSA) is a leading professional organization dedicated to advancing the understanding of earthquakes and their effects. The society publishes several high-impact journals and supports research, education, and public outreach in the field of seismology. The work presented in this article is a testament to the ongoing efforts of the SSA to promote cutting-edge research and innovation in seismic data analysis. 

Frequently Asked Questions (FAQS): 

Q: What is the main focus of the evaluation framework discussed in the article?

A: The main focus of the evaluation framework is to incorporate performance uncertainty and learning efficiency in the assessment of deep learning models for seismic data analysis.

Q: How does the framework handle the stochastic nature of deep learning training processes?

A: The framework systematically evaluates the variability in model performance due to the randomness in the training process, such as the initialization of weights and the selection of mini-batches.

Q: What is PhaseNet and why is it significant in this context?

A: PhaseNet is a popular deep learning model designed to accurately identify seismic phases. It is significant in this context because it is used to demonstrate the effectiveness of the evaluation framework.

Q: Why is it important to analyze model performance with uncertainty at varying budgets of training data?

A: Analyzing model performance with uncertainty at varying budgets of training data helps practitioners choose the best model for their specific problem and set realistic performance expectations, especially in environments where data collection is expensive or time-consuming.

Q: What is the role of the Seismological Society of America (SSA) in this research?

A: The Seismological Society of America (SSA) is a leading professional organization that supports research, education, and public outreach in the field of seismology. The work presented in this article is part of the SSA's efforts to promote cutting-edge research and innovation in seismic data analysis. 

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