Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking to elucidate the reasoning behind those predictions. The combination of predictive power and enhanced transparency makes IML a promising approach for uncovering relationships in data that may be overlooked by traditional analysis. Despite its potential, the broader implications for the field have yet to be fully appreciated. Meanwhile, the rapid proliferation of IML, still in its early stages, has been accompanied by instances of careless application. In response to these challenges, this paper focuses on how IML can effectively and appropriately aid geoscientists in advancing process understanding—areas that are often underexplored in more technical discussions of IML. Specifically, we identify pragmatic application scenarios for IML in typical geoscientific studies, such as quantifying relationships in specific contexts, generating hypotheses about potential mechanisms, and evaluating process-based models. Moreover, we present a general and practical workflow for using IML to address specific research questions. In particular, we identify several critical and common pitfalls in the use of IML that can lead to misleading conclusions, and propose corresponding good practices. Our goal is to facilitate a broader, yet more careful and thoughtful integration of IML into Earth science research, positioning it as a valuable data science tool capable of enhancing our current understanding of the Earth system.
可解释机器学习（IML）近年来发展迅速，为增进我们对复杂地球系统的理解提供了新的机遇。IML 超越了传统的机器学习，它不仅能做出预测，还能阐明这些预测背后的原因。预测能力和更高透明度的结合，使得 IML 成为一种很有前途的方法，可以揭示传统分析可能忽略的数据关系。尽管 IML 潜力巨大，但其对该领域的广泛影响仍有待充分认识。与此同时，IML 仍处于早期阶段，在其迅速普及的同时，也出现了应用不慎的情况。为了应对这些挑战，本文重点讨论了 IML 如何有效、适当地帮助地球科学研究者加深对过程的理解--而这些领域在关于 IML 的技术讨论中往往未得到充分探讨。具体来说，我们确定了 IML 在典型地球科学研究中的实用应用场景，如量化特定环境中的关系、生成潜在机制假设以及评估基于过程的模型。此外，我们还介绍了使用 IML 解决具体研究问题的通用实用工作流程。特别是，我们指出了使用 IML 时可能导致误导性结论的几个关键和常见陷阱，并提出了相应的良好做法。我们的目标是促进 IML 更广泛、更仔细、更周到地融入地球科学研究，将其定位为一种有价值的数据科学工具，能够增强我们目前对地球系统的理解。
