Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backward optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network‐related geoscience research.

中文翻译：

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地球科学的可物理解释的神经网络：对地球系统变异性的应用

尽管神经网络在使用上的普遍局限性在于缺乏解释网络学习内容和决策方式的方法，但神经网络在地球科学中已变得越来越普遍。因此，在地球科学领域，神经网络经常被用来在给定一组输入的情况下最准确地识别所需的输出，并将对网络学习内容的解释用作辅助度量，以确保网络针对正确的情况做出正确的决定。原因。但是，近年来，神经网络解释技术已经变得更加先进，因此我们建议使用神经网络的最终目标也可以是对网络所学知识的解释，而不是对输出本身的解释。我们表明，对神经网络的解释可以使人们在地球科学数据内发现具有科学意义的联系。特别是，我们使用两种方法进行神经网络解释，称为后向优化和分层相关性传播，这两种方法都将网络的决策路径投影回原始输入维。据我们所知，LRP尚未应用于地球科学研究，我们相信它在该领域具有巨大的潜力。我们将展示如何通过将这些解释技术应用于常见的气候模式，从而从神经网络可靠地推断出具有科学意义的信息。