Identifying and Locating Volcanic Eruptions using Convolutional Neural Networks and Interpretability Techniques

Accurately interpreting past climate variability can be a challenging task, particularly when it comes to distinguishing between forced and unforced changes. In the  case of large volcanic eruptions, ice core records are a very valuable tool but still often not sufficient to link reconstructed anomaly patterns to a volcanic eruption at all or to its geographical location. In this study, we developed a convolutional neural network (CNN) that is able to classify whether a volcanic eruption occurred and its location (northern hemisphere extratropical, southern hemisphere extratropical, or tropics) with an accuracy of 92%.

To train the CNN, we used 100 member ensembles of the MPI-ESM-LR global climate model, generated using the easy volcanic aerosol (EVA) model, which provides the radiative forcing of idealized volcanic eruptions of different strengths and locations. The model considered global sea surface temperature and precipitation patterns 12 months after the eruption over a time period of 3 months.

In addition to demonstrating the high accuracy of the CNN, we also applied layer-wise relevance propagation (LRP) to the model to understand its decision-making process and identify the input data that influenced its predictions. Our study demonstrates the potential of using CNNs and interpretability techniques for identifying and locating past volcanic eruptions as well as improving the accuracy and understanding of volcanic climate signals.