Explainable AI for transparent seismic signal classification

Deep learning has found extensive applications in classifying seismic signals in recent years. However, as a black box algorithm, deep learning is still rarely exploited in real-world applications, such as landslide monitoring. This is particularly a concern for geoscientists who prefer to classify seismic signals based on their physical properties, through feature engineering. To build trust in deep learning model outputs, we propose a CNN multi-classifier architecture to classify seismic signals into four classes (earthquake, micro-quake, rockfall and noise), and explain its outputs based on Layer-wise Relevance Propagation. We demonstrate that the provided explanations can lead to a more interpretable model by relating network outputs to geophysical phenomena and showing that distinguishing features extracted by the network are aligned with those identified by geoscientists as pertinent to classes of interest.