A human-on-the-loop approach for labelling seismic recordings from landslide site via a multi-class deep-learning based classification model

Abstract

With the increased frequency and intensity of landslides in recent years, there is growing research on timely detection of the underlying subsurface processes that contribute to these hazards. Recent advances in machine learning have introduced algorithms for classifying seismic events associated with landslides, such as earthquakes, rockfalls, and smaller quakes. However, the opaque, “black box” nature of deep learning algorithms has raised concerns of reliability and interpretability by Earth scientists and end-users, hesitant to adopt these models. Leveraging on recent recommendations on embedding humans in the Artificial Intelligence (AI) decision making process, particularly training and validation, we propose a methodology that incorporates data labelling, verification, and re-labelling through a multi-class convolutional neural network (CNN) supported by Explainable Artificial Intelligence (XAI) tools, specifically, Layer-wise Relevance Propagation (LRP). To ensure reproducibility, a catalogue of training events is provided as supplementary material. Evaluation from the French Seismologic and Geodetic Network (Résif) dataset, gathered in the Alps in France, demonstrate the effectiveness of the proposed methodology, achieving a recall/sensitivity of 97.3% for rockfalls and 68.4% for quakes.

Keywords

• Seismic signal analysis
• Microseismic signal classification
• Deep learning
• Explainable artificial intelligence
• Data annotation
• Model training