Catastrophic mass movements, such as rock avalanches, glacier collapses, and destructive debris flows, are typically rare events. Their detection is consequently challenging as annotated and verified events used as training data for instrumentation and algorithm tuning are absent or limited. In this work, we explore seismic mass-movement data through the lens of anomaly detection. The idea is to screen out segments of the data that are unlikely to contain mass movements by focusing only on anomalous signals, thereby reducing the number of signals to be studied, making downstream tasks such as expert labeling and clustering of events easier. To extract anomalous signals, we design a triggering algorithm using an anomaly score computed from an isolation forest obtained from sliding windows taken from the continuous data. The extracted signals are subjected to expert labeling and/or further analyzed by dynamic time warping, a popular technique used to evaluate the dissimilarity between different types of signals. We illustrate our approach by (a) mining for seismic signals of hazardous debris-flows in Switzerland's Illgraben catchment and (b) labeling of seismic mass movement data obtained from a Greenland seismometer network.