S U M M A R YIn order to improve our understanding of hazardous underground cavities, the development and collapse of a ∼200 m wide salt solution mining cavity was seismically monitored in the Lorraine basin in northeastern France. The microseismic events show a swarm-like behaviour, with clustering sequences lasting from seconds to days, and distinct spatiotemporal migration. Observed microseismic signals are interpreted as the result of detachment and block breakage processes occurring at the cavity roof. Body wave amplitude patterns indicated the presence of relatively stable source mechanisms, either associated with dip-slip and/or tensile faulting. Signal overlaps during swarm activity due to short interevent times, the high-frequency geophone recordings and the limited network station coverage often limit the application of classical source analysis techniques. To overcome these shortcomings, we investigated the source mechanisms through different procedures including modelling of observed and synthetic waveforms and amplitude spectra of some well-located events, as well as modelling of peak-to-peak amplitude ratios for the majority of the detected events. We extended the latter approach to infer the average source mechanism of many swarming events at once, using multiple events recorded at a single three component station. This methodology is applied here for the first time and represents a useful tool for source studies of seismic swarms and seismicity clusters. The results obtained with different methods are consistent and indicate that the source mechanisms for at least 50 per cent of the microseismic events are remarkably stable, with a predominant thrust faulting regime with faults similarly oriented, striking NW-SE and dipping around 35• -55• . This dominance of consistent source mechanisms might be related to the presence of a preferential direction of pre-existing crack or fault structures. As an interesting byproduct, we demonstrate, for the first time directly on seismic data, that the source radiation pattern significantly controls the detection capability of a seismic station and network.