An approach is presented to determine the time-dependent moment tensor and the origin time in addition to commonly derived locations of seismic events using time-reverse imaging (TRI). It is crucial to locate and characterize the occurring micro-seismicity without making a priori assumptions about the sources to fully understand the subsurface processes inducing seismicity. Low signal-to-noise ratios often force standard methods to make assumptions about sources or only characterize selected larger-magnitude events. In TRI, micro-earthquakes are located by back propagating the full recorded time-reversed wavefield through a velocity model until it ideally convergences on the source location. Therefore, it is less affected by low signal-to-noise ratios and potentially locates and characterizes most of the events. After distinguishing artificial convergence locations from source locations, the quality of the source location and the moment tensors are derived by recording the stress at the determined source locations during the back propagation of the time-reversed wavefield. A robust workflow is derived using synthetic test cases in a realistic scenario with velocity models that only approximate the true velocity model and/or noisy displacement traces. The influence of a rudimentary velocity model on the source-location accuracy and characterisation is significant. The proposed workflow handles these less-than optimal station distributions and velocity models. Finally, the derived workflow is successfully applied to field data recorded at the geothermal field of Los Humeros, Mexico. Although only a one-dimensional velocity model is currently available, source locations and (time-dependent) moment tensors could be determined for selected events.
A Bayesian Combined Model for Time-Dependent Turning Movement Proportions Estimation at Intersections
