Automated Event Location for Noisy Microearthquake Data Using Envelope Stacking and Robust Global Optimization

Most earthquake location methods require phase identification and arrival time measurements. These methods are generally fast and efficient, but not always applicable to microearthquake data with low signal-to-noise ratios, where the phase identification might be very difficult. Migration-based source location methods, which do not require the explicit phase identification are often more suitable for such low quality data. While some of the existing migration-based methods are computationally intensive, others are limited to a certain type of data or make use of only a particular phase of the signal. We present a migration-based event location method especially applicable to data with very low signal-to-noise ratios. In this method, we project the seismograms onto the local ray-centered coordinate system (LTQ) for each source-receiver configuration and compute their envelopes. A time window of predefined length is centered on the computed arrival time of the corresponding phase in each seismogram. We stack the envelopes for these time windows for each component (L, T, Q) individually. Subsequently, the objective function is formulated as the squared sum of the stacked values. We apply a robust global optimization routine called differential evolution to identify the source location. Our procedure provides a complete algorithm with a minimum of control parameters making it suitable for automated processing. The method can be applied to both single and multi-component data, and either P or S or both phases can be used. As a result, this method allows for a flexible application to a wide range of data. We performed an extensive test using synthetic microearthquake data superimposed with 30% white noise. These synthetic data were computed for a complex and heterogeneous model of the Pyhäsalmi ore mine in Finland. Finally, we applied this method to observed microearthquake data.