Automated Microseismic Waveform Location Using Multichannel Coherency Migration

Abstract

With the proliferation of dense seismic networks on sampling the seismic wavefield, as an effect the recorded microseismic data volumes are getting bigger and automated analysis tools to e.g. detect seismic events are necessary. Here, we propose a new multichannel coherency migration method to detect earthquakes in continuous data and reveal the location and origin time of seismic events directly from recorded waveforms. By continuously calculating the coherency between waveforms from different receiver pairs, this method greatly expands the available information which can be used for location. The method does not require phase picking or phase identification, which allows fully automated waveform location. By migrating the coherency between waveforms, the method leads to improved source energy focusing. We have tested and compared the new method to other migration-based methods in noise-free and noisy synthetic data. The tests and analysis show that the new method is noise resistant and can achieve higher fidelity results compared with other migration-based methods. The new method exhibits excellent location performance and can be easily parallelized giving it large potential to be developed as a real-time location method for very large datasets.