An evaluation of adaptive-beamforming techniques applied to recorded seismic data

TLDR: In this paper, the authors investigated the adaptive beamforming detection gain relative to beamsteering for unmixed long-period seismic events in the presence of background noise and for simulated mixed events where two data samples, each containing a signal, are summed to create a composite sample containing an interfering event.

Abstract: : This report presents time-domain maximum likelihood adaptive beamforming results for data from the Alaska Long-Period Array and the Korean Short-Period Array. Adaptive-beamforming detection gain relative to beamsteering is investigated for unmixed long-period seismic events in the presence of background noise and for simulated mixed events where two data samples, each containing a signal, are summed to create a composite sample containing an interfering event. In the adaptive beamforming studies for background noise, the performance of two different adaptive algorithms, the effect of using closely-spaced partial arrays instead of the full ALPA array, and the effect of different frequency filters applied to the input channels before array beamforming are examined. In the interfering-event analysis, the relative on-azimuth to off-azimuth event strength at which an on-azimuth event detection is possible determines the detection gain of adaptive multichannel filtering over time-shift-and-sum beamforming.