Self-tuning seismic sensors : real-time trigger level adjustments

Typical automated processing of time series data from seismic sensors produces many false signal detections, i.e. detections that are not associated with events of interest to the analyst. This is in part because the analyst does not want to miss any detections that are of interest, so they set the sensor detection parameters to be as sensitive as possible, accepting that this will lead to many false detections. This paper presents a model wherein the data processing parameters for each sensor are dynamically changed to achieve an optimal balance between missing signals from events of interest and detecting false signals. They key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood.