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In the field of nuclear explosion monitoring, it has become a priority to detect, locate, and identify seismic events down to increasingly small magnitudes. The consideration of smaller seismic events has implications for a reliable monitoring regime. Firstly, the number of events to be considered increases greatly; an exponential increase in naturally occurring seismicity is compounded by large numbers of seismic signals generated by human activity. Secondly, the signals from smaller events become more difficult to detect above the background noise and estimates of parameters required for locating the events may be subject to greater errors. Thirdly, events are likely to be observed by a far smaller number of seismic stations, and the reliability of event detection and location using a very limited set of observations needs to be quantified. For many key seismic stations, detection lists may be dominated by signals from routine industrial explosions which should be ascribed, automatically and with a high level of confidence, to known sources. This means that expensive analyst time is not spent locating routine events from repeating seismic sources and that events from unknown sources, which could be of concern in an explosion monitoring context, are more easily identified and can be examined with due care. We have obtained extensive lists of confirmed seismic events from mining and other artificial sources which have provided an excellent opportunity to assess the quality of existing fully-automatic event bulletins and to guide the development of new techniques for online seismic processing. Comparing the times and locations of confirmed events from sources in Fennoscandia and NW Russia with the corresponding time and location estimates reported in existing automatic bulletins has revealed substantial mislocation errors which preclude a confident association of detected signals with known industrial sources. The causes of the errors are well understood and are primarily the result of spurious identification and incorrect association of phases, and of excessive variability in estimates for the velocity and direction of incoming seismic phases. The mitigation of these causes has led to the development of two complimentary techniques for classifying seismic sources by testing detected signals under mutually exclusive event hypotheses. Both of these techniques require appropriate calibration data from the region to be monitored, and are therefore ideally suited to mining areas or other sites with recurring seismicity. The first such technique is a classification and location algorithm where a template is designed for each site being monitored which defines which phases should be observed, and at which times, for all available regional array stations. For each phase, the variability of measurements (primarily the azimuth and apparent velocity) from previous events is examined and it is determined which processing parameters (array configuration, data window length, frequency band) provide the most stable results. This allows us to define optimal diagnostic tests for subsequent occurrences of the phase in question. The calibration of templates for this project revealed significant results with major implications for seismic processing in both automatic and analyst reviewed contexts: • one or more fixed frequency bands should be chosen for each phase tested for. • the frequency band providing the most stable parameter estimates varies from site to site and a frequency band which provides optimal measurements for one site may give substantially worse measurements for a nearby site. • slowness corrections applied depend strongly on the frequency band chosen. • the frequency band providing the most stable estimates is often neither the band providing the greatest SNR nor the band providing the best array gain. For this reason, the automatic template location estimates provided here are frequently far better than those obtained by analysts. The second technique is that of matched field processing whereby spatial covariance matrices calculated from large numbers of confirmed events from a single site can be used to generate calibrated narrow-band steering vectors which can replace the theoretical plane-wave steering vectors of traditional f-k analysis. This provides a kind of fingerprint which is specific to a given source region and is effective to higher frequencies than traditional beamforming since deviations from the theoretical planewave model are compensated for in the calibrations. The narrow-band nature of the technique makes the source identification most sensitive to the spatial nature of the recorded wavefield and less sensitive to the temporal nature. This may make the method far more suitable for events with very complicated seismic sources than full waveform-correlation methods

Final Scientific Report, Integrated Seismic Event Detection and Location by Advanced Array Processing