ALBERT

Description: Standard processing of seismic events for reporting in bulletins is usually done one-at-a time. State-of-the-art relative event methods, often involving cross correlation, are increasingly used and have improved estimates of event parameters for event detection, location and magnitude. This is because relative event techniques can simultaneously reduce measurement error and effects of model error. We show how cross correlation can be used to assign relative magnitudes for neighbouring seismic events distributed over a large region in east Asia and quantify to what extent the uncertainty in these values increases as waveform similarity breaks down. We find that cross correlation works well for magnitude comparison of two events when it is expected that they generate very similar signals even if these may be almost buried in large amounts of noise. This may be the case when investigating repeating earthquakes or nuclear explosions within a few kilometres of each other. Cross correlation is the optimal detector in these cases assuming noise is white and Gaussian, and also provides the least-squares solution for the relative amplitudes. However, when the waveform similarity of the underlying signals breaks down, due to interevent separation distance, source time function differences or focal mechanism differences, these assumptions are no longer valid and a bias is introduced into the relative magnitude measurement. This bias due to degradation of waveform similarity is modelled here with synthetics and an analytic expression for it is derived based on three terms—the cross-correlation coefficient (CC), and the signal-to-noise ratio (SNR) of the larger and smaller events. The analytic expression is a good match to the observed bias in the data. If the equation for relative magnitude is rewritten to correct for the bias due to the CC, a new equation results which is simply the log of the ratio of the L2 norms. The bias due to SNRs is still present because the observed waveforms inevitably contain both signal and noise. However, this bias is predicted to be minimal for typical detection thresholds. Making measurements of the ratio of the L2 norms is shown to remove the bias due to degradation of waveform similarity for real data. The scatter of these cross-correlation measurements of relative magnitude is much less than those obtained by differencing magnitudes in a traditional catalogue. Of 14 025 events in and near China, 34 per cent had over an order of magnitude reduction in the median standard deviation (0.0342 magnitude units) as compared to the estimated scatter in the catalogue (0.3454 magnitude units). And 78 per cent of the events show a factor 3 improvement or better in the precision of relative event size measured as the ratio of the L2 norms as compared to the precision of the catalogue for relative magnitudes. These results suggest that the ratio of the L2 norms is an appropriate measure of relative magnitudes for general seismicity of a monitoring region, when there is significant waveform dissimilarity for neighbouring events. This measure maintains a higher degree of measurement precision as compared to the catalogue.

Description: There are basically three ways that have been demonstrated to reliably measure seismic velocity changes in the crust of the Earth—repeating events, controlled sources, and ambient noise. Each has its complementary benefits. Repeating events and controlled sources provide measurements at discrete points in time and space with high signal-to-noise ratios. But to achieve a sufficient signal-to-noise ratio for ambient noise it has been necessary to average over long periods of time and/or numbers of station pairs. Often 30 days has been the average, but researchers have experimented with one-day averages to capture short-term velocity variations at the expense of reduced measurement precision. Coseismic and postseismic velocity changes have been established for many earthquakes. But measuring a clear preseismic signal has continued to elude us. One of the main reasons is due to insufficient temporal sampling for repeating events and controlled sources making the findings inconclusive as to the existence of a preseismic signal because of a lack of data. The benefit of ambient noise monitoring is that it provides continuous measurements in the preseismic period and offers hope to alleviate this problem. This paper improves the measurement precision for one-day ambient noise monitoring by averaging over the three station components to construct the full nine-element Green’s tensor. Doing so, I am able to measure a 95% confidence limit for an upper bound on preseismic velocity changes to be 0.0265%. I also compare my results constructing the Green’s function by the correlation of the coda of the correlation of ambient noise.

Description: We assess seismological evidence bearing on claims that North Korea conducted a small nuclear test on 12 May 2010 in the vicinity of known underground nuclear tests (UNTs) in 2006, 2009, 2013, and 2016. First, we use Lg -wave cross correlation and more traditional methods to locate the 2010 event between about 4 and 10 km southwest of the 2009 test. Second, we compare the relative sizes of regional P and S waves, using stations within 400 km of the known North Korean nuclear tests, to assess the nature of the event. We measured P / S ratios at different frequencies, at first using data from the open station MDJ in northeast China, for training sets of earthquakes and of explosions. We developed a linear discriminant function (LDF) that, in application to P / S measured at MDJ, is most effective in separating the earthquake and explosion populations. MDJ lacks usable data for the event of interest, but we obtained regional data from stations of the nearby Dongbei Broadband Seismographic Network (DBSN) for the 12 May 2010 event and for nearby UNTs conducted in 2006 and 2009. When our LDF is applied to DBSN data, and to data from stations SMT and NE3C in China, the LDF values measured from P / S ratios from known explosions are explosion-like; but for the 12 May 2010 event, the LDF values are earthquake-like for frequencies between 6 and 12 Hz. Our method for characterizing earthquakes and explosions on the basis of their regional signals can be widely applied. Measurements of P / S based on the three-component waveform data provide better discrimination power than do those based on vertical-component data alone. Electronic Supplement: Tutorial material on the Mahalanobis distance-squared measure, three-component linear discriminant function (LDF) analysis, tables of measurements of the log 10 P / S spectral ratios obtained from waveforms recorded at station MDJ for the two training sets and three-component discrimination analysis, and figures of log ( P / S ) values measured at 8 Hz from vertical-component waveforms at station MDJ for two training sets and probability distributions for D .

Description: A surprising discovery has been made that in some cases the complex, highly scattered Lg wave is found to be similar for clusters of events. We analyze in detail a subset of 28 out of 90 events from the 1999 Xiuyan sequence. Cross correlations provide highly accurate differential travel-time measurements. Their error estimated from the internal consistency is about 7 msec. These travel-time differences are then inverted by the double-difference technique to obtain epicenter estimates that have location precision on the order of 150 m. The locations are computed with waveform data from four to five regional stations 500 to 1000 km away. The epicenter estimates are not substantially affected by the sparseness of stations or large azimuthal gaps. Comparison with a surface trace a few kilometers away and location estimates based on much more dense networks led us to conclude that the absolute positions are accurate to the 5-km level. Regional event locations must often be based on a small number of phases and stations due to weak signal-to-noise ratios and sparse station coverage. This is especially true for monitoring work that seeks to locate smaller magnitude seismic events with a handful of regional stations. Two primary advantages of using Lg for detection and location are that it is commonly the largest amplitude regional wave (enabling detection of smaller events) and it propagates more slowly than P waves or Sn (resulting in smaller uncertainty in distance, for a given uncertainty in travel time.

Description: Earthquake location using relative arrival time measurements can lead to dramatically reduced location errors and a view of fault-zone processes with unprecedented detail. There are two principal reasons why this approach reduces location errors. The first is that the use of differenced arrival times to solve for the vector separation of earthquakes removes from the earthquake location problem much of the error due to unmodeled velocity structure. The second reason, on which we focus in this article, is that waveform cross correlation can substantially reduce measurement error. While cross correlation has long been used to determine relative arrival times with subsample precision, we extend correlation measurements to less similar waveforms, and we introduce a general quantitative means to assess when correlation data provide an improvement over catalog phase picks. We apply the technique to local earthquake data from the Calaveras Fault in northern California. Tests for an example streak of 243 earthquakes demonstrate that relative arrival times with normalized cross correlation coefficients as low as approximately 70%, interevent separation distances as large as to 2 km, and magnitudes up to 3.5 as recorded on the Northern California Seismic Network are more precise than relative arrival times determined from catalog phase data. Also discussed are improvements made to the correlation technique itself. We find that for large time offsets, our implementation of timedomain cross correlation is often more robust and that it recovers more observations than the cross spectral approach. Longer time windows give better results than shorter ones. Finally, we explain how thresholds and empirical weighting functions may be derived to optimize the location procedure for any given region of interest, taking advantage of the respective strengths of diverse correlation and catalog phase data on different length scales.

Description: We processed the complete digital seismogram database for northern California to measure accurate differential travel times for correlated earthquakes observed at common stations. Correlated earthquakes are earthquakes that occur within a few kilometers of one another and have similar focal mechanisms, thus generating similar waveforms, allowing measurements to be made via cross-correlation analysis. The waveform database was obtained from the Northern California Earthquake Data Center and includes about 15 million seismograms from 225,000 local earthquakes between 1984 and 2003. A total of 26 billion cross-correlation measurements were performed on a 32-node (64 processor) Linux cluster, using improved analysis tools. All event pairs with separation distances of 5 km or less were processed at all stations that recorded the pair. We computed a total of about 1.7 billion P-wave differential times from pairs of waveforms that had cross-correlation coefficients (CC) of 0.6 or larger. The P-wave differential times are often on the order of a factor of ten to a hundred times more accurate than those obtained from routinely picked phase onsets. 1.2 billion S-wave differential times were measured with CC〉 or =0.6, a phase not routinely picked at the Northern California Seismic Network because of the noise level of remaining P coda. We found that approximately 95% of the seismicity includes events that have cross-correlation coefficients of CC〉 or =0.7 with at least one other event recorded at four or more stations. At some stations more than 40% of the recorded events are similar at the CC〉 or =0.9 level, indicating the potential existence of large numbers of repeating earthquakes. Large numbers of correlated events occur in different tectonic regions, including the San Andreas Fault, Long Valley caldera, Geysers geothermal field and Mendocino triple junction. Future research using these data may substantially improve earthquake locations and add insight into the velocity structure in the crust.

Description: Statistical analyses were conducted on the capability of correlation detectors for similar events. Semiempirical synthetic runs took a 50-sec window on an Lg wave recorded at 750-km distance filtered from 1 to 3 Hz and embedded it 300,000 times in real continuous background seismic noise. The noise was selected for 36 days spread throughout the year to capture diurnal and seasonal variations. No screening for random, unknown signals in the noise was performed. A correlation detector has a 50% probability of detection with 1.5 false alarms per day for a signal-to-noise ratio (SNR) of 0.32, which corresponds to a full magnitude unit reduction in detection threshold over a standard short-term average/long-term average (STA/LTA) technique. A scaled cross-correlation coefficient performs slightly better with one false alarm per day and has fewer false triggers on unknown, random signals. Summing the cross-correlation traces together for all three components enhances the detection signal similar to beamforming. A correlation detector summing the correlation traces for the three components together has a 96% probability of detection with zero false alarms in 36 days for an SNR of 0.32. The significant result of this study is that a correlation detector has more than an order of magnitude improvement in detection threshold for similar events with acceptably low false alarm rates to be used in practice.

Description: There are basically three ways that have been demonstrated to reliably measure seismic velocity changes in the crust of the Earth--repeating events, controlled sources, and ambient noise. Each has its complementary benefits. Repeating events and controlled sources provide measurements at discrete points in time and space with high signal-to-noise ratios. But to achieve a sufficient signal-to-noise ratio for ambient noise it has been necessary to average over long periods of time and/or numbers of station pairs. Often 30 days has been the average, but researchers have experimented with one-day averages to capture short-term velocity variations at the expense of reduced measurement precision. Coseismic and postseismic velocity changes have been established for many earthquakes. But measuring a clear preseismic signal has continued to elude us. One of the main reasons is due to insufficient temporal sampling for repeating events and controlled sources making the findings inconclusive as to the existence of a preseismic signal because of a lack of data. The benefit of ambient noise monitoring is that it provides continuous measurements in the preseismic period and offers hope to alleviate this problem. This paper improves the measurement precision for one-day ambient noise monitoring by averaging over the three station components to construct the full nine-element Green's tensor. Doing so, I am able to measure a 95% confidence limit for an upper bound on preseismic velocity changes to be 0.0265%. I also compare my results constructing the Green's function by the correlation of the coda of the correlation of ambient noise.