15 Fakultätsübergreifend / Sonstige Einrichtung
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/16
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Item Open Access Master-event correlation of weak local earthquakes by dynamic waveform matching(1993) Joswig, Manfred; Schulte-Theis, HartwigDynamic waveform matching (DWM) performs a non-linear correlation between two seismograms that are similar in shape but may be squeezed or stretched relative to each other. It extends the application of master-event comparisons to seismograms of greater spatial distance and retains the high-timing resolution of correlation techniques that act on the original time series. The DWM approach is applied to data recorded by a small array being part of the BOCHUM UNIVERSITY GERMANY (BUG) network which monitors the mining-induced seismicity in the Ruhr basin of NW Germany. The observed epicentres occur in clusters and therefore display only a limited number of seismogram waveform types. In one application an automatized cluster association with DWM obtains a resolution of about 100 metres at an epicentral distance of 200 to 40 km, using 10-20 defined master events for each region. These results are confirmed both by seismograms from a near-site station for mining-induced events from the Hamm region and by blast reports for a quarry region near Wuppertal. In another application of DWM, array traces from the BUG array are correlated to yield azimuth and slowness for epicentre location. as for the master event application, this approach is tuned for high performance on weak local events using a priori information about the approximate epicentral region. The implemented processes are shown to be capable of locating events with a rate of success equal to the performance of an experienced seismologist when processing all seismogrmas of four years BUG registration.Item Open Access Knowledge-based seismogram processing by mental images(1994) Joswig, ManfredThe impact of pictorial knowledge representation is demonstrated for two examples of time series analysis in seismology. The approaches perform a) automated recognition of known event signatures and b) high-resolution onset timing of later phases. Both methods work well under extreme conditions of noise and achieved human-like performance in recognizing known situations. Crucial for this success of pictorial knowledge representation was the design of suitably scaled images. They must be simple and robust enough to transform the complexity of “real life” data into a limited set of patterns. These patterns differ significantly from the initial data; they correspond more closely to the non-linear weighting of recognized impressions by an experienced scientist. Thus the author addresses the pictorial presentations as mental images. For both reported applications, part of their power comes by model-based image modifications. However, this enhancement is far from demanding a complete theory. Any fractional model already enhances the image adaptation, so mental images are best suited to deal with incomplete knowledge like any other artificial intelligence approach. Cognitive plausibility was found for both the non-linear image scalings and the model-based image modifications. In general, the author's method of pictorial knowledge representation conforms to the concept of mental images by Kosslyn. Any new task will demand the composition of new, dedicated image transformations where some generalized design criteria are derived from the author's applications.Item Open Access Automated reevaluation of local earthquake data by application of generic polarization patterns for P- and S-onsets(1993) Klumpen, Eric; Joswig, ManfredThe particle motion of local earthquake seismograms is affected strongly by the fine structure details of upper crust. The angle of incidence gets frequency dependent, shear wave splitting occurs and strong P-SV conversions contaminate the P-coda. Contrary to teleseism, it is not possible any more to detect S-onsets by conformance tests between data and simple models. Instead, we must derive polarization images in the time-frequency plane that display particle motion without any assumptions. By suitable scaling, these images neutralize all high frequency effects and allow for onset recognition by simple patterns. The method was applied to the 800 events of 1989 evaluated by the Bochum University Germany (BUG) observatory. We determined a 67% success rate with 13% wrong and 20% rejected because of unstable phase energy. For two source regions, the automated results are shown to be more reliable than interactive routine evaluation by man.Item Open Access Automated seismogram analysis for the tripartite BUG array : an introduction(1993) Joswig, ManfredThe tasks for automated epicenter determination in the Bochum University Germany (BUG) small array are subdivided for different signal-processing modules that utilize knowledge-based approaches. The modules are designed for complementary advantages to yield best system performance in an interdependent architecture. This “bottom-up” solution proceeds from reliable waveform parameters to more simple interpretation rules than in seismic expert systems that must cope with traditional detectors as erratic front ends.Item Open Access Pattern recognition for earthquake detection(1990) Joswig, ManfredThe detector algorithms in use at date rely on negative decision logic: based on a model of the ambient noise process they detect all deviations, but many of them are false alarms. The principal alternative to this approach is pattern recognition, which tests on positive correlation with some known signal patterns. The Sonogram-detector realizes this scheme for single seismogram traces. Sonograms display spectral energy versus time. Suitably scaled, these images display only information which is signiffcant to the detection process. Patterns of known earthquakes and noise signals are defined by means of these Images. Event detection is performed by recognizing one of the patterns in the actual sonogram. The overall proceSSing scheme is similar to the visual inspection of seismograms by the human observer. An off-line test Installation for detecting local earthquakes proves the expected ow false alarm rate, high timing accuracy and good detection probability of the Sonogram-detector.Item Open Access Clustering and location of mining induced seismicity in the Ruhr Basin by automated master event comparison based on dynamic waveform matching (DWM)(1993) Schulte-Theis, Hartwig; Joswig, ManfredMost of the local seismicity in the Ruhr Basin can be separated into characteristic clusters of similar, mining induced earthquakes. Each cluster can be represented by a strong master event. Therefore, it is possible to associate weak events to the corresponding clusters by master event comparison. The seismic signal matching is performed by a nonlinear correlation termed DWM for the entire seismogram length. DWM permits stretchings and shortenings between the two signals and overcomes the ambiguities in phase correlation by a consistent matching path. The automatic cluster association searches for the best DWM-correlation between the actual event and all master events of the appropriate epicenter region. Knowing the P- and S-onsets of the master event, they can be transposed to the actual event by the correlation path with one sample accuracy. The method has been applied to all BUG small array recordings 1987–1990 of local events from the Hamm-region to investigate spatial and temporal clustering. Within the clusters, a high percentage of weak events could be located relative to its master event. The temporal clustering resolved seismic activities that typically last a few months per cluster, but single aftershocks occur in the following years.Item Open Access Single-trace detection and array-wide coincidence association of local earthquakes and explosions(1993) Joswig, ManfredLocal earthquakes and explosions can be recognized automatedly for the Bochum University Germany (BUG) small array by a sequence of knowledge-based approaches performed in the field and in the central hub. In single-trace detection, the recognition is based on sonogram patterns adapted for a wide variety of noise conditions on all array sites. The adaptation is performed by two steps: first each pattern is adjusted to the actual signal energy, second all those weaker phases that are below the new detection threshold are excluded. In the hub, a rule-based approach performs the coincidence evaluation. It is described by its 14 rules and the implicit assumptions. This scheme was tested on 1 month of data. The knowledge base consisted of 12 seismograms transformed automatically into the detector's internal knowledge representation of sonograms. The results show excellent performance for noise rejection and quarry blast recognition; for earthquakes clustering, a 85% success is achieved. The network success - usually below the best single performance - could be improved above any single-station optimum. Results of the rule-based approach are compared to the routine processing of the same data by Walsh-detection and the ‘2 of 4’ coincidence voting.