Knowledge-based seismogram processing by mental images

The 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

Single-trace detection and array-wide coincidence association of local earthquakes and explosions

Local 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

Automated seismogram analysis for the tripartite BUG array : an introduction

The 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

Pattern recognition for earthquake detection

The 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

Impulse response measurement of individual ear canals and impedances at the eardrum in man

Die Messung der Impulsantwort ist eine einfache Methode zur gleichzeitigen Bestimmung der individuellen Querschnittsfunktion des menschlichen Gehörgangs und der Abschlußimpedanz an der Stelle des Trommelfells in einem Frequenzbereich von 1-20 kHz. Zuerst wird die theoretische und numerische Lösung für das inverse Problem del Berechnung einer beliebigen Querschnittsfunktion aus der Impulsantwort angegeben. Zur praktischen Überprüfung dieser Methode wurden einige Versuchsmessungen an speziaiangefertigten Messingrohren ausgeführt, welche hervorragende Ergebnisse liefern. Auch stimmen die Impedanzwerte gut mit der Horn-Impedanz überein, die analytisch berechnet wurde. Des weiteren wird über Einzelergebnisse der Messung am Menschen für acht Personen berichtet

Master-event correlation of weak local earthquakes by dynamic waveform matching

Dynamic 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

Seismotectonic analysis around the Mont Terri rock laboratory (Switzerland): a pilot study

For this pilot study we used recorded seismic events from the SED permanent network and data from a dedicated SNS network to improve the seismotectonic understanding of very weak seismicity in the vicinity of the Mont Terri underground laboratory. We combined field data on faults with microseismic events and modelling of stress and focal mechanisms. Eighty-six events with very low magnitudes (ML ≈ −2.0 to 2.0) recorded between July 2014 and August 2015 were located within a radius of 10 km of the underground laboratory and used for modelling. We compiled 234 fault/striation data from laboratory tunnels and regional geology, and also from seismic/borehole data on basement faults. With this database we defined seven groups of main faults in the cover and four groups in the basement. For each of these groups we computed a synthetic focal mechanism that was subsequently used to determine a synthetic P-phase waveform. The synthetic waveforms were then correlated with the microseismic events of the cover and the basement respectively. Of these, 78 events yielded satisfactorily correlation coefficients that we used for a regional seismotectonic interpretation. The synthetic focal mechanism can be linked to the main regional structural features: the NNE–SSW-oriented reactivated faults associated with the Rhine Graben development, and the NE–SW-oriented reverse faults related to the thrust development of major folds such as the Mont Terri anticline. The results for this pilot study confirm that our affirmative method can be used to augment local and regional seismotectonic interpretations with very weak-intensity earthquake data

Pattern recognition for earthquake detection

The 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 significant 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 low false alarm rate, high timing accuracy and good detection probability of the Sonogram-detector

Nanoseismic Monitoring: Method and First Applications

This article is dedicated to my academic teacher, Prof. Dr. Hans-Peter Harjes

Automated reevaluation of local earthquake data by application of generic polarization patterns for P- and S-onsets

The 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