TY - JOUR A1 - Hammer, Conny A1 - Ohrnberger, Matthias A1 - Faeh, Donat T1 - Classifying seismic waveforms from scratch: a case study in the alpine environment JF - Geophysical journal international N2 - Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTAtrigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification system. KW - Time series analysis KW - Neural networks, fuzzy logic KW - Seismic monitoring and test-ban treaty verification KW - Early warning KW - Probability distributions Y1 - 2013 U6 - https://doi.org/10.1093/gji/ggs036 SN - 0956-540X SN - 1365-246X VL - 192 IS - 1 SP - 425 EP - 439 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Ullrich, Sophie Louise A1 - Hegnauer, Mark A1 - Nguyen, Dung Viet A1 - Merz, Bruno A1 - Kwadijk, Jaap A1 - Vorogushyn, Sergiy T1 - Comparative evaluation of two types of stochastic weather generators for synthetic precipitation in the Rhine basin JF - Journal of hydrology N2 - Stochastic modeling of precipitation for estimation of hydrological extremes is an important element of flood risk assessment and management. The spatially consistent estimation of rainfall fields and their temporal variability remains challenging and is addressed by various stochastic weather generators. In this study, two types of weather generators are evaluated against observed data and benchmarked regarding their ability to simulate spatio-temporal precipitation fields in the Rhine catchment. A multi-site station-based weather generator uses an auto-regressive model and estimates the spatial correlation structure between stations. Another weather generator is raster-based and uses the nearest-neighbor resampling technique for reshuffling daily patterns while preserving the correlation structure between the observations. Both weather generators perform well and are comparable at the point (station) scale with regards to daily mean and 99.9th percentile precipitation as well as concerning wet/dry frequencies and transition probabilities. The areal extreme precipitation at the sub-basin scale is however overestimated in the station-based weather generator due to an overestimation of the correlation structure between individual stations. The auto-regressive model tends to generate larger rainfall fields in space for extreme precipitation than observed, particularly in summer. The weather generator based on nearest-neighbor resampling reproduces the observed daily and multiday (5, 10 and 20) extreme events in a similar magnitude. Improvements in performance regarding wet frequencies and transition probabilities are recommended for both models. KW - Rainfall generation KW - Rainfall occurrence KW - Multi-site stochastic weather KW - generator KW - Resampling weather generator KW - Time series analysis Y1 - 2021 U6 - https://doi.org/10.1016/j.jhydrol.2021.126544 SN - 0022-1694 SN - 1879-2707 VL - 601 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - THES A1 - Forster, Florian T1 - Continuous microgravity monitoring of the Þeistareykir geothermal field (North Iceland) N2 - In my doctoral thesis, I examine continuous gravity measurements for monitoring of the geothermal site at Þeistareykir in North Iceland. With the help of high-precision superconducting gravity meters (iGravs), I investigate underground mass changes that are caused by operation of the geothermal power plant (i.e. by extraction of hot water and reinjection of cold water). The overall goal of this research project is to make a statement about the sustainable use of the geothermal reservoir, from which also the Icelandic energy supplier and power plant operator Landsvirkjun should benefit. As a first step, for investigating the performance and measurement stability of the gravity meters, in summer 2017, I performed comparative measurements at the gravimetric observatory J9 in Strasbourg. From the three-month gravity time series, I examined calibration, noise and drift behaviour of the iGravs in comparison to stable long-term time series of the observatory superconducting gravity meters. After preparatory work in Iceland (setup of gravity stations, additional measuring equipment and infrastructure, discussions with Landsvirkjun and meetings with the Icelandic partner institute ISOR), gravity monitoring at Þeistareykir was started in December 2017. With the help of the iGrav records of the initial 18 months after start of measurements, I carried out the same investigations (on calibration, noise and drift behaviour) as in J9 to understand how the transport of the superconducting gravity meters to Iceland may influence instrumental parameters. In the further course of this work, I focus on modelling and reduction of local gravity contributions at Þeistareykir. These comprise additional mass changes due to rain, snowfall and vertical surface displacements that superimpose onto the geothermal signal of the gravity measurements. For this purpose, I used data sets from additional monitoring sensors that are installed at each gravity station and adapted scripts for hydro-gravitational modelling. The third part of my thesis targets geothermal signals in the gravity measurements. Together with my PhD colleague Nolwenn Portier from France, I carried out additional gravity measurements with a Scintrex CG5 gravity meter at 26 measuring points within the geothermal field in the summers of 2017, 2018 and 2019. These annual time-lapse gravity measurements are intended to increase the spatial coverage of gravity data from the three continuous monitoring stations to the entire geothermal field. The combination of CG5 and iGrav observations, as well as annual reference measurements with an FG5 absolute gravity meter represent the hybrid gravimetric monitoring method for Þeistareykir. Comparison of the gravimetric data to local borehole measurements (of groundwater levels, geothermal extraction and injection rates) is used to relate the observed gravity changes to the actually extracted (and reinjected) geothermal fluids. An approach to explain the observed gravity signals by means of forward modelling of the geothermal production rate is presented at the end of the third (hybrid gravimetric) study. Further modelling with the help of the processed gravity data is planned by Landsvirkjun. In addition, the experience from time-lapse and continuous gravity monitoring will be used for future gravity measurements at the Krafla geothermal field 22 km south-east of Þeistareykir. N2 - In meiner Doktorarbeit beschäftige ich mich mit kontinuierlichen Schweremessungen zum Monitoring des geothermisch genutzten Standorts Þeistareykir in Nordisland. Unter Verwendung von hochpräzisen Supraleitgravimetern (iGravs) untersuche ich unterirdische Massenveränderungen, die durch den Betrieb des isländischen Erdwärmekraftwerks (d.h. durch die Entnahme von Heißwasser und Rückinjektion von Kaltwasser) hervorgerufen werden. Als übergeordnetes Ziel des Forschungsprojektes soll eine Aussage zur nachhaltigen Nutzung des geothermischen Reservoirs gemacht werden, von der auch der isländische Energieversorger und Kraftwerksbetreiber Landsvirkjun profitieren soll. Als ersten Schritt, zur Untersuchung der Leistungsfähigkeit und Messstabilität der Gravimeter, begleitete ich im Sommer 2017 Vergleichsmessungen in dem gravimetrischen Observatorium J9 in Straßburg. Aus den dreimonatigen Messzeitreihen untersuchte ich Kalibration, Rausch- und Driftverhalten der iGravs im Vergleich zu den betriebssicher laufenden Observatoriums-Supraleitgravimetern. Nach vorbereitender Arbeit in Island (Aufbau der Gravimeter-Stationen und zusätzlicher Messeinrichtung, Einrichtung der Infrastruktur, Gespräche mit Landsvirkjun und Treffen mit isländischen Partnerinstitut ISOR) startete ich mit meinen Kollegen im Dezember 2017 das Gravimeter-Monitoring in Þeistareykir. Anhand der iGrav-Aufzeichnungen der ersten 18 Monaten nach Messbeginn führte ich die gleichen Untersuchungen (zu Kalibration, Rausch- und Driftverhalten) wie in J9 durch, um zu verstehen inwieweit der Transport der Supraleitgravimeter nach Island die Geräteeigenschaften beeinflusst hat. Im weiteren Verlauf der vorliegenden Arbeit beschäftige ich mich verstärkt mit der Modellierung und Korrektur von oberflächennahen Schwereeffekten in Þeistareykir. Dies umfasst zusätzliche Massenbewegungen durch Regen, Schneefall oder vulkanisch-tektonische Bodenbewegungen, die das geothermische Signal in den Gravimeter-Messungen überlagern. Als Hilfsmittel verwende ich die Datensätze der zusätzlich an jeder Gravimeter-Station eingerichteten Messsensorik und von mir angepasste Modellierungsskripte meiner Gravimetrie-Kollegen. Als dritten Punkt meiner Dissertation untersuche ich die geothermischen Signale in den Gravimeter-Messungen. Gemeinsam mit meiner PhD-Kollegin Nolwenn Portier aus Frankreich führte ich in den Sommern 2017, 2018 und 2019 zusätzliche Schweremessungen mit einem Scintrex CG5 Gravimeter an 26 im Geothermie-Feld verteilten Messpunkten durch. Diese jährlich begrenzten Schweredaten dienen der Verbesserung der räumlichen Auflösung unserer kontinuierlichen iGrav-Messungen. Die kombinierten Ergebnisse beider Messmethoden (der CG5 und iGrav Gravimeter), sowie jährlich im Messgebiet durchgeführter Referenz-Messungen mit einem FG5 Absolut-Gravimeter, komplettieren das hybridgravimetrische Monitoring am Messstandort Þeistareykir. Die abschließende Gegenüberstellung der gravimetrischen Daten mit lokalen Bohrlochmessungen (von Grundwasserpegeln, geothermischen Extraktions- und Injektions-Raten) des Kraftwerksbetreibers, ermöglicht einen direkten Vergleich der beobachteten Schwereveränderungen mit den tatsächlich geförderten geothermischen Fluiden. Ein Ansatz zur Erklärung des beobachteten Schweresignals mittels Vorwärtsmodellierung der geförderten geothermischen Förderrate wird im Abschluss der dritten (hybridgravimetrischen) Studie vorgestellt. Weitere Modellierungen unter Verwendung der aufbereiteten gravimetrischen Messdaten sind durch den Kraftwerksbetreibers von Þeistareykir geplant. Außerdem sollen die gesammelten Erfahrungen des gravimetrischen Messnetzes und Monitorings in Þeistareykir zur Durchführung weiterer gravimetrischer Messungen an dem 22 km südöstlich gelegenem Geothermiefeld Krafla genutzt werden. T2 - Kontinuierliche Schweremessungen zum Monitoring des Geothermalfeldes Þeistareykir (Nordisland) KW - Superconducting gravimetry KW - Geothermal monitoring KW - Time series analysis KW - Þeistareykir Iceland KW - Geothermisches Monitoring KW - Supraleit-Gravimetrie KW - Zeitreihenanalyse KW - Þeistareykir Island Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-548517 ER - TY - JOUR A1 - Belaid, Mohamed Karim A1 - Rabus, Maximilian A1 - Krestel, Ralf T1 - CrashNet BT - an encoder-decoder architecture to predict crash test outcomes JF - Data mining and knowledge discovery N2 - Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder-decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain. KW - Predictive models KW - Time series analysis KW - Supervised deep neural KW - networks KW - Car safety management Y1 - 2021 U6 - https://doi.org/10.1007/s10618-021-00761-9 SN - 1384-5810 SN - 1573-756X VL - 35 IS - 4 SP - 1688 EP - 1709 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Di Giacomo, Domenico A1 - Bindi, Dino A1 - Parolai, Stefano A1 - Oth, Adrien T1 - Residual analysis of teleseismic P-wave energy magnitude estimates: inter- and intrastation variability JF - Geophysical journal international N2 - P>Computing the magnitude of an earthquake requires correcting for the propagation effects from the source to the receivers. This is often accomplished by performing numerical simulations using a suitable Earth model. In this work, the energy magnitude M(e) is considered and its determination is performed using theoretical spectral amplitude decay functions over teleseismic distances based on the global Earth model AK135Q. Since the high frequency part (above the corner frequency) of the source spectrum has to be considered in computing M(e), the influence of propagation and site effects may not be negligible and they could bias the single station M(e) estimations. Therefore, in this study we assess the inter- and intrastation distributions of errors by considering the M(e) residuals computed for a large data set of earthquakes recorded at teleseismic distances by seismic stations deployed worldwide. To separate the inter- and intrastation contribution of errors, we apply a maximum likelihood approach to the M(e) residuals. We show that the interstation errors (describing a sort of site effect for a station) are within +/- 0.2 magnitude units for most stations and their spatial distribution reflects the expected lateral variation affecting the velocity and attenuation of the Earth's structure in the uppermost layers, not accounted for by the 1-D AK135Q model. The variance of the intrastation error distribution (describing the record-to-record component of variability) is larger than the interstation one (0.240 against 0.159), and the spatial distribution of the errors is not random but shows specific patterns depending on the source-to-station paths. The set of coefficients empirically determined may be used in the future to account for the heterogeneities of the real Earth not considered in the theoretical calculations of the spectral amplitude decay functions used to correct the recorded data for propagation effects. KW - Time series analysis KW - Earthquake source observations KW - Body waves KW - Site effects KW - Wave propagation Y1 - 2011 U6 - https://doi.org/10.1111/j.1365-246X.2011.05019.x SN - 0956-540X VL - 185 IS - 3 SP - 1444 EP - 1454 PB - Wiley-Blackwell CY - Malden ER - TY - JOUR A1 - Böttcher, Steven A1 - Merz, Christoph A1 - Lischeid, Gunnar A1 - Dannowski, Ralf T1 - Using Isomap to differentiate between anthropogenic and natural effects on groundwater dynamics in a complex geological setting JF - Journal of hydrology N2 - Due to increasing demands and competition for high quality groundwater resources in many parts of the world, there is an urgent need for efficient methods that shed light on the interplay between complex natural settings and anthropogenic impacts. Thus a new approach is introduced, that aims to identify and quantify the predominant processes or factors of influence that drive groundwater and lake water dynamics on a catchment scale. The approach involves a non-linear dimension reduction method called Isometric feature mapping (Isomap). This method is applied to time series of groundwater head and lake water level data from a complex geological setting in Northeastern Germany. Two factors explaining more than 95% of the observed spatial variations are identified: (1) the anthropogenic impact of a waterworks in the study area and (2) natural groundwater recharge with different degrees of dampening at the respective sites of observation. The approach enables a presumption-free assessment to be made of the existing geological conception in the catchment, leading to an extension of the conception. Previously unknown hydraulic connections between two aquifers are identified, and connections revealed between surface water bodies and groundwater. (C) 2014 Elsevier B.V. All rights reserved. KW - Groundwater KW - Lake KW - Interaction KW - Isometric feature mapping KW - Time series analysis Y1 - 2014 U6 - https://doi.org/10.1016/j.jhydrol.2014.09.048 SN - 0022-1694 SN - 1879-2707 VL - 519 SP - 1634 EP - 1641 PB - Elsevier CY - Amsterdam ER -