Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

贝叶斯滤波与平滑 (Bayesian filtering and smoothing)

Enhanced (or engineered) geothermal systems (EGS) have evolved from the hot dry rock concept, implemented for the first time at Fenton Hill in 1977. This paper systematically reviews all of the EGS projects worldwide, based on the information available in the public domain. The projects are classified by country, reservoir type, depth, reservoir temperature, stimulation methods, associated seismicity, plant capacity and current status. Thirty five years on from the first EGS implementation, the geothermal community can benefit from the lessons learnt and take a more objective approach to the pros and cons of ‘conventional’ EGS systems.

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A systematic review of enhanced (or engineered) geothermal systems: past, present and future

Global review of human-induced earthquakes

Human activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. Although the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This quiet period provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of human activities.

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Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures.

Visual testing, as one of the oldest methods for nondestructive testing (NDT), plays a large role in the inspection of civil infrastructure. As NDT has evolved, more quantitative techniques...

Video Camera–Based Vibration Measurement for Civil Infrastructure Applications

Over time railway networks have become complex Systems characterized by manifold types of technical components with a broad range of age distribution. De facto, about 50 percent of the life cycle costs of railway infrastructures are made up by direct and indirect maintenance costs. A remedy can be provided by a condition based preventive maintenance strategy leading to an optimized scheduling of maintenance actions taking the actual aswell as the expected future infrastructure condition into account. A prerequisite is, however, that the thousands of Kilometers of railway tracks are almost continuously monitored. Thus, a promising approach is the usage of low-cost sensors, e.g. accelerometers and gyroscopes, which can be installed on common in-line freight and passenger trains. Due to ambiguous data records a credible classification of railway track irregularities directly from these data is challenging. Alternatively to this pure data-driven approach, in this paper a novel hybrid Approach is presented. To this end, a simplified vehicle Suspension model is applied for the purpose of railway track condition monitoring by analyzing the dynamic railway track - Train interactions. The inversion of the model can be used to recalculate the actual inputs (irregularities) of the monitored system (rail surface) which have caused recorded System Responses (dynamic vehicle reactions and acceleration data, respectively). These recalculated inputs are a sound Basis of subsequent data-driven condition monitoring analyses. In this preliminary study, a classification algorithm is implemented to identify a simulated railway track irregularity automatically.

/pdf/improved-railway-track-irregularities-classification-by-a-2728m5zawp.pdf

Improved Railway Track Irregularities Classification by a Model Inversion Approach

Strengthening the Rail Mode of Transport by Condition Based Preventive Maintenance

This thesis aims at a better understanding of the spatial and temporal variations of the amplitudes as well as the statistical properties of the (urban) seismic noise. A new statistical time series classification is presented which is used to conduct an analysis of the spatial and temporal variations of seismic noise between 8 mHz and 45 Hz in a metropolitan area. Furthermore, it is used to realise data selection approaches for the estimation of Green's functions from seismic noise.

Broadband seismic noise : classification and Green's function estimation

Several of the new techniques using the deep underground, such as geothermal power plants or CO2 sequestration, have to be installed in densely populated areas to be economically successful. Geothermal power plants need access to the district heating network to efficiently use the low-temperature heat remaining after power production. Therefore, even weak and non-destructive induced earthquakes became a serious problem for operators of geothermal power plants as such events are felt by many residents. This provokes a loss of acceptance for the new technologies in the local population. The potential application of CO2 sequestration struggles with similar problems as large coal power plants are also installed in densely populated areas. A transparent and comprehensive (seismological) monitoring of new interventions in the underground is crucial to get and keep the public acceptance. Seismological monitoring in cities and densely-populated areas is a challenging task due to the complexity of the man-made seismic noise wave field. Especially the important identification of the small events which are unnoticeable for humans is made difficult by numerous other man-made sources of seismic energy such as traffic and industry. Man-made seismic signals are the dominant source of seismic energy in the frequency range of interest above 1 Hz. A good knowledge and understanding of the seismic noise wave field in densely populated areas is important for the successful planning and operation of seismological monitoring networks. Especially the identification of suitable measuring sites is important as the installation of entire networks in boreholes is hardly possible for economic or practical reasons. Furthermore, the reliable identification of small earthquakes requires a good knowledge of the local seismic noise wave field besides other parameters (e.g. velocity structure, etc.). We present a statistical classification scheme in the time domain to quantify and characterise automatically the seismic noise wave field. The character of seismic noise (e.g. Gaussian distributed or dominated by single transient signals) is represented by only six noise classes. This approach allows us to easily visualise the seismic noise properties (amplitude and statistical properties). Furthermore, it provides a reduced dataset from broadband seismic waveforms to analyse temporal and spatial changes of seismic noise conditions. We use this new classification scheme in combination with a spectral time-frequency analysis to demonstrate the most important properties of the urban seismic noise wave field especially in the frequency range important for seismological monitoring. We select representative seismological measurements in the city of Bucharest, Romania, and in the vicinity of geothermal power plants in south-western Germany for our discussion of the seismic noise wave field in large cities and densely populated areas.

https://publikationen.bibliothek.kit.edu/1000038621/2978517

Seismic noise: A challenge and opportunity for seismological monitoring in densely populated areas

We demonstrate how positioning concepts enable in-service condition monitoring of railway tracks Specifically, it is shown that accurate georeferencing of monitoring data can be achieved by sensor fusion of GNSS and IMU measurements with a map of the railway network Because such georeferencing is an offline positioning problem, a two-stage approach that operates on batches of data is developed: First, path hypotheses are estimated from the GNSS data and the railway map Second, a nonlinear Rauch-Tung-Striebel smoother provides on-track positions and speeds in path coordinates given each path hypothesis and the IMU and GNSS data The developed methods are an essential part of a track condition monitoring system developed at DLR The positioning results are used for the track-dependent analysis of axle-box-acceleration data Accordingly, all results shown in this paper have been obtained on real data collected in the harbor railway network of Braunschweig, Germany

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Jörn Christoffer Groos

Papers

Improved Railway Track Irregularities Classification by a Model Inversion Approach

Strengthening the Rail Mode of Transport by Condition Based Preventive Maintenance

Broadband seismic noise : classification and Green's function estimation

Seismic noise: A challenge and opportunity for seismological monitoring in densely populated areas

Map-Supported Positioning Enables In-Service Condition Monitoring of Railway Tracks