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

SUMMARY Broad-band urban seismic noise (USN) must be considered as a temporally and spatially non-stationary random process. Due to the high variability of USN a single measure like the standard deviation of a seismic noise time-series or the power spectral density at a given frequency is not enough to characterize a sample (time-series) of USN comprehensively. Therefore, we use long-term spectrograms and propose an automated statistical classification in the time domain to quantify and characterize USN. Long-term spectrograms of up to 28 d duration are calculated from a broad-band seismic data set recorded in the metropolitan area of Bucharest, Romania, to identify the frequency-dependent behaviour of the timevariable processes contributing to USN. Based on the spectral analysis eight frequency ranges between 8 mHz and 45 Hz are selected for our proposed time domain classification. The classification scheme identifies deviations from the Gaussian distribution of 4-hr-long timeseries of USN. Our classification is capable to identify Gaussian distributed seismic noise timeseries as well as time-series dominated by transient or periodic signals using six noise classes. Four additional noise classes are introduced to identify corrupt time-series. The performance of the method is tested with a synthetic data set. We also apply the statistical classification for the data set from Bucharest in three time windows (0–4, 8–12 and 13–17 EET) at 11 d in the eight frequency ranges. Only 40 per cent of the analysed time-series are observed to be Gaussian distributed. Most common deviations from the Gaussian distribution (∼47 per cent) are due to the influence of large-amplitude transient signals. In all frequency ranges between 0.04 and 45 Hz significant variations of the statistical properties of USN are observed with daytime, indicating the broad-band human influence on USN. We observe the human activity as a dominant influence on the USN above and below the frequency band of ocean-generated microseism between 0.04 and 0.6 Hz. Our time domain classification and quantification is furthermore capable to resolve the influence of wind on seismic noise and a known site effect variation in the metropolitan area of Bucharest. The information about noise amplitudes and statistical properties derived automatically from broad-band seismic data can be used to select time windows containing adequate data for seismic noise utilization like H/V-studies or ambient noise tomography.

Time domain classification and quantification of seismic noise in an urban environment

SUMMARY

The estimation of the Green's function between two points on the Earth's surface by the cross-correlation of seismic noise time-series became a widely used method in seismology. In general, very long time-series (months to years) as well as massive normalization and/or data selection are necessary to obtain useful cross-correlation functions. One task of this study is to evaluate the influence of different established normalization methods on the obtained cross-correlation functions. Furthermore, we evaluate two waveform preserving time domain normalizations as well as a new fully automated data selection approach. The cross-correlation functions analysed in this study are obtained from 12 months of seismic noise recorded in 2004 at five seismic stations in the United States with station distances on a continental scale. For practical reasons, the cross-correlation functions of such long time-series are calculated by stacking the cross-correlation functions obtained from shorter time windows. We use this stacking process for the implementation of the waveform preserving time domain normalizations. The time window length is in general an important parameter of the cross-correlation processing, as it influences the normalization and data selection. Therefore, we evaluate the cross-correlation functions obtained with 47 different time window lengths between one hr and 24 hr. The time domain normalizations intend to suppress the influence of transient signals like earthquake waves as well as long-term (e.g. seasonal) amplitude variations. We compare the proposed waveform preserving time domain normalizations with the established running absolute mean normalization and the one-bit normalization. We demonstrate that a waveform preserving time domain normalization can replace a non-linear time domain normalization, if a time window length similar to the duration of the typically occurring transient signals is used. Next to the time domain normalizations also the spectral whitening in the frequency domain is evaluated. Spectral whitening is a powerful normalization to improve the emergence of broad-band signals in seismic noise cross-correlations. Nevertheless, we observe spectral whitening to depend strongly on the time window length. An unwanted amplification of a persistent microseism signal is observed on the continental scale with time windows shorter than 12 hr. Our approach of automated data selection is based on a statistical time-series classification and reliably excludes time windows with transient signals occurring contemporaneously at both sites (e.g. earthquake waves). This data selection approach is capable to replace a non-linear time domain normalization, but no improvement of the waveform symmetry or the signal-to-noise ratio of the cross-correlation functions is observed in general.

Performance of different processing schemes in seismic noise cross-correlations

The Upper Rhine Graben in south-western Germany and especially the southern part of Rhineland-Palatinate is one of the regions with the highest potential for deep geothermal power generation in Germany. One geothermal power plant is operated since 2007 in the city of Landau and a second power plant since Oct. 2012 near Insheim (∼4 km south-east of Landau). Further geothermal power plants are currently projected in this region. In 2009 two earthquakes with magnitudes (ML) of 2.4 and 2.7 occurred in direct vicinity of the geothermal reservoir below Landau (depth ∼3 km) and were felt within a radius of several kilometres (intensity up to V+). Furthermore, two felt earthquakes with magnitudes (ML) of 2.2 and 2.4 occurred during the stimulation of the reservoir near Insheim in April 2010. Therefore, a temporary seismic network was deployed and is continuously extended by the Geophysical Institute of the Karlsruhe Institute of Technology with 12 stations (surface and shallow borehole) of the KArlsruhe Broad Band Array KABBA. In total more than 35 stations are operated by four operators for the microseismic monitoring of the region around Landau.

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Microseismicity at two geothermal power plants at Landau and Insheim in the Upper Rhine Graben, Germany

Local seismic shear wave anisotropy is studied with recordings of microearthquakes near Landau and Insheim in the central Upper Rhine Graben, SW Germany. Although the recordings have a low signal-to-noise level and there is a complex heterogeneous 3-D tectonic structure, a time separation δt between horizontally polarised SH-waves and vertically polarised SV-waves can be observed in seismograms and particle motion diagrams. The observations can be explained by azimuthal anisotropy in the upper crust with a direction φ
 0 = 155° east of north for the fast polarisation direction of SV-waves. A gradient of time separation with distance x of δt/x ~ 10 ms/km can explain the data. This model can be interpreted with a classical scenario of fluid-filled (sub-)vertical cracks with a preferred NNW-SSE orientation. Known faults strike NNW-SSE around Landau and Insheim and the seismicity pattern is also oriented in this direction. This direction coincides with the regional orientation of the maximum horizontal stress (σ
 H
 ), and fluids apparently exist at depth as known from geothermal water extraction. Furthermore, we find that 3-D seismic velocity heterogeneities have a much larger influence on the precision of the microearthquake location than the anisotropy effect in this complex tectonic region. This is obvious from the up to five times larger travel time residuals (maximum −0.5 s and +0.35 s), which are used as station corrections during the location procedure, compared to the anisotropic δt observations (maximum 0.1 s).

Detection and Delineation of a Fracture Zone with Observation of Seismic Shear Wave Anisotropy in the Upper Rhine Graben, SW Germany

Rising demands on railroad infrastructure operator by means of profitability and punctuality call for advanced concepts of Prognostics and Health Management. Condition based preventive maintenance aims at strengthening the rail mode of transport through an optimized scheduling of maintenance actions based on the actual and prognosticated infrastructure condition, respectively. When applying model-based algorithms within the framework of Prognostics and Health Management unknown model parameters have to be identified first. Which of these parameters should be known as precisely as possible can be figured out systematically by a sensitivity analysis. A comprehensive global sensitivity analysis, however, might be prohibitive by means of computation load when standard algorithms are implemented. In this study, it is shown how global parameter sensitivities can be calculated efficiently by combining Polynomial Chaos Expansion and Point Estimate Method principles. The proposed framework is demonstrated by a model inversion problem which aims to recalculate the track quality by measurements of the vehicle acceleration, i.e. analyzing the dynamic railway track-vehicle interaction.

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Jörn Christoffer Groos

Papers

Time domain classification and quantification of seismic noise in an urban environment

Performance of different processing schemes in seismic noise cross-correlations

Microseismicity at two geothermal power plants at Landau and Insheim in the Upper Rhine Graben, Germany

Detection and Delineation of a Fracture Zone with Observation of Seismic Shear Wave Anisotropy in the Upper Rhine Graben, SW Germany

Global Sensitivity Analysis applied to Model Inversion Problems: A Contribution to Rail Condition Monitoring