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  • 1
    Electronic Resource
    Electronic Resource
    Neuromonitoring in defibrillation threshold testing. A comparison between EEG, near-infrared spectroscopy and jugular bulb oximetry. (1997)
    Springer
    Journal of clinical monitoring and computing 13 (1997), S. 303-307 
    Details
    ISSN: 1573-2614
    Keywords: Brain, ischemia ; measurement techniques, electroecephalography ; monitoring ; oximetry ; jugular bulb ; near infrared spectroscopy ; ICD
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Medicine
    Notes: Abstract Objectives. The aim was to study the physiological effects of induced ventricular fibrillation and subsequent circulatory arrest for defibrillation threshold testing on the brain using the EEG, jugular bulb oxymetry and near-infrared spectroscopy. Methods. Thirteen patients undergoing surgery for implantable cardioverter-defibrillator implantation or replacement under general anesthesia were included. We continuously monitored the jugular bulb oxygen saturation (SjO2), regional oxygen saturation (rSO2) and the EEG. Results. 59 episodes of circulatory arrest were studied. In all cases the rSO2 fell instantly while the EEG changed within 12 ± 4 seconds after induction. The EEG indicated ischemic changes, ranging from occurrence of rhythmic delta activity to cessation of all electrical activity. On successful defibrillation the rSO2 increased to values in excess of pre-arrest levels and restored towards baseline; the SjO2 initially fell followed by a similar overshoot. Recovery times increased in proportion to arrest duration. Conclusion. Short lasting episodes of circulatory arrest have serious, but transient effects on brain function. The rSO2 is an effective non-invasive tool for monitoring cerebral oxygenation during DFT-testing.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007323823806
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    Paper (German National Licenses)
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  • 2
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    Bayesian trans-dimensional full waveform inversion: synthetic and field data application (2020)
    Oxford University Press
    Details
    Publication Date: 2020-04-25
    Description: SUMMARY Seismic full waveform inversion (FWI) is a state-of-the-art technique for estimating subsurface physical models from recorded seismic waveform, but its application requires care because of high non-linearity and non-uniqueness. The final outcome of global convergence from conventional FWI using local gradient information relies on an informative starting model. Bayesian inference using Markov chain Monte Carlo (MCMC) sampling is able to remove such dependence, by a direct extensive search of the model space. We use a Bayesian trans-dimensional MCMC seismic FWI method with a parsimonious dipping layer parametrization, to invert for subsurface velocity models from pre-stack seismic shot gathers that contain mainly reflections. For the synthetic study, we use a simple four-layer model and a modified Marmousi model. A recently collected multichannel off-shore seismic reflection data set, from the Lord Howe Rise (LHR) in the east of Australia, is used for the field data test. The trans-dimensional FWI method is able to provide model ensembles for describing posterior distribution, when the dipping-layer model assumption satisfies the observed data. The model assumption requires narrow models, thus only near-offset data to be used. We use model stitching with lateral and depth constraints to create larger 2-D models from many adjacent overlapping submodel inversions. The inverted 2-D velocity model from the Bayesian inference can then be used as a starting model for the gradient-based FWI, from which we are able to obtain high-resolution subsurface velocity models, as demonstrated using the synthetic data. However, lacking far-offset data limits the constraints for the low-wavenumber part of the velocity model, making the inversion highly non-unique. We found it challenging to apply the dipping-layer based Bayesian FWI to the field data. The approximations in the source wavelet and forward modelling physics increase the multimodality of the posterior distribution; the sampled velocity models clearly show the trade-off between interface depth and velocity. Numerical examples using the synthetic and field data indicate that trans-dimensional FWI has the potential for inverting earth models from reflection waveform. However, a sparse model parametrization and far offset constraints are required, especially for field application.
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Royal Astronomical Society.
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  • 3
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    Probabilistic Cover‐Basement Interface Map in Cloncurry, Australia, Using Magnetotelluric Soundings (2021)
    American Geophysical Union
    Details
    Publication Date: 2021-07-01
    Print ISSN: 2169-9313
    Electronic ISSN: 2169-9356
    Topics: Geosciences , Physics
    Published by American Geophysical Union
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  • 4
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    Bayesian inversion of magnetotelluric data considering dimensionality discrepancies (2020)
    Oxford University Press
    Details
    Publication Date: 2020-08-21
    Description: SUMMARY Bayesian inversion of magnetotelluric (MT) data is a powerful but computationally expensive approach to estimate the subsurface electrical conductivity distribution and associated uncertainty. Approximating the Earth subsurface with 1-D physics considerably speeds-up calculation of the forward problem, making the Bayesian approach tractable, but can lead to biased results when the assumption is violated. We propose a methodology to quantitatively compensate for the bias caused by the 1-D Earth assumption within a 1-D trans-dimensional Markov chain Monte Carlo sampler. Our approach determines site-specific likelihood functions which are calculated using a dimensionality discrepancy error model derived by a machine learning algorithm trained on a set of synthetic 3-D conductivity training images. This is achieved by exploiting known geometrical dimensional properties of the MT phase tensor. A complex synthetic model which mimics a sedimentary basin environment is used to illustrate the ability of our workflow to reliably estimate uncertainty in the inversion results, even in presence of strong 2-D and 3-D effects. Using this dimensionality discrepancy error model we demonstrate that on this synthetic data set the use of our workflow performs better in 80 per cent of the cases compared to the existing practice of using constant errors. Finally, our workflow is benchmarked against real data acquired in Queensland, Australia, and shows its ability to detect the depth to basement accurately.
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Royal Astronomical Society.
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  • 5
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    Non-linear full waveform inversion of wide-aperture OBS data for Moho structure using a trans-dimensional Bayesian method (2020)
    Oxford University Press
    Details
    Publication Date: 2020-10-20
    Description: Summary Seismic full waveform inversion (FWI) is a powerful method for estimating quantitative subsurface physical parameters from seismic data. As the full waveform inversion is a non-linear problem, the linearized approach updates model iteratively from an initial model, which can get trapped in local minima. In the presence of a high velocity contrast, such as at Moho, the reflection coefficient and recorded waveforms from wide-aperture seismic acquisition are extremely non-linear around critical angles. The problem at the Moho is further complicated by the interference of lower crustal (Pg) and upper mantle (Pn) turning ray arrivals with the critically reflected Moho arrivals (PmP). In order to determine velocity structure near Moho, a non-linear method should be used. We propose to solve this strong non-linear FWI problem at Moho using a trans-dimensional Markov chain Monte Carlo (MCMC) method, where the earth model between lower crust and upper mantle is idealy parameterized with a 1-D assumption using a variable number of velocity interfaces. Different from common MCMC methods that require determining the number of unknown as a fixed prior before inversion, trans-dimensional MCMC allows the flexibility for an automatic estimation of both the model complexity (e.g. the number of velocity interfaces) and the velocity-depth structure from the data. We first test the algorithm on synthetic data using four representative Moho models and then apply to an ocean bottom seismometer (OBS) data from the Mid-Atlantic Ocean. A 2-D finite-difference solution of an acoustic wave equation is used for data simulation at each iteration of MCMC search, for taking into account the lateral heterogeneities in the upper crust, which is constrained from travel time tomography and is kept unchanged during inversion; the 1-D model parameterization near Moho enables an efficient search of the trans-dimensional model space. Inversion results indicate that, with very little prior and the wide-aperture seismograms, the trans-dimensional FWI method is able to infer the posterior distribution of both the number of velocity interfaces and the velocity-depth model for a strong nonlinear problem, making the inversion a complete data-driven process. The distribution of interface matches the velocity discontinuities. We find that the Moho in the study area is a transition zone of 0.7 km, or a sharp boundary with velocities from around 7 km/s in the lower crust to 8 km/s of the upper mantle; both provide nearly identical waveform match for the field data. The ambiguity comes from the resolution limit of the band-limited seismic data and limited offset range for PmP arrivals.
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Royal Astronomical Society.
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  • 6
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    Electroencephalography in Normotensive and Hypertensive Pregnancies and Subsequent Quality of Life (2016)
    Public Library of Science
    Details
    Publication Date: 2016-05-11
    Electronic ISSN: 1932-6203
    Topics: Medicine , Natural Sciences in General
    Published by Public Library of Science
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  • 7
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    Cover thickness uncertainty mapping using Bayesian estimate fusion: leveraging domain knowledge (2019)
    Oxford University Press
    Details
    Publication Date: 2019-08-01
    Description: SUMMARY Thickness of cover over crystalline basement is an important consideration for mineral exploration in covered regions. It can be estimated from a variety of geophysical data types using a variety of inference methods. A robust method for combining such estimates to map the cover–basement interface over a region of interest is needed. Due to the large uncertainties involved, these need to be probabilistic maps. Predominantly, interpolation methods are used for this purpose, but these are built on simplifying assumptions about the inputs which are often inappropriate. The Bayesian estimate fusion is an alternative capable of addressing that issue by enabling more extensive use of domain knowledge about all inputs. This study is intended as a first step towards making the Bayesian estimate fusion a practical tool for cover thickness uncertainty mapping. The main contribution is to identify the types of data assumptions that are important for this problem, to demonstrate their importance using synthetic tests and to design a method that enables their use without introducing excessive tedium. We argue that interpolation methods like kriging often cannot achieve this goal and demonstrate that Markov chain Monte Carlo sampling can. This paper focuses on the development of statistical methodology and presents synthetic data tests designed to reflect realistic exploration scenarios on an abstract level. Intended application is for the early stages of exploration where some geophysical data are available while drill hole coverage is poor.
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
    Published by Oxford University Press on behalf of The Royal Astronomical Society.
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  • 8
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    Bayesian transdimensional seismic full-waveform inversion with a dipping layer parameterization (2019)
    Society of Exploration Geophysicists
    Details
    Publication Date: 2019-10-09
    Description: Seismic full-waveform inversion (FWI) has become a popular tool for estimating subsurface models using the amplitude and phase of seismograms. Unlike the conventional gradient-based approach, Bayesian inference using Markov chain Monte Carlo (MCMC) sampling can remove dependence on starting models and can quantify uncertainty. We have developed a Bayesian transdimensional (trans-d) MCMC seismic FWI method for estimating dipping-layer velocity models, in which the number of layers is unknown. A time-domain staggered-grid finite-difference wave equation solver is used for forward modeling. The FWI and MCMC methods are known to be computationally expensive. Two strategies are used to get practical computational performance. A layer-stripping strategy is used to accelerate sampler convergence, and a parsimonious dipping layer parameterization is used so that the MCMC algorithm can search broadly with fewer iterations. The parameters for each layer are velocity, thickness, and lower interface dip angle. We find that this parameterization has sufficient flexibility to invert for narrow 2D velocity models using small offset data. Model stitching is then used to bring several such inversions together to create larger 2D models. In turn, these can be used as starting models for gradient-based adjoint FWI to image complicated geologic settings. Two synthetic 2D numerical examples, including the Marmousi model, are considered, using seismic data dominated by reflections. Creation of good starting models traditionally requires significant human effort. We determine how much of that effort can be substituted with computation.
    Print ISSN: 0016-8033
    Electronic ISSN: 1942-2156
    Topics: Geosciences , Physics
    Published by Society of Exploration Geophysicists
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  • 9
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    Non-linear full waveform inversion of wide-aperture OBS data for Moho structure using a trans-dimensional Bayesian method (2021)
    Oxford University Press
    Details
    Publication Date: 2024-02-07
    Description: Seismic full waveform inversion (FWI) is a powerful method for estimating quantitative subsurface physical parameters from seismic data. As the full waveform inversion is a non-linear problem, the linearized approach updates model iteratively from an initial model, which can get trapped in local minima. In the presence of a high velocity contrast, such as at Moho, the reflection coefficient and recorded waveforms from wide-aperture seismic acquisition are extremely non-linear around critical angles. The problem at the Moho is further complicated by the interference of lower crustal (Pg) and upper mantle (Pn) turning ray arrivals with the critically reflected Moho arrivals (PmP). In order to determine velocity structure near Moho, a non-linear method should be used. We propose to solve this strong non-linear FWI problem at Moho using a trans-dimensional Markov chain Monte Carlo (MCMC) method, where the earth model between lower crust and upper mantle is idealy parameterized with a 1-D assumption using a variable number of velocity interfaces. Different from common MCMC methods that require determining the number of unknown as a fixed prior before inversion, trans-dimensional MCMC allows the flexibility for an automatic estimation of both the model complexity (e.g. the number of velocity interfaces) and the velocity-depth structure from the data. We first test the algorithm on synthetic data using four representative Moho models and then apply to an ocean bottom seismometer (OBS) data from the Mid-Atlantic Ocean. A 2-D finite-difference solution of an acoustic wave equation is used for data simulation at each iteration of MCMC search, for taking into account the lateral heterogeneities in the upper crust, which is constrained from travel time tomography and is kept unchanged during inversion; the 1-D model parameterization near Moho enables an efficient search of the trans-dimensional model space. Inversion results indicate that, with very little prior and the wide-aperture seismograms, the trans-dimensional FWI method is able to infer the posterior distribution of both the number of velocity interfaces and the velocity-depth model for a strong nonlinear problem, making the inversion a complete data-driven process. The distribution of interface matches the velocity discontinuities. We find that the Moho in the study area is a transition zone of 0.7 km, or a sharp boundary with velocities from around 7 km/s in the lower crust to 8 km/s of the upper mantle; both provide nearly identical waveform match for the field data. The ambiguity comes from the resolution limit of the band-limited seismic data and limited offset range for PmP arrivals.
    Type: Article , PeerReviewed
    Format: text
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