The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

Peter M. Shearer - One of the best experts on this subject based on the ideXlab platform.

  • Quantifying seismic Source Parameter uncertainties
    Bulletin of the Seismological Society of America, 2011
    Co-Authors: Deborah L. Kane, Germán A. Prieto, Frank L. Vernon, Peter M. Shearer
    Abstract:

    We use data from a small aperture array in southern California to quantify variations in Source Parameter estimates at closely spaced stations (distances ranging from ∼7 to 350 m) to provide constraints on Parameter uncertainties. Many studies do not consider uncertainties in these estimates even though they can be significant and haveimportantimplicationsforstudiesofearthquakeSourcephysics.Here,weestimate seismic Source Parameters in the frequency domain using empirical Green's function (EGF) methods to remove effects of the travel paths between earthquakes and their recordingstations.Weexamineuncertainties inourestimatesbyquantifyingtheresult- ing distributions over all stations in the array. For coseismic stress drop estimates, we find that minimum uncertainties of ∼30% of the estimate can be expected. To test the robustness of our results, we explorevariations of the dataset using different groupings of stations, different Source regions, and different EGF earthquakes. Although these differences affect our absolute estimates of stress drop, they do not greatly influence thespreadinourresultingestimates.Thesesensitivitytestsshowthatstationselectionis not the primary contribution to the uncertainties in our Parameter estimates for single stations. We conclude that establishing reliable methods of estimating uncertainties in SourceParameterestimates(includingcornerfrequencies,Sourcedurations,andcoseis- mic static stress drops) is essential, particularly when the results are used in the com- parisons among different studies over a range of earthquake magnitudes and locations.

V. I. Turchin - One of the best experts on this subject based on the ideXlab platform.

  • Nonwave field processing using sensor array approach
    Signal Processing, 1995
    Co-Authors: Alex B. Gershman, V. I. Turchin
    Abstract:

    Abstract The application of sensor array processing methods for estimation and localization of wavefield Sources is well known and has been intensively studied in literature. In this paper we extend sensor array processing approach to estimating the Parameters of the fields of nonwave nature (the so-called nonwave fields). Considering the static and the diffusion field as typical examples of nonwave fields, and assuming that measurements are carried out by an antenna array, we derive the Cramer-Rao bounds of Source Parameter estimation errors. These theoretical results are completed by the experimental results of localization of the diffusion Sources in distilled water by chemical sensor array, showing high performance of sensor array processing approach to the problem considered. A modified version of the well-known CLEAN deconvolution algorithm has been used for experimental data processing.

  • ICASSP - Sensor array approach to nonwave field processing
    1995 International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: V. I. Turchin, Alex B. Gershman
    Abstract:

    The application of sensor array processing methods for estimation and localization of wavefield Sources is well-known. We extend the sensor array processing approach to estimating the Parameters of the fields of a nonwave nature (the so-called nonwave fields). Considering the static and diffusion fields as typical examples of nonwave field, we derive the Cramer-Rao bounds of Source Parameter estimation errors. These theoretical results are completed by the experimental results of localization of diffusion Sources in distilled water by a chemical sensor array, showing potentially high performance of sensor array approach. A modified version of the well-known CLEAN deconvolution algorithm has been used for experimental data processing. The nonwave field sensor array processing can find various applications such as localization of pollution Sources and another types of admixtures, detection of metallic masses and wandering currents, etc.

Alex B. Gershman - One of the best experts on this subject based on the ideXlab platform.

  • Nonwave field processing using sensor array approach
    Signal Processing, 1995
    Co-Authors: Alex B. Gershman, V. I. Turchin
    Abstract:

    Abstract The application of sensor array processing methods for estimation and localization of wavefield Sources is well known and has been intensively studied in literature. In this paper we extend sensor array processing approach to estimating the Parameters of the fields of nonwave nature (the so-called nonwave fields). Considering the static and the diffusion field as typical examples of nonwave fields, and assuming that measurements are carried out by an antenna array, we derive the Cramer-Rao bounds of Source Parameter estimation errors. These theoretical results are completed by the experimental results of localization of the diffusion Sources in distilled water by chemical sensor array, showing high performance of sensor array processing approach to the problem considered. A modified version of the well-known CLEAN deconvolution algorithm has been used for experimental data processing.

  • ICASSP - Sensor array approach to nonwave field processing
    1995 International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: V. I. Turchin, Alex B. Gershman
    Abstract:

    The application of sensor array processing methods for estimation and localization of wavefield Sources is well-known. We extend the sensor array processing approach to estimating the Parameters of the fields of a nonwave nature (the so-called nonwave fields). Considering the static and diffusion fields as typical examples of nonwave field, we derive the Cramer-Rao bounds of Source Parameter estimation errors. These theoretical results are completed by the experimental results of localization of diffusion Sources in distilled water by a chemical sensor array, showing potentially high performance of sensor array approach. A modified version of the well-known CLEAN deconvolution algorithm has been used for experimental data processing. The nonwave field sensor array processing can find various applications such as localization of pollution Sources and another types of admixtures, detection of metallic masses and wandering currents, etc.

Jeannot Trampert - One of the best experts on this subject based on the ideXlab platform.

  • robust and fast probabilistic Source Parameter estimation from near field displacement waveforms using pattern recognition
    Bulletin of the Seismological Society of America, 2015
    Co-Authors: Paul Kaufl, Andrew P Valentine, Ralph W L De Wit, Jeannot Trampert
    Abstract:

    The robust and automated determination of earthquake Source Parameters on a global and regional scale is important for many applications in seismology. We present a novel probabilistic method to invert a wide variety of (waveform) data for point‐Source Parameters in real time using pattern recognition. Inferences are made in the form of marginal probability density functions for point‐Source Parameters and incorporate realistic posterior uncertainty estimates. The neural‐network‐based method is calibrated using samples from the prior distribution, which are synthetic data vectors, and corresponding Sources located in a predefined monitoring volume. Once a set of trained neural networks is available, inversions are fast with very moderate demands on computational reSources: an inversion takes less than a second on a standard desktop computer. Uncertainties in the layered Earth model are taken into account in the Bayesian framework and increase the robustness of the results with respect to neglected 3D heterogeneities. Moreover, we find that the method is very robust with respect to perturbations such as observational noise and missing data and therefore is potentially well suited for automated and real‐time tasks, such as earthquake monitoring and early warning. We demonstrate the method by means of synthetic tests and by inverting an observed high‐rate Global Positioning System displacement dataset for the 2010 M w 7.2 El Mayor–Cucapah event. Our results are compatible with published point‐Source estimates for this event within the respective uncertainty bounds. Online Material: Additional information on the neural network methodology and implementation details, tables on neural network Parameters, crustal model and reference double‐couple solution, and figures showing prediction error, crustal models, normalized displacements, and histograms of weighted Parameters.

  • assessing the uncertainties on seismic Source Parameters towards realistic error estimates for centroid moment tensor determinations
    Physics of the Earth and Planetary Interiors, 2012
    Co-Authors: Andrew P Valentine, Jeannot Trampert
    Abstract:

    Abstract The centroid-moment-tensor (CMT) algorithm provides a straightforward, rapid method for the determination of seismic Source Parameters from waveform data. As such, it has found widespread application, and catalogues of CMT solutions – particularly the catalogue maintained by the Global CMT Project – are routinely used by geoscientists. However, there have been few attempts to quantify the uncertainties associated with any given CMT determination: whilst catalogues typically quote a ‘standard error’ for each Source Parameter, these are generally accepted to significantly underestimate the true scale of uncertainty, as all systematic effects are ignored. This prevents users of Source Parameters from properly assessing possible impacts of this uncertainty upon their own analysis. The CMT algorithm determines the best-fitting Source Parameters within a particular modelling framework, but any deficiencies in this framework may lead to systematic errors. As a result, the minimum-misfit Source may not be equivalent to the ‘true’ Source. We suggest a pragmatic solution to uncertainty assessment, based on accepting that any ‘low-misfit’ Source may be a plausible model for a given event. The definition of ‘low-misfit’ should be based upon an assessment of the scale of potential systematic effects. We set out how this can be used to estimate the range of values that each Parameter might take, by considering the curvature of the misfit function as minimised by the CMT algorithm. This approach is computationally efficient, with cost similar to that of performing an additional iteration during CMT inversion for each Source Parameter to be considered. The Source inversion process is sensitive to the various choices that must be made regarding dataset, earth model and inversion strategy, and for best results, uncertainty assessment should be performed using the same choices. Unfortunately, this information is rarely available when Sources are obtained from catalogues. As already indicated by Valentine and Woodhouse (2010) , researchers conducting comparisons between data and synthetic waveforms must ensure that their approach to forward-modelling is consistent with the Source Parameters used; in practice, this suggests that they should consider performing their own Source inversions. However, it is possible to obtain rough estimates of uncertainty using only forward-modelling.

Tarunraj Singh - One of the best experts on this subject based on the ideXlab platform.

  • computation of probabilistic hazard maps and Source Parameter estimation for volcanic ash transport and dispersion
    Journal of Computational Physics, 2014
    Co-Authors: Reza Madankan, Solene Pouget, Puneet Singla, Marcus Bursik, J Dehn, Matthew D Jones, Abani Patra, Michael J Pavolonis, E B Pitman, Tarunraj Singh
    Abstract:

    Volcanic ash advisory centers are charged with forecasting the movement of volcanic ash plumes, for aviation, health and safety preparation. Deterministic mathematical equations model the advection and dispersion of these plumes. However initial plume conditions - height, profile of particle location, volcanic vent Parameters - are known only approximately at best, and other features of the governing system such as the windfield are stochastic. These uncertainties make forecasting plume motion difficult. As a result of these uncertainties, ash advisories based on a deterministic approach tend to be conservative, and many times over/under estimate the extent of a plume. This paper presents an end-to-end framework for generating a probabilistic approach to ash plume forecasting. This framework uses an ensemble of solutions, guided by Conjugate Unscented Transform (CUT) method for evaluating expectation integrals. This ensemble is used to construct a polynomial chaos expansion that can be sampled cheaply, to provide a probabilistic model forecast. The CUT method is then combined with a minimum variance condition, to provide a full posterior pdf of the uncertain Source Parameters, based on observed satellite imagery.The April 2010 eruption of the Eyjafjallajokull volcano in Iceland is employed as a test example. The puff advection/dispersion model is used to hindcast the motion of the ash plume through time, concentrating on the period 14-16 April 2010. Variability in the height and particle loading of that eruption is introduced through a volcano column model called bent. Output uncertainty due to the assumed uncertain input Parameter probability distributions, and a probabilistic spatial-temporal estimate of ash presence are computed.

  • ACC - Optimal information collection for Source Parameter estimation of atmospheric release phenomenon
    2014 American Control Conference, 2014
    Co-Authors: Reza Madankan, Puneet Singla, Tarunraj Singh
    Abstract:

    In this research, the effect of dynamic data measurement on Source Parameters estimation is studied. The concept of mutual information is exploited to identify the optimal location for each sensor, while performing the dynamic data measurement to improve accuracy of estimation. For validation purposes, an advection - diffusion simulation code, SCIPUFF (Second-order Closure Integrated PUFF) is being used as a modeling testbed to study the effect of using dynamic data measurement. A Bayesian estimation framework is being used to characterize the Source Parameters, while data measurement is performed by mobile sensors, which are located based on the concept of maximizing the information content. As our numerical simulations show, using dynamic data measurement, based on maximum information collection, leads to considerably better estimates of Source Parameters.

  • estimation and propagation of volcanic Source Parameter uncertainty in an ash transport and dispersal model application to the eyjafjallajokull plume of 14 16 april 2010
    Bulletin of Volcanology, 2012
    Co-Authors: Marcus Bursik, Puneet Singla, Matthew D Jones, Michael J Pavolonis, Tarunraj Singh, S A Carn, Ken Dean, Abani K Patra, Bruce E Pitman, Peter Webley
    Abstract:

    Data on Source conditions for the 14 April 2010 paroxysmal phase of the Eyjafjallajokull eruption, Iceland, have been used as inputs to a trajectory-based eruption column model, bent. This model has in turn been adapted to generate output suitable as input to the volcanic ash transport and dispersal model, puff, which was used to propagate the paroxysmal ash cloud toward and over Europe over the following days. Some of the Source param- eters, specifically vent radius, vent Source velocity, mean grain size of ejecta, and standard deviation of ejecta grain size have been assigned probability distributions based on our lack of knowledge of exact conditions at the Source. These probability distributions for the input variables have been sampled in a Monte Carlo fashion using a technique that yields what we herein call the polynomial chaos quad- rature weighted estimate (PCQWE) of output Parameters from the ash transport and dispersal model. The advantage of PCQWE over Monte Carlo is that since it intelligently samples the input Parameter space, fewer model runs are needed to yield estimates of moments and probabilities for the output variables. At each of these sample points for the input variables, a model run is performed. Output moments and probabilities are then computed by properly summing the weighted values of the output Parameters of interest. Use