Wavefield Reconstruction and Wave Equation Inversion for Seismic Surface Waves

Surface waves are a particular type of seismic wave that propagate around the surface of the Earth, but which oscillate over depth ranges beneath the surface that depend on their frequency of oscillation. This causes them to travel with a speed that depends on their frequency, a property called dispersion. Estimating surface wave dispersion is of interest for many geophysical applications using both active and passive seismic sources, not least because the speed-frequency relationship can be used to infer the subsurface velocity structure at depth beneath the surface. We present an inversion scheme that exploits spatial and temporal relationships in the scalar Helmholtz (wave) equation to estimate dispersion relations of the elastic surface wave data in both active and passive surveys, while also reconstructing the wavefield continuously in space (i.e. between the receivers at which the wavefield was recorded). We verify the retrieved dispersive phase velocity by comparing the results to dispersion analysis in the frequency-slowness domain, and to the local calculation of dispersion using modal analysis. Synthetic elastic examples demonstrate the method under a variety of recording scenarios. The results show that despite the scalar approximation made to represent these intrinsically elastic waves, the proposed method reconstructs both the wavefield and the phase dispersion structure even in the case of strong aliasing and irregular sampling

Classification of Stellar Spectra with LLE

We investigate the use of dimensionality reduction techniques for the classification of stellar spectra selected from the SDSS. Using local linear embedding (LLE), a technique that preserves the local (and possibly non-linear) structure within high dimensional data sets, we show that the majority of stellar spectra can be represented as a one dimensional sequence within a three dimensional space. The position along this sequence is highly correlated with spectral temperature. Deviations from this "stellar locus" are indicative of spectra with strong emission lines (including misclassified galaxies) or broad absorption lines (e.g. Carbon stars). Based on this analysis, we propose a hierarchical classification scheme using LLE that progressively identifies and classifies stellar spectra in a manner that requires no feature extraction and that can reproduce the classic MK classifications to an accuracy of one type.Comment: 15 pages, 13 figures; accepted for publication in The Astronomical Journa

An Algorithm for Finding Candidate Synaptic Sites in Computer Generated Networks of Neurons with Realistic Morphologies

Neurons make synaptic connections at locations where axons and dendrites are sufficiently close in space. Typically the required proximity is based on the dimensions of dendritic spines and axonal boutons. Based on this principle one can search those locations in networks formed by reconstructed neurons or computer generated neurons. Candidate synapses are then located where axons and dendrites are within a given criterion distance from each other. Both experimentally reconstructed and model generated neurons are usually represented morphologically by piecewise-linear structures (line pieces or cylinders). Proximity tests are then performed on all pairs of line pieces from both axonal and dendritic branches. Applying just a test on the distance between line pieces may result in local clusters of synaptic sites when more than one pair of nearby line pieces from axonal and dendritic branches is sufficient close, and may introduce a dependency on the length scale of the individual line pieces. The present paper describes a new algorithm for defining locations of candidate synapses which is based on the crossing requirement of a line piece pair, while the length of the orthogonal distance between the line pieces is subjected to the distance criterion for testing 3D proximity

Do CMIP6 Climate Models Simulate Global or Regional Compound Events Skillfully?

Compound events have the potential to cause high socioeconomic and environmental losses. We examine the ability of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models to capture two bivariate compound events: the co-occurrence of heavy rain and strong wind, and heat waves and meteorological drought. We evaluate the models over North America, Europe, Eurasia, and Australia using observations and reanalysis data set spanning 1980-2014. Some of the CMIP6 models capture the return periods of both bivariate compound events over North America, Europe, and Eurasia surprisingly well but perform less well over Australia. For heavy rain and strong wind, this poor performance was particularly clear in northern Australia which suggests limits in simulating tropical and extratropical cyclones, local convection, and mesoscale convective systems. We did not find higher model resolution improved performance in any region. Overall, our results show some CMIP6 models can be used to examine compound events, particularly over North America, Europe, and Eurasia. Plain Language Summary Compound events, such as the co-occurrence of heavy rain and strong wind or heat waves and drought, can have major economic, social, and environmental consequences. We therefore ask the question whether the new generation of climate models represented by the sixth phase of the Coupled Model Intercomparison Project (CMIP6) can simulate the occurrence of these important events. We found that some of the CMIP6 models do simulate these compound events surprisingly well over North America, Europe, and Eurasia. Unfortunately, they perform less well over Australia which is likely associated with the problem of simulating extratropical cyclones, local convection, and mesoscale convective systems. Our results suggest that some CMIP6 models can be used to examine these two compound events particularly over North America, Europe, and Eurasia. Key Points . Some CMIP6 models reproduce observed return periods of co-occurring rain and wind extremes and co-occurring heat waves and droughts well CMIP6 models simulate these compound events over North America, Europe, or Eurasia with similar levels of skill CMIP6 models simulate these compound events over Australia with lower skill than the other regions analyzedThe research was funded by the Australian Research Council Center of Excellence for Climate Extremes (CE170100023) and was support-ed in part by the New South Wales Department of Planning, Industry and Environment

Ensemble Asteroseismology of the Young Open Cluster NGC 2244

Our goal is to perform in-depth ensemble asteroseismology of the young open cluster NGC2244 with the 2-wheel Kepler mission. While the nominal Kepler mission already implied a revolution in stellar physics for solar-type stars and red giants, it was not possible to perform asteroseismic studies of massive OB stars because such targets were carefully avoided in the FoV in order not to disturb the exoplanet hunting. Now is an excellent time to fill this hole in mission capacity and to focus on the metal factories of the Universe, for which stellar evolution theory is least adequate. Our white paper aims to remedy major shortcomings in the theory of stellar structure and evolution of the most massive stars by focusing on a large ensemble of stars in a carefully selected young open cluster. Cluster asteroseismology of very young stars such as those of NGC2244 has the major advantage that all cluster stars have similar age, distance and initial chemical composition, implying drastic restrictions for the stellar modeling compared to asteroseismology of single isolated stars with very different ages and metallicities. Our study requires long-term photometric measurements of stars with visual magnitude ranging from 6.5 to 15 in a large FoV with a precision better than 30 ppm for the brightest cluster members (magnitude below 9) up to 500 ppm for the fainter ones, which is well achievable with 2-Wheel Kepler, in combination with high-precision high-resolution spectroscopy and spectro-polarimetry of the brightest pulsating cluster members. These ground-based spectroscopic data will be assembled with the HERMES and CORALIE spectrographs (twin 1.2m Mercator and Euler telescopes, La Palma, Canary Islands and La Silla, Chile), as well as with the spectro-polarimetric NARVAL instrument (2m BLT at the Pic du Midi, French Pyrenees), to which we have guaranteed access.Comment: 10 pages, 3 figures, white paper submitted in response to the NASA call for community input for science investigations the Kepler 2-Wheel spacecraf

Color discrimination errors associate with axial motor impairments in Parkinson’s Disease

BackgroundVisual function deficits are more common in imbalance‐predominant compared to tremor‐predominant PD, suggesting a pathophysiological role of impaired visual functions in axial motor impairments.ObjectiveTo investigate the relationship between changes in color discrimination and motor impairments in PD while accounting for cognitive or other confounder factors.MethodsPD subjects (n = 49, age 66.7 ± 8.3 years; Hoehn & Yahr stage 2.6 ± 0.6) completed color discrimination assessment using the Farnsworth‐Munsell 100 Hue Color Vision Test, neuropsychological, motor assessments, and [11C]dihydrotetrabenazine vesicular monoamine transporter type 2 PET imaging. MDS‐UPDRS sub‐scores for cardinal motor features were computed. Timed Up & Go mobility and walking tests were assessed in 48 subjects.ResultsBivariate correlation coefficients between color discrimination and motor variables were significant only for the Timed Up & Go test (RS = 0.44, P = 0.0018) and the MDS‐UPDRS axial motor scores (RS = 0.38, P = 0.0068). Multiple regression confounder analysis using the Timed Up & Go as outcome parameter showed a significant total model (F(5,43) = 7.3, P < 0.0001) with significant regressor effects for color discrimination (standardized β = 0.32, t = 2.6, P = 0.012), global cognitive Z‐score (β = −0.33, t = −2.5, P = 0.018), duration of disease (β = 0.26, t = 1.8, P = 0.038), but not for age or striatal dopaminergic binding. The color discrimination test was also a significant independent regressor in the MDS‐UPDRS axial motor model (standardized β = 0.29, t = 2.4, P = 0.022; total model t(5,43) = 6.4, P = 0.0002).ConclusionsColor discrimination errors associate with axial motor features in PD independent of cognitive deficits, nigrostriatal dopaminergic denervation, and other confounder variables. These findings may reflect shared pathophysiology between color discrimination visual impairments and axial motor burden in PD.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141397/1/mdc312527.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141397/2/mdc312527_am.pd

3D Monte Carlo Surface Wave Tomography

Seismic surface wave tomography is a tried and tested method to reveal the subsurface structure of the Earth. However, the conventional 2-step scheme of inverting first for 2-D maps of surface wave phase or group velocity and then inverting for the 3-D spatial velocity structure preserves little information about lateral spatial correlations, and introduces additional uncertainties and errors into the 3-D result. We introduce a 1-step 3-D non-linear surface wave tomography method that removes these effects by inverting for 3-D spatial structure directly from frequency-dependent traveltime measurements. We achieve this using the reversible jump Markov chain Monte Carlo (McMC) algorithm with a fully 3-D model parametrization. Synthetic tests show that the method estimates the velocity model and associated uncertainties significantly better than the conventional 2-step McMC method, and that the computational cost seems to be comparable with 2-step McMC methods. The resulting uncertainties are more intuitively reasonable than those from the 2-step method, and provide directly interpretable uncertainty on volumetrics of structures of interest.ISSN:0956-540XISSN:1365-246