Fluctuation characteristics of the wave field in a medium with large-scale inhomogeneities for non-gaussian phase statistics

1974 ◽  
Vol 17 (6) ◽  
pp. 669-673 ◽  
Author(s):  
L. M. Erukhimov ◽  
P. I. Shpiro
Keyword(s):  
Wave Field ◽  
Large Scale ◽  
Non Gaussian

On the irreversibility of internal-wave dynamics due to wave trapping by mean flow inhomogeneities. Part 1. Local analysis

1993 ◽  
Vol 251 ◽  
pp. 21-53 ◽  
Author(s):  
Sergei I. Badulin ◽  
Victor I. Shrira
Keyword(s):  
Shear Flow ◽  
Internal Wave ◽  
Wave Field ◽  
Large Scale ◽  
Spatial Scales ◽  
Energy Concentration ◽  
Local Analysis ◽  
Mean Flow ◽  
Wave Trapping ◽  
Wave Dynamics

The propagation of guided internal waves on non-uniform large-scale flows of arbitrary geometry is studied within the framework of linear inviscid theory in the WKB-approximation. Our study is based on a set of Hamiltonian ray equations, with the Hamiltonian being determined from the Taylor-Goldstein boundary-value problem for a stratified shear flow. Attention is focused on the fundamental fact that the generic smooth non-uniformities of the large-scale flow result in specific singularities of the Hamiltonian. Interpreting wave packets as particles with momenta equal to their wave vectors moving in a certain force field, one can consider these singularities as infinitely deep potential holes acting quite similarly to the ‘black holes’ of astrophysics. It is shown that the particles fall for infinitely long time, each into its own ‘black hole‘. In terms of a particular wave packet this falling implies infinite growth with time of the wavenumber and the amplitude, as well as wave motion focusing at a certain depth. For internal-wave-field dynamics this provides a robust mechanism of a very specific conservative and moreover Hamiltonian irreversibility.This phenomenon was previously studied for the simplest model of the flow non-uniformity, parallel shear flow (Badulin, Shrira & Tsimring 1985), where the term ‘trapping’ for it was introduced and the basic features were established. In the present paper we study the case of arbitrary flow geometry. Our main conclusion is that although the wave dynamics in the general case is incomparably more complicated, the phenomenon persists and retains its most fundamental features. Qualitatively new features appear as well, namely, the possibility of three-dimensional wave focusing and of ‘non-dispersive’ focusing. In terms of the particle analogy, the latter means that a certain group of particles fall into the same hole.These results indicate a robust tendency of the wave field towards an irreversible transformation into small spatial scales, due to the presence of large-scale flows and towards considerable wave energy concentration in narrow spatial zones.

Large‐scale three‐dimensional seismic models and their interpretive significance

Geophysics ◽  
1990 ◽  
Vol 55 (9) ◽  
pp. 1166-1182 ◽  
Author(s):  
Irshad R. Mufti
Keyword(s):  
Finite Difference ◽  
Wave Field ◽  
Large Scale ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Real Data ◽  
The Real ◽  
Need To Evaluate ◽  
Zero Offset ◽  
Seismic Models

Finite‐difference seismic models are commonly set up in 2-D space. Such models must be excited by a line source which leads to different amplitudes than those in the real data commonly generated from a point source. Moreover, there is no provision for any out‐of‐plane events. These problems can be eliminated by using 3-D finite‐difference models. The fundamental strategy in designing efficient 3-D models is to minimize computational work without sacrificing accuracy. This was accomplished by using a (4,2) differencing operator which ensures the accuracy of much larger operators but requires many fewer numerical operations as well as significantly reduced manipulation of data in the computer memory. Such a choice also simplifies the problem of evaluating the wave field near the subsurface boundaries of the model where large operators cannot be used. We also exploited the fact that, unlike the real data, the synthetic data are free from ambient noise; consequently, one can retain sufficient resolution in the results by optimizing the frequency content of the source signal. Further computational efficiency was achieved by using the concept of the exploding reflector which yields zero‐offset seismic sections without the need to evaluate the wave field for individual shot locations. These considerations opened up the possibility of carrying out a complete synthetic 3-D survey on a supercomputer to investigate the seismic response of a large‐scale structure located in Oklahoma. The analysis of results done on a geophysical workstation provides new insight regarding the role of interference and diffraction in the interpretation of seismic data.

Leveraging coherent wave field analysis and deep learning in fiber-optic seismology

2021 ◽  
Author(s):  
Benjamin Schwarz ◽  
Korbinian Sager ◽  
Philippe Jousset ◽  
Gilda Currenti ◽  
Charlotte Krawczyk ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wave Field ◽  
Real Time ◽  
Learning Strategies ◽  
Data Storage ◽  
Large Scale ◽  
Fiber Optic ◽  
Positive Impact ◽  
Field Analysis ◽  
Coherent Wave

<p><span>Fiber-optic cables form an integral part of modern telecommunications infrastructure and are ubiquitous in particular in regions where dedicated seismic instrumentation is traditionally sparse or lacking entirely. Fiber-optic seismology promises to enable affordable and time-extended observations of earth and environmental processes at an unprecedented temporal and spatial resolution. The method’s unique potential for combined large-N and large-T observations implies intriguing opportunities but also significant challenges in terms of data storage, data handling and computation.</span></p><p><span>Our goal is to enable real-time data enhancement, rapid signal detection and wave field characterization without the need for time-demanding user interaction. We therefore combine coherent wave field analysis, an optics-inspired processing framework developed in controlled-source seismology, with state-of-the-art deep convolutional neural network (CNN) architectures commonly used in visual perception. While conventional deep learning strategies have to rely on manually labeled or purely synthetic training datasets, coherent wave field analysis labels field data based on physical principles and enables large-scale and purely data-driven training of the CNN models. The shear amount of data already recorded in various settings makes artificial data generation by numerical modeling superfluous – a task that is often constrained by incomplete knowledge of the embedding medium and an insufficient description of processes at or close to the surface, which are challenging to capture in integrated simulations.</span></p><p><span>Applications to extensive field datasets acquired with dark-fiber infrastructure at a geothermal field in SW Iceland and in a town at the flank of Mt Etna, Italy, reveal that the suggested framework generalizes well across different observational scales and environments, and sheds new light on the origin of a broad range of physically distinct wave fields that can be sensed with fiber-optic technology. Owing to the real-time applicability with affordable computing infrastructure, our analysis lends itself well to rapid on-the-fly data enhancement, wave field separation and compression strategies, thereby promising to have a positive impact on the full processing chain currently in use in fiber-optic seismology.</span></p>

Consistent nonlinear deterministic and stochastic wave evolution equations from deep water to the breaking region

2019 ◽  
Vol 877 ◽  
pp. 373-404
Author(s):  
T. Vrecica ◽  
Y. Toledo
Keyword(s):  
Wave Field ◽  
Large Scale ◽  
Evolution Equations ◽  
Deterministic Model ◽  
Two Dimensional ◽  
Spectral Evolution ◽  
Wave Interactions ◽  
Nonlinear Part ◽  
Mild Slope Equation ◽  
Wave Models

Modelling the evolution of the wave field in coastal waters is a complicated task, partly due to triad nonlinear wave interactions, which are one of the dominant mechanisms in this area. Stochastic formulations already implemented into large-scale operational wave models, whilst very efficient, are one-dimensional in nature and fail to account for the majority of the physical properties of the wave field evolution. This paper presents new two-dimensional (2-D) formulations for the triad interactions source term. A quasi-two-dimensional deterministic mild slope equation is improved by including dissipation and first-order spatial derivatives in the nonlinear part of equation, significantly enhancing the accuracy in the breaking zone. The newly defined deterministic model is used to derive an updated stochastic model consistent from deep waters to the breaking region. It is localized following the approach derived in Vrecica & Toledo (J. Fluid Mech., vol. 794, 2016, pp. 310–342), to which several improvements are also presented. The model is compared to measurements of breaking and non-breaking spectral evolution, showing good agreement in both cases. Finally, the model is used to analyse several interesting 2-D properties of the shoaling wave field including the evolution of directionally spread seas.

scholarly journals Isotropic non-Gaussian gNL-like toy models that reproduce cosmic microwave background anomalies

2019 ◽  
Vol 626 ◽  
pp. A13 ◽  
Author(s):  
F. K. Hansen ◽  
T. Trombetti ◽  
N. Bartolo ◽  
U. Natale ◽  
M. Liguori ◽  
...  
Keyword(s):  
Cosmic Microwave Background ◽  
Large Scale ◽  
Statistical Significance ◽  
Common Mechanism ◽  
Small Scale ◽  
Cold Spot ◽  
Microwave Background ◽  
Significance Level ◽  
Non Gaussian ◽  
Cmb Fluctuations

Context. Based on recent observations of the cosmic microwave background (CMB), claims of statistical anomalies in the properties of the CMB fluctuations have been made. Although the statistical significance of the anomalies remains only at the ∼2−3σ significance level, the fact that there are many different anomalies, several of which support a possible deviation from statistical isotropy, has motivated a search for models that provide a common mechanism to generate them. Aims. The goal of this paper is to investigate whether these anomalies could originate from non-Gaussian cosmological models, and to determine what properties these models should have. Methods. We present a simple isotropic, non-Gaussian class of toy models that can reproduce six of the most extensively studied anomalies. We compare the presence of anomalies found in simulated maps generated from the toy models and from a standard model with Gaussian fluctuations. Results. We show that the following anomalies, as found in the Planck data, commonly occur in the toy model maps: (1) large-scale hemispherical asymmetry (large-scale dipolar modulation), (2) small-scale hemispherical asymmetry (alignment of the spatial distribution of CMB power over all scales ℓ = [2, 1500]), (3) a strongly non-Gaussian hot or cold spot, (4) a low power spectrum amplitude for ℓ <  30, including specifically (5) a low quadrupole and an unusual alignment between the quadrupole and the octopole, and (6) parity asymmetry of the lowest multipoles. We note that this class of toy model resembles models of primordial non-Gaussianity characterised by strongly scale-dependent gNL-like trispectra.

scholarly journals Fast and easy super-sample covariance of large-scale structure observables

2019 ◽  
Vol 624 ◽  
pp. A61 ◽  
Author(s):  
Fabien Lacasa ◽  
Julien Grain
Keyword(s):  
Large Scale ◽  
Signal To Noise Ratio ◽  
Cosmological Parameters ◽  
Power Spectra ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Numerical Application ◽  
Sample Covariance ◽  
Non Gaussian ◽  
Fast Prediction

We present a numerically cheap approximation to super-sample covariance (SSC) of large-scale structure cosmological probes, first in the case of angular power spectra. No new elements are needed besides those used to predict the considered probes, thus relieving analysis pipelines from having to develop a full SSC modeling, and reducing the computational load. The approximation is asymptotically exact for fine redshift bins Δz → 0. We furthermore show how it can be implemented at the level of a Gaussian likelihood or a Fisher matrix forecast as a fast correction to the Gaussian case without needing to build large covariance matrices. Numerical application to a Euclid-like survey show that, compared to a full SSC computation, the approximation nicely recovers the signal-to-noise ratio and the Fisher forecasts on cosmological parameters of the wCDM cosmological model. Moreover, it allows for a fast prediction of which parameters are going to be the most affected by SSC and at what level. In the case of photometric galaxy clustering with Euclid-like specifications, we find that σ8, ns, and the dark energy equation of state w are particularly heavily affected. We finally show how to generalize the approximation for probes other than angular spectra (correlation functions, number counts, and bispectra) and at the likelihood level, allowing for the latter to be non-Gaussian if necessary. We release publicly a Python module allowing the implementation of the SSC approximation and a notebook reproducing the plots of the article.

scholarly journals Need for Caution in Interpreting Extreme Weather Statistics

2015 ◽  
Vol 28 (23) ◽  
pp. 9166-9187 ◽  
Author(s):  
Prashant D. Sardeshmukh ◽  
Gilbert P. Compo ◽  
Cécile Penland
Keyword(s):  
Process Model ◽  
Large Scale ◽  
Probability Distributions ◽  
Extreme Weather ◽  
Recent Change ◽  
Atmospheric Variability ◽  
Tail Probabilities ◽  
The North ◽  
Special Cases ◽  
Non Gaussian

Abstract Given the reality of anthropogenic global warming, it is tempting to seek an anthropogenic component in any recent change in the statistics of extreme weather. This paper cautions that such efforts may, however, lead to wrong conclusions if the distinctively skewed and heavy-tailed aspects of the probability distributions of daily weather anomalies are ignored or misrepresented. Departures of several standard deviations from the mean, although rare, are far more common in such a distinctively non-Gaussian world than they are in a Gaussian world. This further complicates the problem of detecting changes in tail probabilities from historical records of limited length and accuracy. A possible solution is to exploit the fact that the salient non-Gaussian features of the observed distributions are captured by so-called stochastically generated skewed (SGS) distributions that include Gaussian distributions as special cases. SGS distributions are associated with damped linear Markov processes perturbed by asymmetric stochastic noise and as such represent the simplest physically based prototypes of the observed distributions. The tails of SGS distributions can also be directly linked to generalized extreme value (GEV) and generalized Pareto (GP) distributions. The Markov process model can be used to provide rigorous confidence intervals and to investigate temporal persistence statistics. The procedure is illustrated for assessing changes in the observed distributions of daily wintertime indices of large-scale atmospheric variability in the North Atlantic and North Pacific sectors over the period 1872–2011. No significant changes in these indices are found from the first to the second half of the period.

Statistical properties of the kinematics and dynamics of a random gravity-wave field

1975 ◽  
Vol 70 (2) ◽  
pp. 251-255 ◽  
Author(s):  
C. C. Tung
Keyword(s):  
Probability Density Function ◽  
Wave Field ◽  
Gravity Wave ◽  
Vertical Velocity ◽  
Dynamic Pressure ◽  
Kinematics And Dynamics ◽  
Vertical Acceleration ◽  
Vertical Velocity Component ◽  
Wave Solutions ◽  
Non Gaussian

The probability density function and the first three statistical moments of the velocity, acceleration and dynamic pressure are obtained for a Gaussian, stationary, homogeneous, random gravity-wave field in deep water, using infinitesimal wave solutions. It is shown that the velocity, acceleration and pressure are non-Gaussian. While the horizontal accelerations and vertical velocity component are of zero mean and unskewed, the dynamic pressure, vertical acceleration and horizontal velocity components are skewed and have non-zero mean.

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