Effective flow properties of heterolithic, cross-bedded tidal sandstones: Part 1. Surface-based modeling

Tidal heterolithic sandstones are commonly characterized by millimeter- to centimeter-scale intercalations of mudstone and sandstone. Consequently, their effective flow properties are poorly predicted by (1) data that do not sample a representative volume or (2) models that fail to capture the complex three-dimensional architecture of sandstone and mudstone layers. We present a modeling approach in which surfaces are used to represent all geologic heterogeneities that control the spatial distribution of reservoir rock properties (surface-based modeling). The workflow uses template surfaces to represent heterogeneities classified by geometry instead of length scale. The topology of the template surfaces is described mathematically by a small number of geometric input parameters, and models are constructed stochastically. The methodology has been applied to generate generic, three-dimensional minimodels (9 m3 volume) of cross-bedded heterolithic sandstones representing trough and tabular cross-bedding with differing proportions of sandstone and mudstone, using conditioning data from two outcrop analogs from a tide-dominated deltaic deposit. The minimodels capture the cross-stratified architectures observed in outcrop and are suitable for flow simulation, allowing computation of effective permeability values for use in larger-scale models. We show that mudstone drapes in cross-bedded heterolithic sandstones significantly reduce effective permeability and also impart permeability anisotropy in the horizontal as well as vertical flow directions. The workflow can be used with subsurface data, supplemented by outcrop analog observations, to generate effective permeability values to be derived for use in larger-scale reservoir models. The methodology could be applied to the characterization and modeling of heterogeneities in other types of sandstone reservoirs

A linear CO chemistry parameterization in a chemistry-transport model: evaluation and application to data assimilation

This paper presents an evaluation of a new linear parameterization valid for the troposphere and the stratosphere, based on a first order approximation of the carbon monoxide (CO) continuity equation. This linear scheme (hereinafter noted LINCO) has been implemented in the 3-D Chemical Transport Model (CTM) MOCAGE (MOdèle de Chimie Atmospherique Grande Echelle). First, a one and a half years of LINCO simulation has been compared to output obtained from a detailed chemical scheme output. The mean differences between both schemes are about ±25 ppbv (part per billion by volume) or 15% in the troposphere and ±10 ppbv or 100% in the stratosphere. Second, LINCO has been compared to diverse observations from satellite instruments covering the troposphere (Measurements Of Pollution In The Troposphere: MOPITT) and the stratosphere (Microwave Limb Sounder: MLS) and also from aircraft (Measurements of ozone and water vapour by Airbus in-service aircraft: MOZAIC programme) mostly flying in the upper troposphere and lower stratosphere (UTLS). In the troposphere, the LINCO seasonal variations as well as the vertical and horizontal distributions are quite close to MOPITT CO observations. However, a bias of ~−40 ppbv is observed at 700 Pa between LINCO and MOPITT. In the stratosphere, MLS and LINCO present similar large-scale patterns, except over the poles where the CO concentration is underestimated by the model. In the UTLS, LINCO presents small biases less than 2% compared to independent MOZAIC profiles. Third, we assimilated MOPITT CO using a variational 3D-FGAT (First Guess at Appropriate Time) method in conjunction with MOCAGE for a long run of one and a half years. The data assimilation greatly improves the vertical CO distribution in the troposphere from 700 to 350 hPa compared to independent MOZAIC profiles. At 146 hPa, the assimilated CO distribution is also improved compared to MLS observations by reducing the bias up to a factor of 2 in the tropics. This study confirms that the linear scheme is able to simulate reasonably well the CO distribution in the troposphere and in the lower stratosphere. Therefore, the low computing cost of the linear scheme opens new perspectives to make free runs and CO data assimilation runs at high resolution and over periods of several years

Combined assimilation of IASI and MLS observations to constrain tropospheric and stratospheric ozone in a global chemical transport model

Accurate and temporally resolved fields of free-troposphere ozone are of major importance to quantify the intercontinental transport of pollution and the ozone radiative forcing. We consider a global chemical transport model (MOdèle de Chimie Atmosphérique à Grande Échelle, MOCAGE) in combination with a linear ozone chemistry scheme to examine the impact of assimilating observations from the Microwave Limb Sounder (MLS) and the Infrared Atmospheric Sounding Interferometer (IASI). The assimilation of the two instruments is performed by means of a variational algorithm (4D-VAR) and allows to constrain stratospheric and tropospheric ozone simultaneously. The analysis is first computed for the months of August and November 2008 and validated against ozonesonde measurements to verify the presence of observations and model biases. Furthermore, a longer analysis of 6 months (July–December 2008) showed that the combined assimilation of MLS and IASI is able to globally reduce the uncertainty (root mean square error, RMSE) of the modeled ozone columns from 30 to 15% in the upper troposphere/lower stratosphere (UTLS, 70–225 hPa). The assimilation of IASI tropospheric ozone observations (1000–225 hPa columns, TOC – tropospheric O₃ column) decreases the RMSE of the model from 40 to 20% in the tropics (30° S–30° N), whereas it is not effective at higher latitudes. Results are confirmed by a comparison with additional ozone data sets like the Measurements of OZone and wAter vapour by aIrbus in-service airCraft (MOZAIC) data, the Ozone Monitoring Instrument (OMI) total ozone columns and several high-altitude surface measurements. Finally, the analysis is found to be insensitive to the assimilation parameters. We conclude that the combination of a simplified ozone chemistry scheme with frequent satellite observations is a valuable tool for the long-term analysis of stratospheric and free-tropospheric ozone

Iteratively regularized Newton-type methods for general data misfit functionals and applications to Poisson data

We study Newton type methods for inverse problems described by nonlinear operator equations $F(u)=g$ in Banach spaces where the Newton equations $F'(u_n;u_{n+1}-u_n) = g-F(u_n)$ are regularized variationally using a general data misfit functional and a convex regularization term. This generalizes the well-known iteratively regularized Gauss-Newton method (IRGNM). We prove convergence and convergence rates as the noise level tends to 0 both for an a priori stopping rule and for a Lepski{\u\i}-type a posteriori stopping rule. Our analysis includes previous order optimal convergence rate results for the IRGNM as special cases. The main focus of this paper is on inverse problems with Poisson data where the natural data misfit functional is given by the Kullback-Leibler divergence. Two examples of such problems are discussed in detail: an inverse obstacle scattering problem with amplitude data of the far-field pattern and a phase retrieval problem. The performence of the proposed method for these problems is illustrated in numerical examples

Adaptive estimation in circular functional linear models

We consider the problem of estimating the slope parameter in circular functional linear regression, where scalar responses Y1,...,Yn are modeled in dependence of 1-periodic, second order stationary random functions X1,...,Xn. We consider an orthogonal series estimator of the slope function, by replacing the first m theoretical coefficients of its development in the trigonometric basis by adequate estimators. Wepropose a model selection procedure for m in a set of admissible values, by defining a contrast function minimized by our estimator and a theoretical penalty function; this first step assumes the degree of ill posedness to be known. Then we generalize the procedure to a random set of admissible m's and a random penalty function. The resulting estimator is completely data driven and reaches automatically what is known to be the optimal minimax rate of convergence, in term of a general weighted L2-risk. This means that we provide adaptive estimators of both the slope function and its derivatives

Chronological age, somatic maturation and anthropometric measures: Association with physical performance of young male judo athletes

© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Sport for children and adolescents must consider growth and maturation to ensure suitable training and competition, and anthropometric variables could be used as bio-banding strategies in youth sport. This investigation aimed to analyze the association between chronological age, biologic maturation, and anthropometric characteristics to explain physical performance of young judo athletes. Sixty-seven judokas (11.0–14.7 years) were assessed for anthropometric and physical performance. Predicted adult stature was used as a somatic maturation indicator. A Pearson’s bivariate correlation was performed to define which anthropometric variables were associated with each physical test. A multiple linear hierarchical regression was conducted to verify the effects of age, maturity, and anthropometry on physical performance. The regression models were built with age, predicted adult stature, and the three most significantly correlated anthropometric variables for each physical test. Older judokas performed better in most of the physical tests. However, maturation attenuated the age effect in most variables and significantly affected upper body and handgrip strength. Anthropometric variables attenuated age and maturity and those associated with body composition significantly affected the performance in most tests, suggesting a potential as bio-banding strategies. Future studies should investigate the role of anthropometric variables on the maturity effect in young judokas

Sparsity and Incoherence in Compressive Sampling

We consider the problem of reconstructing a sparse signal $x^0\in\R^n$ from a limited number of linear measurements. Given $m$ randomly selected samples of $U x^0$, where $U$ is an orthonormal matrix, we show that $\ell_1$ minimization recovers $x^0$ exactly when the number of measurements exceeds $$m\geq \mathrm{Const}\cdot\mu^2(U)\cdot S\cdot\log n,$$ where $S$ is the number of nonzero components in $x^0$, and $\mu$ is the largest entry in $U$ properly normalized: $\mu(U) = \sqrt{n} \cdot \max_{k,j} |U_{k,j}|$. The smaller $\mu$, the fewer samples needed. The result holds for ``most'' sparse signals $x^0$ supported on a fixed (but arbitrary) set $T$. Given $T$, if the sign of $x^0$ for each nonzero entry on $T$ and the observed values of $Ux^0$ are drawn at random, the signal is recovered with overwhelming probability. Moreover, there is a sense in which this is nearly optimal since any method succeeding with the same probability would require just about this many samples