Manganiferous smectite from tirebolu (NE-Turkey)

A pink-colored (HUE 5R-7/4), predominantly calcium saturated, clay mineral from Tirebolu, NE-Turkey, is shown to be a dioctahedral, high-charge manganiferous smectite. It is probably an alteration product of volcanic tuffs of rhyodactitic composition. Ionic formula: (Si_7.71Al_0.29)^IV(Al_3.04Fe _0.12 ³⁺ Fe _0.02 ²⁺ Mg_0.92Mn _0.07 ²⁺ )^VI O₂₀ (OH)₄ 0.79 M⁺ Cation exchange capacity: 92 me/100 g (air dry sample). Unit cell parameters (ca. 20 ° C; ca. 40% RH): a₀9.02 Å b₀5.21 Å Mg⁺⁺... Mn-smectite: c₀14.65 Å Ca⁺⁺... Mn-smectite: c₀14.83 Å K⁺... Mn-smectite: c₀12.10 Å

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Zusammenfassung Ein rosa (HUE 5R-7/4), weitgehend Calcium-gesättigtes Tonmineral von Tirebolu (NO-Türkei) wurde als ein dioktaedrischer, hochgeladener manganhaltiger Smektit identifiziert. Er ist wahrscheinlich ein Umwandlungsprodukt eines rhyodazitischen Tuffs. Strukturformel: (Si_7.71Al_0.29)^IV(Al_3.04Fe _0.12 ³⁺ Fe _0.02 ²⁺ Mg_0.92Mn _0.07 ²⁺ )^VI O₂₀ (OH)₄ 0.79 M⁺ Kationaustauschkapazität: 92 me/100 g (luftgetrocknet). Gitterkonstanten (ca. 20° C; ca. 40% RF): a₀9.02 Å b₀5.21 Å Mg⁺⁺... Mn-Smektit: c₀14.65 Å Ca⁺⁺... Mn-Smektit: c₀14.83 Å K⁺... Mn-Smektit: c₀12.10 Å

     

Sampling strategies and square root analysis schemes for the EnKF

The purpose of this paper is to examine how different sampling strategies and implementations of the analysis scheme influence the quality of the results in the EnKF. It is shown that by selecting the initial ensemble, the model noise and the measurement perturbations wisely, it is possible to achieve a significant improvement in the EnKF results, using the same number of members in the ensemble. The results are also compared with a square root implementation of the EnKF analysis scheme where the analyzed ensemble is computed without the perturbation of measurements. It is shown that the measurement perturbations introduce sampling errors which can be reduced using improved sampling schemes in the standard EnKF or fully eliminated when the square root analysis algorithm is used. Further, a new computationally efficient square root algorithm is proposed which allows for the use of a low-rank representation of the measurement error covariance matrix. It is shown that this algorithm in fact solves the full problem at a low cost without introducing any new approximations.     

Sediment pollution in the Vågen,Bergen harbour,Norway

Recent sediments in the Vågen, Bergen harbour, are subjected to injection of untreated sewage from around 15,000 person equivalent. Here the distribution of trace elements is mapped in surface and subsurface sediments. Selected trace-element concentrations decrease from the inner towards the outer Vågen. These concentration gradients do not correlate with minerogenic grain size. Linear correlation, combined with enriched sediment trace elements, are used to infer sources for these elements and possible reasons for their enrichment. (1) Frequent use of dental amalgam might be related to the enrichments of and correlation between Ag, Cu, Hg and Sn in surficial sediments pre-1994. (2) Enrichment and correlation of Cu/Zn may be related to the commercial and private use of anti-fouling boat-paint. Further, tributyltin is suggested as a source of Sn enrichment. (3) Increased traffic in the city of Bergen with gasoline combustion and car-wheel wear-off release Pb and Zn. (4) Hg and Pb might have been atmospherically supplied due to the combustion of coal in a nearby gas plant pre-1985. (5) No correlation is observed between enriched Vågen trace metals and bedrock element composition of the surrounding mountains, indicating a minor supply from erosion.     

Large-offset approximation to seismic reflection traveltimes

Conventional approximations of reflection traveltimes assume a small offset-to-depth ratio, and their accuracy decreases with increasing offset-to-depth ratio. Hence, they are not suitable for velocity analysis and stacking of long-offset reflection seismic data. Assuming that the offset is large, rather than small, we present a new traveltime approximation which is exact at infinite offset and has a decreasing accuracy with decreasing offset-to-depth ratio. This approximation has the form of a series containing powers of the offset from 1 to −∞. It is particularly accurate in the presence of a thin high-velocity layer above the reflector, i.e. in a situation where the accuracy of the Taner and Koehler series is poor. This new series can be used to gain insight into the velocity information contained in reflection traveltimes at large offsets, and possibly to improve velocity analysis and stacking of long-offset reflection seismic data.     

Environmental toxicology: population modeling of cod larvae shows high sensitivity to loss of zooplankton prey

Two factors determine whether pollution is likely to affect a population indirectly through loss of prey: firstly, the sensitivity of the prey to the pollutants, and secondly, the sensitivity of the predator population to loss of prey at the given life stage. We here apply a statistical recruitment model for Northeast Arctic cod to evaluate the sensitivity of cod cohorts to loss of zooplankton prey, for example following an oil spill. The calculations show that cod cohorts are highly sensitive to possible zooplankton biomass reductions in the distribution area of the cod larvae, and point to a need for more knowledge about oil-effects on zooplankton. Our study illustrates how knowledge about population dynamics may guide which indirect effects to consider in environmental impact studies.     

Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter

The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic behavior such as particle filters exist, but they are computationally too expensive when working with high-dimensional systems. The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the samples are not guaranteed to come from the true posterior distribution. By approximating the model error with a Gaussian distribution, one may represent the posterior distribution as a sum of Gaussian kernels. The resulting Gaussian mixture filter has the advantage of both a local Kalman type correction and the weighting/resampling step of a particle filter. The Gaussian mixture approximation relies on a bandwidth parameter which often has to be kept quite large in order to avoid a weight collapse in high dimensions. As a result, the Kalman correction is too large to capture highly non-Gaussian posterior distributions. In this paper, we have extended the Gaussian mixture filter (Hoteit et al., Mon Weather Rev 136:317–334, 2008) and also made the connection to particle filters more transparent. In particular, we introduce a tuning parameter for the importance weights. In the last part of the paper, we have performed a simulation experiment with the Lorenz40 model where our method has been compared to the EnKF and a full implementation of a particle filter. The results clearly indicate that the new method has advantages compared to the standard EnKF.     

A modified randomized maximum likelihood for improved Bayesian history matching

Randomized maximum likelihood is known in the petroleum reservoir community as a Bayesian history matching technique by means of minimizing a stochastic quadratic objective function. The algorithm is well established and has shown promising results in several applications. For linear models with linear observation operator, the algorithm samples the posterior density accurately. To improve the sampling for nonlinear models, we introduce a generalized version in its simplest form by re-weighting the prior. The weight term is motivated by a sufficiency condition on the expected gradient of the objective function. Recently, an ensemble version of the algorithm was proposed which can be implemented with any simulator. Unfortunately, the method has some practical implementation issues due to computation of low rank pseudo inverse matrices and in practice only the data mismatch part of the objective function is maintained. Here, we take advantage of the fact that the measurement space is often much smaller than the parameter space and project the prior uncertainty from the parameter space to the measurement space to avoid over fitting of data. The proposed algorithms show good performance on synthetic test cases including a 2D reservoir model.     

Using Multiple Grids in Markov Mesh Facies Modeling

A multigrid Markov mesh model for geological facies is formulated by defining a hierarchy of nested grids and defining a Markov mesh model for each of these grids. The facies probabilities in the Markov mesh models are formulated as generalized linear models that combine functions of the grid values in a sequential neighborhood. The parameters in the generalized linear model for each grid are estimated from the training image. During simulation, the coarse patterns are first laid out, and by simulating increasingly finer grids we are able to recreate patterns at different scales. The method is applied to several tests cases and results are compared to the training image and the results of a commercially available snesim algorithm. In each test case, simulation results are compared qualitatively by visual inspection, and quantitatively by using volume fractions, and an upscaled permeability tensor. When compared to the training image, the method produces results that only have a few percent deviation from the values of the training image. When compared with the snesim algorithm the results in general have the same quality. The largest computational cost in the multigrid Markov mesh is the estimation of model parameters from the training image. This is of comparable CPU time to that of creating one snesim realization. The simulation of one realization is typically ten times faster than the estimation.     

Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior

We invert prestack seismic amplitude data to find rock properties of a vertical profile of the earth. In particular we focus on lithology, porosity and fluid. Our model includes vertical dependencies of the rock properties. This allows us to compute quantities valid for the full profile such as the probability that the vertical profile contains hydrocarbons and volume distributions of hydrocarbons. In a standard point wise approach, these quantities can not be assessed. We formulate the problem in a Bayesian framework, and model the vertical dependency using spatial statistics. The relation between rock properties and elastic parameters is established through a stochastic rock model, and a convolutional model links the reflectivity to the seismic. A Markov chain Monte Carlo (MCMC) algorithm is used to generate multiple realizations that honours both the seismic data and the prior beliefs and respects the additional constraints imposed by the vertical dependencies. Convergence plots are used to provide quality check of the algorithm and to compare it with a similar method. The implementation has been tested on three different data sets offshore Norway, among these one profile has well control. For all test cases the MCMC algorithm provides reliable estimates with uncertainty quantification within three hours. The inversion result is consistent with the observed well data. In the case example we show that the seismic amplitudes make a significant impact on the inversion result even if the data have a moderate well tie, and that this is due to the vertical dependency imposed on the lithology fluid classes in our model. The vertical correlation in elastic parameters mainly influences the upside potential of the volume distribution. The approach is best suited to evaluate a few selected vertical profiles since the MCMC algorithm is computer demanding.