Corrected kriging update formulae for batch-sequential data assimilation

Recently, a lot of effort has been spent in the efficient computation of kriging predictors when observations are assimilated sequentially. In particular, kriging update formulae enabling significant computational savings were derived. Taking advantage of the previous kriging mean and variance computations helps avoiding a costly matrix inversion when adding one observation to the TeX already available ones. In addition to traditional update formulae taking into account a single new observation, Emery (2009) proposed formulae for the batch-sequential case, i.e. when TeX new observations are simultaneously assimilated. However, the kriging variance and covariance formulae given in Emery (2009) for the batch-sequential case are not correct. In this paper, we fix this issue and establish correct expressions for updated kriging variances and covariances when assimilating observations in parallel. An application in sequential conditional simulation finally shows that coupling update and residual substitution approaches may enable significant speed-ups.

Distance-based kriging relying on proxy simulations for inverse conditioning

Let us consider a large set of candidate parameter fields, such as hydraulic conductivity maps, on which we can run an accurate forward flow and transport simulation. We address the issue of rapidly identifying a subset of candidates whose response best match a reference response curve. In order to keep the number of calls to the accurate flow simulator computationally tractable, a recent distance-based approach relying on fast proxy simulations is revisited, and turned into a non-stationary kriging method where the covariance kernel is obtained by combining a classical kernel with the proxy. Once the accurate simulator has been run for an initial subset of parameter fields and a kriging metamodel has been inferred, the predictive distributions of misfits for the remaining parameter fields can be used as a guide to select candidate parameter fields in a sequential way. The proposed algorithm, Proxy-based Kriging for Sequential Inversion (ProKSI), relies on a variant of the Expected Improvement, a popular criterion for kriging-based global optimization. A statistical benchmark of ProKSI’s performances illustrates the efficiency and the robustness of the approach when using different kinds of proxies.

A benchmark of kriging-based infill criteria for noisy optimization

Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided. In summary, ten methods for choosing the sequential samples are described using a unified formalism. They are compared on analytical benchmark problems, whereby the usual assumption of homoscedastic Gaussian noise made in the underlying models is meet. Different problem configurations (noise level, maximum number of observations, initial number of observations) and setups (covariance functions, budget, initial sample size) are considered. It is found that the choices of the initial sample size and the covariance function are not critical. The choice of the method, however, can result in significant differences in the performance. In particular, the three most intuitive criteria are found as poor alternatives. Although no criterion is found consistently more efficient than the others, two specialized methods appear more robust on average.

KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging

Several strategies relying on kriging have recently been proposed for adaptively estimating contour lines and excursion sets of functions under severely limited evaluation budget. The recently released R package KrigInv 3 is presented and offers a sound implementation of various sampling criteria for those kinds of inverse problems. KrigInv is based on the DiceKriging package, and thus benefits from a number of options concerning the underlying kriging models. Six implemented sampling criteria are detailed in a tutorial and illustrated with graphical examples. Different functionalities of KrigInv are gradually explained. Additionally, two recently proposed criteria for batch-sequential inversion are presented, enabling advanced users to distribute function evaluations in parallel on clusters or clouds of machines. Finally, auxiliary problems are discussed. These include the fine tuning of numerical integration and optimization procedures used within the computation and the optimization of the considered criteria.

Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package

Kriging-based optimization relying on noisy evaluations of complex systems has recently motivated contributions from various research communities. Five strategies have been implemented in the DiceOptim package. The corresponding functions constitute a user-friendly tool for solving expensive noisy optimization problems in a sequential framework, while offering some flexibility for advanced users. Besides, the implementation is done in a unified environment, making this package a useful device for studying the relative performances of existing approaches depending on the experimental setup. An overview of the package structure and interface is provided, as well as a description of the strategies and some insight about the implementation challenges and the proposed solutions. The strategies are compared to some existing optimization packages on analytical test functions and show promising performances.

“Sweating meteorites”-Water-soluble salts and temperature variation in ordinary chondrites and soil from the hot desert of Oman

The common appearance of hygroscopic brine (“sweating”) on ordinary chondrites (OCs) from Oman during storage under room conditions initiated a study on the role of water-soluble salts on the weathering of OCs. Analyses of leachates from OCs and soils, combined with petrography of alteration features and a 11-month record of in situ meteorite and soil temperatures, are used to evaluate the role of salts in OC weathering. Main soluble ions in soils are Ca2+, SO42−, HCO3−, Na+, and Cl−, while OC leachates are dominated by Mg2+ (from meteoritic olivine), Ca2+ (from soil), Cl− (from soil), SO42− (from meteoritic troilite and soil), and iron (meteoritic). “Sweating meteorites” mainly contain Mg2+ and Cl−. The median Na/Cl mass ratio of leachates changes from 0.65 in soils to 0.07 in meteorites, indicating the precipitation of a Na-rich phase or loss of an efflorescent Na-salt. The total concentrations of water-soluble ions in bulk OCs ranges from 600 to 9000 μg g−1 (median 2500 μg g−1) as compared to 187–14140 μg g−1 in soils (median 1148 μg g−1). Soil salts dissolved by rain water are soaked up by meteorites by capillary forces. Daily heating (up to 66.3 °C) and cooling of the meteorites cause a pumping effect, resulting in a strong concentration of soluble ions in meteorites over time. The concentrations of water-soluble ions in meteorites, which are complex mixtures of ions from the soil and from oxidation and hydrolysis of meteoritic material, depend on the degree of weathering and are highest at W3. Input of soil contaminants generally dominates over the ions mobilized from meteorites. Silicate hydrolysis preferentially affects olivine and is enhanced by sulfide oxidation, producing local acidic conditions as evidenced by jarosite. Plagioclase weathering is negligible. After completion of troilite oxidation, the rate of chemical weathering slows down with continuing Ca-sulfate contamination.

Calibration of cosmogenic noble gas production in ordinary chondrites based on 36 Cl- 36 Ar ages. Part 1: Refined produced rates for cosmogenic 21 Ne and 38 Ar

We measured the concentrations and isotopic compositions of He, Ne, and Ar in bulk samples and metal separates of 14 ordinary chondrite falls with long exposure ages and high metamorphic grades. In addition, we measured concentrations of the cosmogenic radionuclides 10Be, 26Al, and 36Cl in metal separates and in the nonmagnetic fractions of the selected meteorites. Using cosmogenic 36Cl and 36Ar measured in the metal separates, we determined 36Cl-36Ar cosmic-ray exposure (CRE) ages, which are shielding-independent and therefore particularly reliable. Using the cosmogenic noble gases and radionuclides, we are able to decipher the CRE history for the studied objects. Based on the correlation 3He/21Ne versus 22Ne/21Ne, we demonstrate that, among the meteorites studied, only one suffered significant diffusive losses (about 35%). The data confirm that the linear correlation 3He/21Ne versus 22Ne/21Ne breaks down at high shielding. Using 36Cl-36Ar exposure ages and measured noble gas concentrations, we determine 21Ne and 38Ar production rates as a function of 22Ne/21Ne. The new data agree with recent model calculations for the relationship between 21Ne and 38Ar production rates and the 22Ne/21Ne ratio, which does not always provide unique shielding information. Based on the model calculations, we determine a new correlation line for 21Ne and 38Ar production rates as a function of the shielding indicator 22Ne/21Ne for H, L, and LL chondrites with preatmospheric radii less than about 65 cm. We also calculated the 10Be/21Ne and 26Al/21Ne production rate ratios for the investigated samples, which show good agreement with recent model calculations.

Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set

Stepwise uncertainty reduction (SUR) strategies aim at constructing a sequence of points for evaluating a function  f in such a way that the residual uncertainty about a quantity of interest progressively decreases to zero. Using such strategies in the framework of Gaussian process modeling has been shown to be efficient for estimating the volume of excursion of f above a fixed threshold. However, SUR strategies remain cumbersome to use in practice because of their high computational complexity, and the fact that they deliver a single point at each iteration. In this article we introduce several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel. Such criteria are of particular interest when f is costly to evaluate and several CPUs are simultaneously available. We also manage to drastically reduce the computational cost of these strategies through the use of closed form formulas. We illustrate their performances in various numerical experiments, including a nuclear safety test case. Basic notions about kriging, auxiliary problems, complexity calculations, R code, and data are available online as supplementary materials.