Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure

Karsanina, Marina V.; Gerke, Kirill M.; Скворцова, Е. Б.; Mallants, Dirk

doi:10.1371/journal.pone.0126515

Cited by 94 publications

(33 citation statements)

References 98 publications

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“…Reconstruction accuracy can be further improved by using a more comprehensive set of correlation functions to better capture image information content, as at present no universal method exists 58 59 . A detailed discussion on current issues related to reconstruction accuracy was recently published 60 and is not reported here. It was proven 15 that an intact autocorrelation (two-point probability) function for a superimposed bimodal structure is the same as for a fully resolved image.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock

Gerke

Karsanina

Mallants

2015

Sci Rep

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Spatial data captured with sensors of different resolution would provide a maximum degree of information if the data were to be merged into a single image representing all scales. We develop a general solution for merging multiscale categorical spatial data into a single dataset using stochastic reconstructions with rescaled correlation functions. The versatility of the method is demonstrated by merging three images of shale rock representing macro, micro and nanoscale spatial information on mineral, organic matter and porosity distribution. Merging multiscale images of shale rock is pivotal to quantify more reliably petrophysical properties needed for production optimization and environmental impacts minimization. Images obtained by X-ray microtomography and scanning electron microscopy were fused into a single image with predefined resolution. The methodology is sufficiently generic for implementation of other stochastic reconstruction techniques, any number of scales, any number of material phases, and any number of images for a given scale. The methodology can be further used to assess effective properties of fused porous media images or to compress voluminous spatial datasets for efficient data storage. Practical applications are not limited to petroleum engineering or more broadly geosciences, but will also find their way in material sciences, climatology, and remote sensing.

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Section: Discussion and Outlookmentioning

confidence: 99%

Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock

Gerke

Karsanina

Mallants

2015

Sci Rep

Self Cite

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“…Moreover, in order to enhance the interpretability of the analysis it is desirable for the reduced dimensions to have physical significance. The determination of QoIs typically involves extraction of features or disclosing a bank of descriptors that can be used to train a classifier based on the frequency of observations [81,82]. A conventional yet very useful approach is to use semantic texton forests [83].…”

Section: Microstructure Quantificationmentioning

confidence: 99%

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

et al. 2020

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ICME approaches provide decision support for materials design by establishing quantitative process-structure-property relations. Confidence in the decision support, however, must be achieved by establishing uncertainty bounds in ICME model chains. The quantification and propagation of uncertainty in computational materials science, however, remains a rather unexplored aspect of computational materials science approaches. Moreover, traditional uncertainty propagation frameworks tend to be limited in cases with computationally expensive simulations. A rather common and important model chain is that of CALPHAD-based thermodynamic models of phase stability coupled to phase field models for microstructure evolution. Propagation of uncertainty in these cases is challenging not only due to the sheer computational cost of the simulations but also because of the high dimensionality of the input space. In this work, we present a framework for the quantification and propagation of uncertainty in a CALPHADbased elasto-chemical phase field model. We motivate our work by investigating the microstructure evolution in Mg 2 (Si x Sn 1−x ) thermoelectric materials. We first carry out a Markov Chain Monte Carlo-based inference of the CALPHAD model parameters for this pseudobinary system and then use advanced sampling schemes to propagate uncertainties across a high-dimensional simulation input space. Through high-throughput phase field simulations we generate 200,000 time series of synthetic microstructures and use machine learning approaches to understand the effects of propagated uncertainties on the microstructure landscape of the system under study. The microstructure dataset has been curated in the Open Phase-field Microstructure Database (OPMD), available at http://microstructures.net.

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“…For random fields, the generation can be done by maximizing the joint probability through Markov chain Monte Carlo simulations [35,36]. While it is shown that material generation through these representations is feasible [18,22,8,11], the computational costs for the optimization through gradient [32,11] and non-gradient [28,39,40,41] methods are often high.…”

Section: Data Science Challenges In Computational Materials Sciencementioning

confidence: 99%

Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model

Cang

Yao

et al. 2018

Computational Materials Science

155

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Direct prediction of material properties from microstructures through statistical models has shown to be a potential approach to accelerating computational material design with large design spaces. However, statistical modeling of highly nonlinear mappings defined on highdimensional microstructure spaces is known to be data-demanding. Thus, the added value of such predictive models diminishes in common cases where material samples (in forms of 2D or 3D microstructures) become costly to acquire either experimentally or computationally. To this end, we propose a generative machine learning model that creates an arbitrary amount of artificial material samples with negligible computation cost, when trained on only a limited amount of authentic samples. The key contribution of this work is the introduction of a morphology constraint to the training of the generative model, that enforces the resultant artificial material samples to have the same morphology distribution as the authentic ones. We show empirically that the proposed model creates artificial samples that better match with the authentic ones in material property distributions than those generated from a state-of-the-art Markov Random Field model, and thus is more effective at improving the prediction performance of a predictive structure-property model.

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Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure

Cited by 94 publications

References 98 publications

Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock

Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model

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