Bernstein copula-based spatial cosimulation for petrophysical property prediction conditioned to elastic attributes

Le, Van Huong; Díaz-Viera, Martín A.; Vázquez-Ramírez, Daniel; Valle-García, Raúl del; Erdely, Arturo; Grana, Dario

doi:10.1016/j.petrol.2020.107382

Cited by 6 publications

(8 citation statements)

References 40 publications

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“…First, a deterministic approach was represented by the widely used exponential relationship of soil CO2 efflux with soil temperature (i.e., exponential function-based estimation (EFE); (Lloyd and Taylor 1994)). Then, a probabilistic approach was represented by a BCC method conditioned by temperature (Le et al 2020). We used a two-year time series of soil CO2 efflux measurements from a temperate forest and tested the differences between these approaches.…”

Section: Methodsmentioning

confidence: 99%

“…The Bernstein copula-based cosimulation (BCC) consisted of: a) modeling the joint probability distribution through univariate probability distributions and the copula distribution approximated by the Bernstein polynomial and the Bernstein copula; and b) modeling the temporal dependency function of the variable of interest 𝑌 using the semivariogram function (Le et al 2020). According to Sklar's theorem (Sklar 1959), the joint probability distribution can be decomposed into the univariate probability functions 𝐹(𝑥), 𝐺(𝑦), and the copula function 𝐶: 𝐻(𝑥, 𝑦) = 𝐶(𝐹(𝑥), 𝐺(𝑦)).…”

Section: B Probabilistic Approach: the Bernstein Copula-based Cosimul...mentioning

confidence: 99%

“…The BCC method does not assume linearity, normality of residuals, homogeneity of variance of residuals, or independence of residuals to identify the joint dependence between two variables (Vázquez-Ramírez et al 2023). Briefly, the BCC is based on stochastic realizations obtained by the global optimization method (i.e., differential evolution; (Storn and Price 1997)) where the objective functions are univariate probability distribution, dependency coefficients, and a semivariogram model (Le et al 2020;Vázquez-Ramírez et al 2023). This approach can simulate the behavior of systems characterized by multiple stochastic interdependent processes while reproducing the statistical properties and temporal dependency.…”

Section: Introductionmentioning

confidence: 99%

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Beyond a deterministic representation of the temperature dependence of soil CO2 efflux

Vargas,

2023

Preprint

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Soil CO2 efflux involves complex biological and physical processes that contribute to the production and transport of CO2 from soils to the atmosphere. Temperature is widely used in deterministic empirical models, but these approaches cannot fully capture the complexity of the temperature-soil CO2 efflux relationship due to environmental drivers' confounding and interacting effects beyond temperature. We introduce the Bernstein copula-based cosimulation (BCC) as a data-driven probabilistic approach for modeling the temperature-soil CO2 efflux relationship. The BCC considers the joint probability distribution and temporal dependence of soil CO2 efflux, often ignored in deterministic models. Our results show that this probabilistic approach accurately reproduced the original probability distribution, temporal dependency, and temperature-soil CO2 efflux relationship. We propose that probabilistic approaches hold promise for accurately representing dependency relationships for modeling soil CO2 efflux across various environmental conditions and predicting the effects of climate change.

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Section: Methodsmentioning

confidence: 99%

Section: B Probabilistic Approach: the Bernstein Copula-based Cosimul...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Beyond a deterministic representation of the temperature dependence of soil CO2 efflux

Vargas,

2023

Preprint

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“…Each measured variable is characterized by the univariate probability distribution function and the temporal dependency function. Once these two functions are known, then the behaviors of the variable can be reproduced (Le et al, 2020;Chilès and Delfiner, 2009;Trangmar et al, 1986;Pyrcz and Deutsch, 2014). The tuLHs consists of three steps: (1) modeling the univariate behavior of the variable using the empirical cumulative univariate probability distribution function;…”

Section: Temporal Univariate Latin Hypercube Sampling (Tulhs)mentioning

confidence: 99%

The paradox of assessing greenhouse gases from soils for nature-based solutions

Vargas

2023

Biogeosciences

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Abstract. Quantifying the role of soils in nature-based solutions requires accurate estimates of soil greenhouse gas (GHG) fluxes. Technological advances allow us to measure multiple GHGs simultaneously, and now it is possible to provide complete GHG budgets from soils (i.e., CO2, CH4, and N2O fluxes). We propose that there is a conflict between the convenience of simultaneously measuring multiple soil GHG fluxes at fixed time intervals (e.g., once or twice per month) and the intrinsic temporal variability in and patterns of different GHG fluxes. Information derived from fixed time intervals – commonly done during manual field campaigns – had limitations to reproducing statistical properties, temporal dependence, annual budgets, and associated uncertainty when compared with information derived from continuous measurements (i.e., automated hourly measurements) for all soil GHG fluxes. We present a novel approach (i.e., temporal univariate Latin hypercube sampling) that can be applied to provide insights and optimize monitoring efforts of GHG fluxes across time. We suggest that multiple GHG fluxes should not be simultaneously measured at a few fixed time intervals (mainly when measurements are limited to once per month), but an optimized sampling approach can be used to reduce bias and uncertainty. These results have implications for assessing GHG fluxes from soils and consequently reduce uncertainty in the role of soils in nature-based solutions.

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“…Recently, the copula-based simulation method was successfully applied for the prediction of petrophysical properties using seismic attributes as a secondary variable in (Le, 2021), (Le et al, 2020), (M. Díaz-Viera et al, 2018), (Vázquez, 2018). But in these works, the method was implemented using a non-parametric approach with Bernstein copulas.…”

Section: Introductionmentioning

confidence: 99%

Joint stochastic simulation of petrophysical properties with elastic attributes based on parametric copula models

Vázquez-Ramírez

Díaz-Viera

et al. 2023

Geofis Int

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El método de co-simulación estocástica espacial, basado en cópulas, es un método general que permite simular variables con cualquier tipo de dependencia y funciones de distribución de probabilidad. Esta flexibilidad proviene del uso de un modelo de cópula para la representación de la función de distribución de probabilidad conjunta. El método se ha implementado principalmente a través de un enfoque no paramétrico utilizando cópulas de Bernstein y se ha aplicado con éxito para la simulación de propiedades petrofísicas usando atributos sísmicos elásticos como variables secundarias. En el presente trabajo este método se implementa mediante otros dos enfoques: paramétrico y semi-paramétrico. Específicamente, para el enfoque paramétrico se usa la familia de cópulas Arquimedianas. Primero, el enfoque paramétrico se valida con un caso publicado y luego se realiza una comparación de los tres enfoques en términos de precisión y rendimiento. Los resultados mostraron que el enfoque paramétrico es el que peor reproduce las estadísticas de los datos y presenta mayor incertidumbre con un menor costo computacional, mientras que el enfoque no-paramétrico resultó el que mejor reproduce la dependencia de los datos a un alto costo computacional. El enfoque semi-paramétrico reduce un 10% el costo computacional respecto al no-paramétrico, pero se degrada significativamente su precisión.

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Bernstein copula-based spatial cosimulation for petrophysical property prediction conditioned to elastic attributes

Cited by 6 publications

References 40 publications

Beyond a deterministic representation of the temperature dependence of soil CO2 efflux

Beyond a deterministic representation of the temperature dependence of soil CO2 efflux

The paradox of assessing greenhouse gases from soils for nature-based solutions

Joint stochastic simulation of petrophysical properties with elastic attributes based on parametric copula models

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