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Citations since 2017
14 Research Items
• Geostatistical (spatial stochastic) simulation based on copulas and global optimization methods (simulated annealing, differential evolution). • Integration methodology of different quantitative and qualitative information based on advanced geostatistical methods for reservoir characterization. • Bayesian and geostatistical inversion. • Machine learning with a deep understanding of geostatistical analysis. • Petrophysical seismic inversion. • Rock physics.
Soil CO 2 efflux involves complex biological and physical processes that contribute to the production and transport of CO 2 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 CO 2 efflux relationship due to environmental drivers...
The spatial stochastic co-simulation method based on copulas is a general method that allows simulating variables with any type of dependency and probability distribution functions. This flexibility comes from the use of a copula model for the representation of the joint probability distribution function. The method has been mainly implemented thro...
Some agroecological pest management systems manipulate insect behavior. The push-pull systems are the most functional implemented, reducing the insect-pest density and modifying its distribution. The present work analyzed the spatial distribution of incidence and severity of fall armyworm (FAW) (Spodoptera frugiperda (J.E. Smith), Lepidoptera: Noct...
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 convenie...
Quantifying the role of soils in nature-based solutions require accurate estimates of soil greenhouse gas (GHG) fluxes. Technological advances allow to simultaneously measure multiple GHGs and now 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 si...
This work presents a comparison between the convolutional neural networks (a machine learning estimation method) with the Bernstein copula quantile regression (a geostatistical estimation method) for petrophysical property prediction from seismic data.
A new methodology for the prediction of spatially distributed petrophysical properties using elastic seismic attributes as secondary variables is presented. The method is based on copula function for the estimation of the joint probability distribution function. The proposed method can model linear and complex nonlinear dependency relationships bet...
Predicción de la distribución espacial de propiedades petrofísicas mediante un modelo de dependencia basado en cópulas utilizando atributos sísmicos como variables secundarias.
A new methodology for the simulation of spatially distributed petrophysical properties conditioned by elastic attributes as secondary variables is presented. The method, namely Bernstein copula-based spatial cosimulation (BCSCS), is based on Bernstein copula for the estimation of the joint probability function and simulated annealing for the spatia...
La presente investigación está dirigida a evaluar las potencialidades metalíferas del área concesionada por la empresa EMINCAR sobre la base del procesamiento integral de la información geocientífica generada en investigaciones anteriores y su comparación con las áreas prospectivas reveladas durante los trabajos recientes de levantamiento aerogeofí...
In this work we showed a method of modeling the 3D spatial stochastic distribution of petrophysical properties using a model of dependence based on copula with the seismic attributes, where a prior not require specific functions or a high linear dependence between them. Subsequently, it is compared with the traditional method as a Gaussian sequenti...