Inversión de registros sónicos por medio de un algoritmo robusto basado en estrategias evolutivas

Los modelos de física de rocas son de gran importancia cuando se requiere de la estimación de las propiedades petrofísicas a partir de información sísmica o de registros de pozos. Losmodelos de física de rocas relacionan los parámetros elásticos con los petrofísicos mediante ecuaciones que se han formulado empíricamente. La mayoría de estos modelos empíricos son no lineales, por lo que la estimación de los parámetros de interés es llevada a cabo mediante métodos de optimización iterativos. Existen diferentes métodos de optimización para problemas no lineales, pero de manera general se pueden clasificar en deterministas o estocásticos. Dos de los tipos de algoritmos utilizados en optimización no lineal son el de mínimos cuadrados no lineales (determinista) y los Algoritmos Evolutivos (estocástico). En este trabajo se implementa un algoritmo basado en Estrategias Evolutivas, el cual es un método de optimización estocástica basado en poblaciones, con el fin de estimar los parámetros petrofísicos a partir de datos de registros sónicos y de densidad. Además, se realiza una comparación con el método de Levenberg-Marquardt y otro algoritmo que utiliza Programación Evolutiva. El objetivo de este trabajo es introducir el uso de las Estrategias Evolutivas en la estimación de los parámetros petrofísicos, además de demostrar que es un algoritmo robusto. Se utilizan datos sintéticos para mostrar el funcionamiento de los algoritmos propuestos, además de usar el algoritmo de Estrategias Evolutivas en la estimación de parámetros petrofísicos en un yacimiento petrolero

A Climate-Mathematical Clustering of Rainfall Stations in the Río Bravo-San Juan Basin (Mexico) by Using the Higuchi Fractal Dimension and the Hurst Exponent

When conducting an analysis of nature’s time series, such as meteorological ones, an important matter is a long-range dependence to quantify the global behavior of the series and connect it with other physical characteristics of the region of study. In this paper, we applied the Higuchi fractal dimension and the Hurst exponent (rescaled range) to quantify the relative trend underlying the time series of historical data from 17 of the 34 weather stations located in the Río Bravo-San Juan Basin, Mexico; these data were provided by the National Water Commission CONAGUA) in Mexico. In this way, this work aims to perform a comparative study about the level of persistency obtained by using the Higuchi fractal dimension and Hurst exponent for each station of the basin. The comparison is supported by a climate clustering of the stations, according to the Köppen classification. Results showed a better fitting between the climate of each station and its Higuchi fractal dimension obtained than when using the Hurst exponent. In fact, we found that the more the aridity of the zone the more the persistency of rainfall, according to Higuchi’s values. In turn, we found more relation between the Hurst exponent and the accumulated amount of rainfall. These are relations between the climate and the long-term persistency of rainfall in the basin that could help to better understand and complete the climatological models of the study region. Trends between the fractal exponents used and the accumulated annual rainfall were also analyzed

Statistical Analysis of PM10 Concentration in the Monterrey Metropolitan Area, Mexico (2010–2018)

Air-quality monitoring and analysis are initial parts of a comprehensive strategy to prevent air pollution in cities. In such a context, statistical tools play an important role in determining the time-series trends, locating areas with high pollutant concentrations, and building predictive models. In this work, we analyzed the spatio-temporal behavior of the pollutant PM10 in the Monterrey Metropolitan Area (MMA), Mexico during the period 2010–2018 by applying statistical analysis to the time series of seven environmental stations. First, we used experimental variograms and scientific visualization to determine the general trends and variability in time. Then, fractal exponents (the Hurst rescaled range and Higuchi algorithm) were used to analyze the long-term dependence of the time series and characterize the study area by correlating that dependence with the geographical parameters of each environmental station. The results suggest a linear decrease in PM10 concentration, which showed an annual cyclicity. The autumn-winter period was the most polluted and the spring-summer period was the least. Furthermore, it was found that the highest average concentrations are located in the western and high-altitude zones of the MMA, and that average concentration is related in a quadratic way to the Hurst and Higuchi exponents, which in turn are related to some geographic parameters. Therefore, in addition to the results for the MMA, the present paper shows three practical statistical methods for analyzing the spatio-temporal behavior of air quality

Statistical Analysis of PM₁₀ Concentration in the Monterrey Metropolitan Area, Mexico (2010–2018)

Air-quality monitoring and analysis are initial parts of a comprehensive strategy to prevent air pollution in cities. In such a context, statistical tools play an important role in determining the time-series trends, locating areas with high pollutant concentrations, and building predictive models. In this work, we analyzed the spatio-temporal behavior of the pollutant PM10 in the Monterrey Metropolitan Area (MMA), Mexico during the period 2010–2018 by applying statistical analysis to the time series of seven environmental stations. First, we used experimental variograms and scientific visualization to determine the general trends and variability in time. Then, fractal exponents (the Hurst rescaled range and Higuchi algorithm) were used to analyze the long-term dependence of the time series and characterize the study area by correlating that dependence with the geographical parameters of each environmental station. The results suggest a linear decrease in PM10 concentration, which showed an annual cyclicity. The autumn-winter period was the most polluted and the spring-summer period was the least. Furthermore, it was found that the highest average concentrations are located in the western and high-altitude zones of the MMA, and that average concentration is related in a quadratic way to the Hurst and Higuchi exponents, which in turn are related to some geographic parameters. Therefore, in addition to the results for the MMA, the present paper shows three practical statistical methods for analyzing the spatio-temporal behavior of air quality