The estimation of the hydraulic parameters of an aquifer such as the hydraulic conductivity is somehow complicated due to its heterogeneity, on the other hand field and laboratory tests are both time consuming and costly. The use of geostatistical-based techniques for data assimilation could represent an alternative tool that allows the use of space-time aquifer behaviour to characterize hydraulic conductivity heterogeneity. In this paper, a spatiotemporal bivariate methodology was implemented combining historical hydraulic head data with hydraulic conductivity sparse data in order to obtain an estimate of the spatial distribution of the latter variable. This approach takes advantage of the correlation between the hydraulic conductivity (K) and the hydraulic head (H) behaviour through time. In order to evaluate this approach, a synthetic experiment was constructed through a transitory numerical flow-model that simulates hydraulic head values in a horizontally-heterogeneous aquifer. Geostatistical tools were used to describe the correlation between simulated spatiotemporal data of hydraulic head and the spatial distribution of the hydraulic conductivity in a group of model nodes. Subsequently, the Kalman filter was used to estimate the hydraulic conductivity values at nonsampled sites. The results showed acceptable differences between estimated and synthetic hydraulic conductivity data, with low estimate error variances (predominating the 1 m2/day2 value for K for all the cases, however, the smallest number of cells with values above 2 m2/day2 correspond to the bivariate spatiotemporal case) and the best agreement between the estimated errors and the selected model variance (SMSE values of 0.574 and 0.469) were found for the bivariate cases, which suggests that the implemented methodology could be used for reducing calibration efforts, particularly when the hydraulic parameters data are scarce.
La presente investigación se centra en el estudio de las canteras de ignimbrita con las que actualmente se realizan las restauraciones en Morelia Michoacan, México, además se obtuvieron resultados de muestras de roca con las que se construyeron algunos edificios y otras de canteras que actualmente se encuentran cerrados. Resulta interesante que al obtener los datos de las diferentes pruebas físico-mecánicas y compararlas se obtenga un buen modelo matemático mediante el ajuste de curvas a los datos de las pruebas, lo que permite estimar el Is(50) que es un índice de clasificación mecánica de la roca, obteniendo un coeficiente de correlación bastante aceptable. Se realizó la prueba de partículas alargadas y lajeadas permitiendo entender la mecánica de falla del material.
Denoising is an important task inside the image processing area. In this paper, an algorithm for detecting and suppressing salt and pepper noise is presented. Firstly, the algorithm computes an estimation of the denoised image by using a variant of the Non-Local Means proposal. This estimation is segmented in order to detect corrupted pixels avoiding misclassifying pixels with extreme values that belong to objects on the uncorrupted image. Once pixels are classified, the algorithm performs a suppression step by using an adaptive median filter. Obtained results show that the implementation of this proposal gives good noise detection and suppression.
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