Potential shallow aquifers characterization through an integrated geophysical method: multivariate approach by means of k-means algorithms

The need to obtain a detailed hydrogeological characterization of the subsurface and its interpretation for the groundwater resources management, often requires to apply several and complementary geophysical methods. The goal of the approach in this paper is to provide a unique model of the aquifer by synthesizing and optimizing the information provided by several geophysical methods. This approach greatly reduces the degree of uncertainty and subjectivity of the interpretation by exploiting the different physical and mechanic characteristics of the aquifer. The studied area, into the municipality of Laterina (Arezzo, Italy), is a shallow basin filled by lacustrine and alluvial deposits (Pleistocene and Olocene epochs, Quaternary period), with alternated silt, sand with variable content of gravel and clay where the bottom is represented by arenaceous-pelitic rocks (Mt. Cervarola Unit, Tuscan Domain, Miocene epoch). This shallow basin constitutes the unconfined superficial aquifer to be exploited in the nearly future. To improve the geological model obtained from a detailed geological survey we performed electrical resistivity and P wave refraction tomographies along the same line in order to obtain different, independent and integrable data sets. For the seismic data also the reflected events have been processed, a remarkable contribution to draw the geologic setting. Through the k-means algorithm, we perform a cluster analysis for the bivariate data set to individuate relationships between the two sets of variables. This algorithm allows to individuate clusters with the aim of minimizing the dissimilarity within each cluster and maximizing it among different clusters of the bivariate data set. The optimal number of clusters "K", corresponding to the individuated geophysical facies, depends to the multivariate data set distribution and in this work is estimated with the Silhouettes. The result is an integrated tomography that shows a finite number of homogeneous geophysical facies, which therefore permits to distinguish and interpret the porous aquifer in a quantitative and objective way

Ricostruzione probabilistica 3D dell’acquifero alluvionale della Val di Cornia (Provincia di Livorno)

The three-dimensional alluvial aquifer reconstruction through deterministic method from well stratigraphical data is a well-known problem. The purpose of this study concerns the realization of a geostatistical stochastic model based on 1d Markov chains with the use of T-PROGS codes of GMS Aquaveo. This method allows to obtain the vertical transition probability of the alluvial deposits and propagate them to x-y plane through the application of Walther law. The Val di Cornia valley and San Vincenzo coastal plain constitute a unique multilayered coastal aquifer, which extends over an area of 170 square kilometers, in the southern coast of Tuscany (Italy), and it is the results of the erosional and depositional processes of the Cornia river. The better understanding of this aquifer is a crucial issue, due to its regional importance and for managing the increasing saltwater intrusion, which affects the area during the last 50 years. The model realization was initially based on 300 stratigraphic data logs coming from a water well database implemented by local authorities, subsequently integrated with HVSR data acquired for this work. The stratigraphic data were digitized and simplified in order to permit a better reconstruction. The control of the quality of the input data allowed to eliminate the stratigraphic logs that could be inconsistent with the surrounding ones, in order to avoid interpretation problems of the conceptual geological model. This filtering operation led, finally, to the selection of only 140 stratigraphic logs. The processing of this data allowed the reconstruction of the bottom of the model (the bedrock) and the realization of n-equiprobable simulations of the sedimentary hetereogeneity. The obtained geological model will allow the realization of further groundwater flow model of the Cornia Valley to be implemented in the next months

Potential shallow aquifers characterization through an integrated geophysical method: multivariate approach by means of k-means algorithms

The need to obtain a detailed hydrogeological characterization of the subsurface and its interpretation for the groundwater resources management, often requires to apply several and complementary geophysical methods. The goal of the approach in this paper is to provide a unique model of the aquifer by synthesizing and optimizing the information provided by several geophysical methods. This approach greatly reduces the degree of uncertainty and subjectivity of the interpretation by exploiting the different physical and mechanic characteristics of the aquifer. The studied area, into the municipality of Laterina (Arezzo, Italy), is a shallow basin filled by lacustrine and alluvial deposits (Pleistocene and Olocene epochs, Quaternary period), with alternated silt, sand with variable content of gravel and clay where the bottom is represented by arenaceous-pelitic rocks (Mt. Cervarola Unit, Tuscan Domain, Miocene epoch). This shallow basin constitutes the unconfined superficial aquifer to be exploited in the nearly future. To improve the geological model obtained from a detailed geological survey we performed electrical resistivity and P wave refraction tomographies along the same line in order to obtain different, independent and integrable data sets. For the seismic data also the reflected events have been processed, a remarkable contribution to draw the geologic setting. Through the k-means algorithm, we perform a cluster analysis for the bivariate data set to individuate relationships between the two sets of variables. This algorithm allows to individuate clusters with the aim of minimizing the dissimilarity within each cluster and maximizing it among different clusters of the bivariate data set. The optimal number of clusters “K”, corresponding to the individuated geophysical facies, depends to the multivariate data set distribution and in this work is estimated with the Silhouettes. The result is an integrated tomography that shows a finite number of homogeneous geophysical facies, which therefore permits to distinguish and interpret the porous aquifer in a quantitative and objective way