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

  • Stefano Bernardinetti CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR); Dipartimento di Scienze chimiche e geologiche, Università degli Studi di Cagliari, Cagliari, Italy. http://orcid.org/0000-0002-9937-0364
  • Stefano Maraio CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR), Italy. http://orcid.org/0000-0001-5027-2061
  • Pier Paolo Gennaro Bruno The Petroleum Institute, Department of Petroleum Geosciences, Abu Dhabi, United Arab Emirates. http://orcid.org/0000-0003-2622-3037
  • Valentina Cicala CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR), Italy.
  • Serena Minucci CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR); Dipartimento di Scienze chimiche e geologiche, Università degli Studi di Cagliari, Cagliari; GeoExplorer Impresa Sociale S.r.l., Cavriglia (AR), Italy.
  • Miriana Giannuzzi CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR), Italy.
  • Marilena Trotta CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR), Italy.
  • Francesco Curedda CGT Centro di Geotecnologie, Università di Siena, 52027 San Giovanni Valdarno (AR), Italy.
  • Simone Febo CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR), Italy.
  • Matteo Vacca CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR), Italy.
  • Enrico Guastaldi | guastaldi@geoexplorer.cgtgroup.org GeoExplorer Impresa Sociale S.r.l., Cavriglia (AR), Italy. http://orcid.org/0000-0002-5756-3539
  • Tommaso Colonna GeoExplorer Impresa Sociale S.r.l., Cavriglia (AR), Italy. http://orcid.org/0000-0002-7363-8078
  • Filippo Bonciani GeoExplorer Impresa Sociale S.r.l., Cavriglia (AR), Italy.
  • Emanuele Tufarolo CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR); Dipartimento di Scienze Fisiche, della Terra e dell’Ambiente, Università di Siena, Siena, Italy.
  • Fabio Brogna Eurovix S.p.A., Entratico (BG), Italy.
  • Andrea Zirulia CGT Centro di Geotecnologie, Università di Siena, San Giovanni Valdarno (AR); Dipartimento di Scienze chimiche e geologiche, Università degli Studi di Cagliari, Cagliari, Italy.
  • Omar Milighetti Nuove Acque S.p.A., Arezzo, Italy.

Abstract

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.

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Published
2017-06-30
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Original Papers
Keywords:
geophysical integration, groundwater research, k-means, shallow aquifer
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How to Cite
Bernardinetti, S., Maraio, S., Bruno, P. P. G., Cicala, V., Minucci, S., Giannuzzi, M., Trotta, M., Curedda, F., Febo, S., Vacca, M., Guastaldi, E., Colonna, T., Bonciani, F., Tufarolo, E., Brogna, F., Zirulia, A., & Milighetti, O. (2017). Potential shallow aquifers characterization through an integrated geophysical method: multivariate approach by means of k-means algorithms. Acque Sotterranee - Italian Journal of Groundwater, 6(2). https://doi.org/10.7343/as-2017-278