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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 May 2020

13 May 2020

Review status
This preprint is currently under review for the journal SE.

On a new robust workflow for the statistical and spatial analysis of fracture data collected with scanlines (or the importance of stationarity)

Andrea Bistacchi1, Silvia Mittempergher2, Mattia Martinelli1, and Fabrizio Storti3 Andrea Bistacchi et al.
  • 1Dipartimento di Scienze dell'Ambiente e della Terra, Università degli Studi di Milano Bicocca, Piazza della Scienza, 4, 20126 Milano
  • 2Dipartimento di Scienze Chimiche e Geologiche, Università degli Studi di Modena e Reggio Emilia, Via G. Campi 106, 41125 Modena
  • 3NEXT – Natural and Experimental Tectonics Research Group, Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, Università degli Studi di Parma, Parco Area delle Scienze 11/a, 43124 Parma

Abstract. We present an innovative workflow for the statistical analysis of fracture data collected along scanlines, composed of two major stages, each one with alternative options. A prerequisite in our analysis is the assessment of stationarity of the dataset, that is motivated by statistical and geological motivations. Calculating statistics on non-stationary data can be statistically meaningless, and moreover the normalization and/or sub-setting approach that we discuss here can greatly improve our understanding of geological deformation processes. Our methodology is based on the analysis of the cumulative spacing function (CSF) and cumulative spacing derivative (CSD), that allows defining the boundaries of stationary domains in an objective way. Once stationarity has been analysed, other statistical methods already known in literature can be applied. Here we discuss in details methods aimed at understanding the degree of saturation of fracture systems based on the type of spacing distribution, and we evidence their limits in cases where they are not supported by a proper spatial statistics analysis.

Andrea Bistacchi et al.

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Andrea Bistacchi et al.

Andrea Bistacchi et al.


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