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Estimation of joint trace length probability distribution function in igneous, sedimentary, and metamorphic rocks

Abstract

To predict the behavior of structures in and on jointed rock masses, it is necessary to characterize the geomechanical properties of joints and intact rock. Among geometry properties of joints, trace length has a vital importance, because it affects rock mass strength and controls the stability of the rock structures in jointed rock masses. Since joint length has a range of values, it is useful to have an understanding of the distribution of these values in order to predict how the extreme values may be compared to the values obtained from a small sample. For this purpose, three datasets of joint systems from nine exposures of igneous, metamorphic, and sedimentary rocks are studied. Joint trace length is one of the most difficult properties to measure accurately, but it may be possible to record other geometrical properties of exposed joints accurately; thereby, support vector machine (SVM) model is used to predict the joint trace length. SVM is a novel machine learning method, which is a powerful tool used to solve the problem characterized by small sample and non-linearity with a good generalization performance. Consequently, goodness-of-fit (GOF) tests were applied on these data. According to these GOF tests, the lognormal distribution was found to be the best probability distribution function for representing a joint trace length distribution.

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Correspondence to Jamal Zadhesh.

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Zadhesh, J., Jalali, SM.E. & Ramezanzadeh, A. Estimation of joint trace length probability distribution function in igneous, sedimentary, and metamorphic rocks. Arab J Geosci 7, 2353–2361 (2014). https://doi.org/10.1007/s12517-013-0861-1

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  • DOI: https://doi.org/10.1007/s12517-013-0861-1

Keywords

  • Jointed rock masses
  • Joint trace length
  • Distribution function
  • Support vector machine
  • Goodness-of-fit tests (GOF)
  • Lognormal distribution