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We propose an algorithm to estimate the Hurst exponent of high-dimensionalfractals, based on a generalized high-dimensional variance around a movingaverage low-pass filter. As working examples, we consider rough surfacesgenerated by the Random Midpoint Displacement and by the Cholesky-LevinsonFactorization algorithms. The surrogate surfaces have Hurst exponents rangingfrom 0.1 to 0.9 with step 0.1, and different sizes. The computationalefficiency and the accuracy of the algorithm are also discussed.

Algorithm to estimate the Hurst exponent of high-dimensional fractals