Quantification of Long-Range Persistence in Geophysical Time Series: Conventional and Benchmark-Based Improvement Techniques

TY - JOUR

T1 - Quantification of Long-Range Persistence in Geophysical Time Series

T2 - Conventional and Benchmark-Based Improvement Techniques

AU - Witt, Annette

AU - Malamud, Bruce D.

PY - 2013/9/1

Y1 - 2013/9/1

N2 - Time series in the Earth Sciences are often characterized as self-affine long-range persistent, where the power spectral density, S, exhibits a power law dependence on frequency, f, S(f)~ f-β, with β the persistence strength. For modelling purposes, it is important to determine the strength of self-affine long-range persistence β as precisely as possible and to quantify the uncertainty of this estimate. After an extensive review and discussion of asymptotic and the more specific case of self-affine long-range persistence, we compare four common analysis techniques for quantifying self-affine long-range persistence: (a) rescaled range (R/S) analysis, (b) semivariogram analysis, (c) detrended fluctuation analysis, and (d) power spectral analysis. To evaluate these methods, we construct ensembles of synthetic self-affine noises and motions with different (i) time series lengths N = 64,128,256,...,131,072, (ii) modelled persistence strengths βmodel = -1.0, -0.8,-0.6,...,4.0, and (iii) one–point probability distributions (Gaussian, log-normal: coefficient of variation cv = 0.2‒2.0, Levy: tail parameter a = 1.0‒1.9) and evaluate the four techniques by statistically comparing their performance. Over 17,000 sets of parameters are produced, each characterising a given process; for each process type, 100 realisations are created. The four techniques give the following results in terms of systematic error (bias = average performance test results for β over 100 realisations minus modelled β) and random error (standard deviation of measured β over 100 realisations): (i) Hurst rescaled range (R/S) analysis is not recommended to use due to large systematic errors. (ii) Semivariogram analysis shows no systematic errors but large random errors for self-affine noises with . (iii) Detrended fluctuation analysis is well-suited for time series with thin-tailed probability distributions and for persistence strengths of . (iv) Spectral techniques perform the best of all four techniques: for self-affine noises with positive persistence ( ) and symmetric one–point distributions they have no systematic errors and compared to the other three techniques, small random errors; for anti-persistent self-affine noises ( ) and asymmetric one-point probability distributions, spectral techniques have small systematic and random errors. For quantifying the strength of long-range persistence of a time series, benchmark-based improvements to the estimator predicated on the performance for self-affine noises with the same time series length and one–point probability distribution are proposed. This scheme adjusts for the systematic errors of the considered technique and results in realistic 95% confidence intervals for the estimated strength of persistence. We finish this paper by discussing long-range persistence of three geophysical time series—palaeotemperature, river discharge, and Auroral electrojet index—with the three representing three different types of probability distribution—Gaussian, log-normal, and Levy, respectively.

AB - Time series in the Earth Sciences are often characterized as self-affine long-range persistent, where the power spectral density, S, exhibits a power law dependence on frequency, f, S(f)~ f-β, with β the persistence strength. For modelling purposes, it is important to determine the strength of self-affine long-range persistence β as precisely as possible and to quantify the uncertainty of this estimate. After an extensive review and discussion of asymptotic and the more specific case of self-affine long-range persistence, we compare four common analysis techniques for quantifying self-affine long-range persistence: (a) rescaled range (R/S) analysis, (b) semivariogram analysis, (c) detrended fluctuation analysis, and (d) power spectral analysis. To evaluate these methods, we construct ensembles of synthetic self-affine noises and motions with different (i) time series lengths N = 64,128,256,...,131,072, (ii) modelled persistence strengths βmodel = -1.0, -0.8,-0.6,...,4.0, and (iii) one–point probability distributions (Gaussian, log-normal: coefficient of variation cv = 0.2‒2.0, Levy: tail parameter a = 1.0‒1.9) and evaluate the four techniques by statistically comparing their performance. Over 17,000 sets of parameters are produced, each characterising a given process; for each process type, 100 realisations are created. The four techniques give the following results in terms of systematic error (bias = average performance test results for β over 100 realisations minus modelled β) and random error (standard deviation of measured β over 100 realisations): (i) Hurst rescaled range (R/S) analysis is not recommended to use due to large systematic errors. (ii) Semivariogram analysis shows no systematic errors but large random errors for self-affine noises with . (iii) Detrended fluctuation analysis is well-suited for time series with thin-tailed probability distributions and for persistence strengths of . (iv) Spectral techniques perform the best of all four techniques: for self-affine noises with positive persistence ( ) and symmetric one–point distributions they have no systematic errors and compared to the other three techniques, small random errors; for anti-persistent self-affine noises ( ) and asymmetric one-point probability distributions, spectral techniques have small systematic and random errors. For quantifying the strength of long-range persistence of a time series, benchmark-based improvements to the estimator predicated on the performance for self-affine noises with the same time series length and one–point probability distribution are proposed. This scheme adjusts for the systematic errors of the considered technique and results in realistic 95% confidence intervals for the estimated strength of persistence. We finish this paper by discussing long-range persistence of three geophysical time series—palaeotemperature, river discharge, and Auroral electrojet index—with the three representing three different types of probability distribution—Gaussian, log-normal, and Levy, respectively.

KW - fractional noises and motions

KW - self-affine time series

KW - long-range persistence

KW - Hurst rescaled range (R/S) analysi

KW - semivariogram analysis

KW - detrended fluctuation analysi

KW - power-spectral analysi

KW - random and systematic errors

KW - root mean squared erro

KW - confideence intervals

KW - benchamrk-based imprvoements

KW - geographysical time series

U2 - 10.1007/s10712-012-9217-8

DO - 10.1007/s10712-012-9217-8

M3 - Article

SN - 0169-3298

VL - 34

SP - 541

EP - 651

JO - SURVEYS IN GEOPHYSICS

JF - SURVEYS IN GEOPHYSICS

IS - 5

ER -