Artifactual log-periodicity in finite size data: Relevance for earthquake aftershocks
Y. Huang, A. Johansen, M.W. Lee, H. Saleur, D. Sornette

TL;DR
This paper reveals that observed log-periodic patterns in earthquake aftershock data can be artifacts caused by data manipulation and sampling methods, rather than genuine physical phenomena.
Contribution
The study introduces a synthetic mechanism explaining how data processing steps can produce apparent log-periodicity in finite size datasets.
Findings
Most observed log-periodicity in aftershock sequences results from data analysis artifacts.
Logarithmic sampling and filtering steps can create spurious spectral peaks.
Resolving genuine log-periodicity in seismic data is statistically challenging.
Abstract
The recently proposed discrete scale invariance and its associated log-periodicity are an elaboration of the concept of scale invariance in which the system is scale invariant only under powers of specific values of the magnification factor. We report on the discovery of a novel mechanism for such log-periodicity relying solely on the manipulation of data. This ``synthetic'' scenario for log-periodicity relies on two steps: (1) the fact that approximately logarithmic sampling in time corresponds to uniform sampling in the logarithm of time; and (2) a low-pass-filtering step, as occurs in constructing cumulative functions, in maximum likelihood estimations, and in de-trending, reddens the noise and, in a finite sample, creates a maximum in the spectrum leading to a most probable frequency in the logarithm of time. We explore in detail this mechanism and present extensive numerical…
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