显著数据丢失对自然时间内去趋势波动分析识别破裂前电信号的影响
Effect of significant data loss on identifying electric signals that precede rupture by detrended fluctuation analysis in natural time
摘要：
破裂前出现的电场变化最近已被研究，采用去趋势波动分析（DFA）作为量化的长期时间相关性的标度方法。这些研究表明，地震电信号（SES）的活动表现出一个规模不变的功能与指数$α_{DFA} \approx 1$在所有规模的调查（约五个数量级）。在这里，我们研究发生了什么重大的数据丢失，这是一个问题的首要的实际重要性，并表明，DFA适用于自然时间表示的剩余数据仍然揭示了SES活动的指数接近1.0，这显着超过人工（人造）噪声中发现的指数。这与自然时间分析相结合，即使在显著（70%）数据丢失之后，也能够以75%的概率识别SES活动。对于50%的数据丢失，概率增加到90%或更大。
英文摘要：
Electric field variations that appear before rupture have been recently studied by employing the detrended fluctuation analysis (DFA) as a scaling method to quantify long-range temporal correlations. These studies revealed that seismic electric signals (SES) activities exhibit a scale invariant feature with an exponent $α_{DFA} \approx 1$ over all scales investigated (around five orders of magnitude). Here, we study what happens upon significant data loss, which is a question of primary practical importance, and show that the DFA applied to the natural time representation of the remaining data still reveals for SES activities an exponent close to 1.0, which markedly exceeds the exponent found in artificial (man-made) noises. This, in combination with natural time analysis, enables the identification of a SES activity with probability 75% even after a significant (70%) data loss. The probability increases to 90% or larger for 50% data loss.
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