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“Empirical Prewhitening” Spectral Analysis Detects Periodic but Inconsistent Signals in Abyssal Hill Morphology at the Southern East Pacific Rise
Geochemistry, Geophysics, Geosystems ( IF 2.9 ) Pub Date : 2020-10-02 , DOI: 10.1029/2020gc009261
John A. Goff 1
Affiliation  

The existence, or not, of periodicities in abyssal hill morphology has been vigorously debated in recent publications, and some have hypothesized that such periodicities are evidence of the impact of Milankovitch cycle‐caused sea level fluctuations on the volcanic construction process at mid‐ocean ridges. Periodicities are detected by the presence of spectral peaks that rise significantly above the random variations of sample power spectra associated with an aperiodic, continuous spectrum process, typically modeled as a band‐limited fractal (von Kármán model). Here, I formulate and test a new algorithm to “empirically prewhiten” the sample power spectrum which, without needing to model the continuous spectrum, flattens it to a zero‐mean process. This greatly simplifies definition of the null hypothesis, and additional modeling approximates standard deviation levels that provide a conservative basis for detecting peaks that may be indicative of periodicity. The algorithm is applied to extensive bathymetric data flanking the southern East Pacific Rise. Significant periodicities are detected on many profiles analyzed, but the periods vary widely, and do not cluster at Milankovitch periods. The most substantial harmonic signals detected exhibit periods ∼0.082–0.216 my, with root‐mean square (RMS) heights approximately a quarter to a third of the RMS height for the aperiodic signal. It is hypothesized that the dominant aperiodic component of abyssal hills corresponds to morphology constructed by faults that follow a random distribution governed by scaling laws, whereas longer‐scale periodic signals are associated with crustal thickness variations controlled internally by variations in melt supply.

中文翻译:

“经验性预增白”光谱分析可检测到东太平洋南部南部深海山地貌的周期性但不一致的信号

在最近的出版物中激烈地讨论了深海山形态的周期性是否存在,并且有一些假设认为这种周期性是米兰科维奇周期引起的海平面波动对中海脊火山构造过程的影响的证据。 。周期性检测是通过频谱峰值的存在来检测的,该频谱峰值明显高于与非周期性连续频谱过程相关的样本功率频谱的随机变化,通常将其建模为带限分形(vonKármán模型)。在这里,我制定并测试了一种新的算法,以“根据经验”对样本功率谱进行“预白化”,而无需对连续谱建模,就可以将其展平为零均值过程。这大大简化了原假设的定义,额外的建模近似于标准偏差水平,这为检测可能指示周期性的峰提供了保守的基础。该算法适用于东太平洋南部南部两侧的大量测深数据。在分析的许多轮廓上都检测到显着的周期性,但是周期变化很大,并且在Milankovitch周期上不聚集。检测到的最大谐波信号的周期约为0.082-0.216my,均方根(RMS)高度约为非周期信号RMS高度的四分之一至三分之一。假设深海丘陵的主要非周期性成分对应于断层构造的形态,这些断层遵循由比例定律控制的随机分布,
更新日期:2020-11-02
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