Savolainen Juha, Laurson Lasse, Alava Mikko
Department of Applied Physics, Aalto University, PO Box 11000, 00076 Aalto, Finland.
Computational Physics Laboratory, Tampere University, P.O. Box 692, FI-33101 Tampere, Finland.
Phys Rev E. 2022 May;105(5-1):054152. doi: 10.1103/PhysRevE.105.054152.
Avalanches are often defined as signals higher than some detection level in bursty systems. The choice of the detection threshold affects the number of avalanches, but it can also affect their temporal correlations. We simulated the depinning of a long-range elastic interface and applied different thresholds including a zero one on the data to see how the sizes and durations of events change and how this affects temporal avalanche clustering. Higher thresholds result in steeper size and duration distributions and cause the avalanches to cluster temporally. Using methods from seismology, the frequency of the events in the clusters was found to decrease as a power-law of time, and the size of an event in a cluster was found to help predict how many events it is followed by. The results bring closer theoretical studies of this class of models to real experiments, but also highlight how different phenomena can be obtained from the same set of data.
在突发系统中，雪崩通常被定义为高于某个检测水平的信号。检测阈值的选择会影响雪崩的数量，但也会影响它们的时间相关性。我们模拟了一个长程弹性界面的脱钉过程，并在数据上应用了不同的阈值，包括零阈值，以观察事件的大小和持续时间如何变化，以及这如何影响时间雪崩聚类。较高的阈值会导致更陡峭的大小和持续时间分布，并使雪崩在时间上聚类。使用地震学方法，发现聚类中事件的频率随时间呈幂律下降，并且聚类中一个事件的大小有助于预测其后跟随的事件数量。这些结果使这类模型的理论研究更接近实际实验，但也突出了如何从同一组数据中获得不同的现象。
