Abstract
Avalanches have been experimentally investigated in a wide range of physical systems from granular physics to friction. Here, we measure and detect avalanches in a 2D granular stick-slip experiment. We discuss the conventional way of signal processing for avalanche extraction and how statistics depend on several parameters that are chosen in the analysis process. Then, we introduce another way of detecting avalanches using wavelet transformations that can be applied in many other systems. We show that by using this method and measuring Lipschitz exponents, we can intelligently detect noise in a signal, which leads to a better avalanche extraction and more reliable avalanche statistics.
Highlights
Many systems, under slow loading, exhibit intermittent and discrete events of broad sizes, known as crackling noise [1, 2]
We introduce a method for avalanche detection based on wavelet transformation to extract more reliable statistics
Our experimental apparatus enables us to study the avalanches in a granular stick-slip experiment
Summary
Under slow loading, exhibit intermittent and discrete events of broad sizes, known as crackling noise [1, 2]. We introduce a method for avalanche detection based on wavelet transformation to extract more reliable statistics. For a given pulling stage speed v, spring stiffness k, and granular bed height l, the evolution of the force F, applied to the slider is measured with a frequency of 100Hz in our experiment. These flaws can be reduced by applying a wavelet transform domain filter that selectively distinguishes noise spikes from events.
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