Section 2: Data processing, statistical analysis and empirical laws and models

Data recording and data processing techniques in non-linear mechanical processes differ depending on the underlying processes and field of study. Analytical toolsets are often designed to deal with the limitations imposed by data acquisition. In a best-case scenario, we know, record, or infer the microscopic internal fields and/or the microstructural rearrangements. Otherwise, full or partial time series from one or multiple devices can detect remotely acoustic, seismic, magnetic, optical or calorimetric signals in a narrow or broad-band spectrum. Individual events, or avalanches, are usually represented in stochastic models with explicit parameter dependencies. These usually include empirical observations, such as scale-free relations, distributions, and accelerations, temporal, and spatial correlations, etc..

This section calls for reviews and latest breakthroughs regarding empirical laws and the techniques used to characterize them: signal processing; match-filtering techniques; avalanche propagation profiles; internal measurements and remote inference of stress and displacement fields; magnitude distributions and magnitude relations; estimation of critical exponents; finite-size scaling; outlier detection; determination of other precursors to failure; identification of event-event correlations, spatial and temporal clustering, etc. We will discuss the possibility to extend analytical procedures to a generalized toolset to study numerical and experimental datasets.