Mining accidents are sometimes preceded by high levels of crackling noise, which follow universal rules for the collapse of minerals. The archetypal test cases are sandstone and coal. Their collapse mechanism is almost identical to earthquakes: the crackling noise in large, porous samples follows a power law (Gutenberg-Richter) distribution P ~ E^−ε with energy exponents ε for near critical stresses of ε = 1.55 for dry and wet sandstone, and ε = 1.32 for coal. The exponents of early stages are slightly increased, 1.7 (sandstone) and 1.5 (coal), and appear to represent the collapse of isolated, uncorrelated cavities. A significant increase of the acoustic emission, AE, activity was observed close to the final failure event, which acts as “warning signal” for the impending major collapse. Waiting times between events also follow power law distributions with exponents 2+ξ between 2 and 2.4. Aftershocks occur with probabilities described by Omori coefficients p between 0.84 (sandstone) and 1 (coal). The “Båth’s law” predicts that the ratio between the magnitude of the main event and the largest aftershock is 1.2. Our experimental findings confirm this conjecture. Our results imply that acoustic warning methods are often possible within the context of mining safety measures, although it is not only the increase of crackling noise that can be used as early warning signal but also the change of the energy distribution of the crackling events.

The quest for predictive indicators for the collapse of coal mines has led to a robust criterion from scale-model tests in the laboratory. Mechanical collapse under uniaxial stress forms avalanches with a power-law probability distribution function of radiated energy $P\sim {E^{-}}^{\varepsilon }$, with exponent $\varepsilon =1.5$. Impending major collapse is preceded by a reduction of the energy exponent to the mean-field value $\varepsilon =1.32$. Concurrently, the crackling noise increases in intensity and the waiting time between avalanches is reduced when the major collapse is approaching. These latter criteria were so-far deemed too unreliable for safety assessments in coal mines. We report a reassessment of previously collected extensive collapse data sets using “record-breaking analysis,” based on the statistical appearance of “superjerks” within a smaller spectrum of collapse events. Superjerks are defined as avalanche signals with energies that surpass those of all previous events. The final major collapse is one such superjerk but other “near collapse” events equally qualify. In this way a very large data set of events is reduced to a sparse sequence of superjerks (21 in our coal sample). The main collapse can be anticipated from the sequence of energies and waiting times of superjerks, ignoring all weaker events. Superjerks are excellent indicators for the temporal evolution, and reveal clear nonstationarity of the crackling noise at constant loading rate, as well as self-similarity in the energy distribution of superjerks as a function of the number of events so far in the sequence $E_{sj}\sim n^{\delta }$ with $\delta =1.79$. They are less robust in identifying the precise time of the final collapse, however, than the shift of the energy exponents in the whole data set which occurs only over a short time interval just before the major event. Nevertheless, they provide additional diagnostics that may increase the reliability of such forecasts.