We report signatures of criticality in mining accident data obtained from the Mine Accident, Injury and Illness Report form (MSHA Form 7000-1). This work builds on the hypothesis that workplace accident statistics follow self-organized criticality (Mauro et al., 2018). “1/f noise,” a distinct feature of critical systems, is extracted from this database and is used to forecast accident trends using a long short-term memory (LSTM) recurrent neural network (RNN). The algorithm used for extracting this noise is applicable to data available in any standard worker's compensation database. We also report a Pareto distribution in the number of accidents in relation to employee mine experience, implying a strong correlation between experience and susceptibility to accidents.
|Original language||English (US)|
|Journal||Physica A: Statistical Mechanics and its Applications|
|State||Published - Jan 1 2020|
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Condensed Matter Physics