Earthquake arrival identification in a record with technogenic noise

Analysis of the problem of seismic event identification at an increased level of noise, by the data of a single sensor or local group of sensors, is made. This problem is the key to development of an ultra-short-term warning system of an earthquake that has occurred. Catastrophic damage and human deaths are usually caused by the sudden character of earthquake occurrence. If data processing could be done rapidly (i.e., within 4–5 s), such an earthquake warning would be of value for decreasing human deaths and economic damage. The aim of the present study is to investigate the selection of information features of signals in order to reduce the feature dimensionality and to incorporate the particular features of self-contained systems. Special emphasis is placed on the possibility of neural network techniques for the analysis of class separability in the attribute space. Our system will be helpful for reducing the impact of a disastrous earthquake. The main problems are highlighted and the methods for their solution are discussed; tests are carried out for a test problem, consisting in detection of small earthquakes in a noise-contaminated record. It is intended to develop algorithms and programs for identification of earthquake phase arrivals at an increased level of industrial and other noise. Further work will consist in designing instrumental solutions of the system.