The problem of detecting the presence of a signal that can lead to a disaster is studied. A decision-maker collects data sequentially over time. At some point in time, called the change point, the distribution of data changes. This change in distribution could be due to an event or a sudden arrival of an enemy object. If not detected quickly, this change has the potential to cause a major disaster. In space and military applications, the values of the measurements can stochastically grow with time as the enemy object moves closer to the target. A new class of stochastic processes, called exploding processes, is introduced to model stochastically growing data. An algorithm is proposed and shown to be asymptotically optimal as the mean time to a false alarm goes to infinity.

翻译：研究了可能导致灾难的信号检测问题。决策者随时间序贯地收集数据。在某个称为变化点的时间点，数据分布发生改变。这种分布变化可能由事件或敌方物体的突然逼近引起。若未能及时检测，该变化可能引发重大灾难。在空间和军事应用中，随着敌方物体向目标靠近，测量值可能随机增长。本文引入一类新型随机过程——爆炸过程（exploding processes）——来建模随机增长的数据。提出一种算法，并证明当虚假警报平均时间趋于无穷大时，该算法是渐近最优的。