Notice first that . Moreover A'1,k,TA1,k,T contains the lags of the autocorrelation of the data: thus, it is the covariance matrix Ryk,T of the data (up to time T). So, the value can be compared to the probability of the sequence according to the covariance matrix Ryk,T:
A small value of means that the previous samples don't deviate from the general statistics of the data. On the contrary, a sudden burst of noise, or a new strong seismic arrival, will produce a large value of . This occurrence will also perturb strongly the covariances , Rrk,T, and the correlation , whose updatings involve a division by (small in that case). So the variable can be assimilated to a likelihood variable, and used for detection of unexpected events.