Abstract A new scalable segmentation algorithm is proposed in this paper for the forensic determination of level shifts in geophysical time series. While a number of segmentation algorithms exist, they are generally not ‘big data friendly’ due either to quadratic scaling of computation time in the length of the series N or subjective penalty parameters. The proposed algorithm is called SumSeg as it collects a table of potential break points via iterative ternary splits on the extreme values of the scaled partial…