A fuzzy c-means assisted AIC workflow for arrival picking on downhole microseismic data

Abstract

We propose a workflow for automatic P- and S-wave arrival picking on downhole microseismic data. It uses conditional fuzzy c-means clustering to identify time intervals of possible wave arrivals. We classify the signal intervals as P- and S-wave using the first and second eigenvalues of the waveforms contained within. The Akaike information criterion (AIC) picker is then applied to the identified P- and S-wave intervals for arrival picking. Using real microseismic dataset examples, we show that the proposed workflow yields accurate arrival picks for both high and low signal-to-noise ratio waveforms. The identification of signal intervals, however, uses features based on amplitude, thus remains susceptible to high amplitude noise.