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

Eduardo Valero Cano, Jubran Akram, Daniel Peter, Leo Eisner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2019
PublisherSociety of Exploration Geophysicists
Pages3056-3060
Number of pages5
DOIs
StatePublished - Aug 10 2019

Fingerprint Dive into the research topics of 'A fuzzy c-means assisted AIC workflow for arrival picking on downhole microseismic data'. Together they form a unique fingerprint.

Cite this