Fast waveform detection for microseismic imaging using unsupervised machine learning Geophysical Journal International Oxford Academic

Automatic arrival picking of certain seismic or microseismic phases has been studied for decades. However, automatic detection of continuous signal waveforms has been seldom addressed. In this paper, I propose a novel approach for automatically detecting the waveforms in the microseismic data. The waveform detection can be formulated into a classification-based machine learning (ML) problem, i.e., each data point in the microseismic record needs to be classified as either waveform or non-waveform. I use the classic K-means clustering based unsupervised machine learning algorithm to solve this problem.