Fast waveform detection for microseismic imaging using unsupervised machine learning

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, that is 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. I use mean, power, and spectral centroid as the three features to help the machine to characterize each data point. Both synthetic and real microseismic data examples are used to demonstrate the feasibility of the proposed algorithm. Results show that the algorithm can help detect the dominant waveforms in the data in an effective and efficient manner. The automatically detected waveforms can help quickly obtain the microseismic imaging result using an amplitude-based reverse time migration method.