Seismic signal recognition by unsupervised machine learning

Abstract

SUMMARYSeismic signal recognition can serve as a powerful auxiliary tool for analysing and processing ever-larger volumes of seismic data. It can facilitate many subsequent procedures such as first-break picking, statics correction, denoising, signal detection, events tracking, structural interpretation, inversion and imaging. In this study, I propose an automatic technique of seismic signal recognition taking advantage of unsupervised machine learning. In the proposed technique, seismic signal recognition is considered as a problem of clustering data points. All the seismic sampling points in time domain are clustered into two clusters, that is, signal or non-signal. The hierarchical clustering algorithm is used to group these sampling points. Four attributes, that is, two short-term-average-to-long-term-average ratios, variance and envelope are investigated in the clustering process. In addition, to quantitatively evaluate the performance of seismic signal recognition properly, I propose two new statistical indicators, namely, the rate between the total energies of original and recognized signals (RTE), and the rate between the average energies of original and recognized signals (RAE). A large number of numerical experiments show that when the signal is slightly corrupted by noise, the proposed technique performs very well, with recognizing accuracy, precision and RTE of nearly 1 (i.e. 100 per cent), recall greater than 0.8 and RAE about 1–1.3. When the signal is moderately corrupted by noise, the proposed technique can hold recognizing accuracy about 0.9, recognizing precision nearly to 1, RTE about 0.9, recall around 0.6 and RAE about 1.5. Applications of the proposed technique to real microseismic data induced from hydraulic fracturing and reflection seismic data demonstrate its feasibility and encouraging prospect.