The Synchrosqueezing Transform for High-resolution Time-frequency Representation of Microseismic Recordings

Abstract

The analysis of the frequency content over time is essential to identify resonance frequencies. The Short-Time Fourier Transform (STFT), based on the Fourier spectra, is the most common transform despite its familiar problems: spectral leakage and signal windowing. The resulting noise can make the identification of a particular frequency component difficult. A new transform called synchrosqueezing (SST) was developed to improve the accuracy of wavelet-based transforms for frequency content estimation, by using the reassignment method which collapse averaged values to their center of gravity. When challenged by a synthetic signal with sharp onsets and a microseismic dataset exhibiting resonance frequencies, the SST is able to detect all frequency components with a better precision than the STFT. In fact, some hidden spectral lines on the STFT representation are identifiable on the SST representation. An accurate determination of the time-frequency content of microseismic recordings is indispensable to separate the different resonance frequencies. The study of these resonances may bring new information on the deformation of the reservoir during hydraulic fracturing experiments.