Application of the Haar wavelet transform to detect microseismic signal arrivals

Abstract

A discrete wavelet transform is applied for the time-scale representation of raw seismic data and subsequent identification of events of interest. The wavelet transform properties such as localization, which is essential for the analysis of transient signals, provide a filter to extract characteristics of interest such as energy and predominant time scales. This information is subsequently exploited for microseismic events detection. The sample sum of squares is partitioned on a scale-by-scale basis and analysed across the time scales to emphasise the signal phase arrival, retaining the scales at which the seismic events have larger energy. The orthonormal decomposition of the signal energy estimated by the wavelet variance into the retained scales provides a useful means of describing the change in the signal magnitude associated with the triggering events. This type of analysis discriminates between signal phase arrival and spurious signal triggering by the different magnitude of local relative energy, which is much smaller in the latter case. The relative energy across the scales also changes, with greater magnitudes at coarser resolutions in the pattern expected in a trace decomposition with only a random noise component.