Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition

Abstract

Reliable detection and recovery of a microseismic event in large volume of passive monitoring data is usually a challenging task due to the low signal-to-noise ratio environment. The accuracy of weak microseismic event identification is a very important step in the analysis and interpretation of microseismic data. This paper introduces an approach for detecting (presence indication) and denoising (accurate recovery) microseismic events using tensor decomposition by considering the time-frequency representation of multiple traces as a 3-D tensor. A tensor is a multiway array having dimension greater than two, and recent signal processing techniques have been developed to manipulate such data by taking advantage of the multidimensional structure. With advances in technology and the availability of cheap memory, it is now possible to store and do mathematical operations, such as higher order singular-value decomposition or tensor decomposition, on multiway data. In active seismic, tensor decomposition has been used for multidimensional reconstruction via higher order interpolation to obtain missing observations. In this paper, we use 3-D tensor decomposition to process passive seismic data. Experiments performed on synthetic and field data sets show promising results achieved by these new methods.