Strain-based forward modeling and inversion of seismic moment tensors using distributed acoustic sensing (DAS) observations - Lecoulant, Jean; Ma, Yuanyuan; Dettmer, Jan; Eaton, David (Frontiers in Earth Science, 2023)
This study uses waveform inversion of distributed acoustic sensing (DAS) data, acquired in two horizontal monitoring wells, to estimate the moment tensor (MT) of two induced microearthquakes. An analytical forward model is developed to simulate far-field tangential strain generated by a MT-source in a homogeneous and anisotropic medium, averaged over the gauge length along a fibre of arbitrary orientation. To prepare the data for inversion, secondary scattered waves are removed from the field observations, using f-k filtering and time-windowing. Modelled and observed primary arrivals are aligned using a cut-and-paste approach. The MT parameters are inverted via a least-square approach and their uncertainties are determined through bootstrap analysis. Using simulated data with additive noise derived from the field data and the same fibre configuration as the monitoring wells, the inversion method adequately resolves the MT. Despite the assumption of Gaussian noise, which underlies the least-squares inversion approach, the method is robust in the presence of heavy-tailed noise observed in field data. When the inversion is applied to field data, independent inversion results using P waves, S waves and both waves together yields results that are consistent between the two events and for different wave types. The agreement of the inversion results for two events resulting from the same stress field illustrates the reliability of the method. The uncertainties of the MT-parameters are small enough to make the inversion method useful for geophysical interpretation. The variance reduction obtained from the data predicted for the most probable MT is satisfying, even though the polarity of P waves is not always correctly reproduced.