Simultaneous inversion of multiple microseismic data for event locations and velocity model with Bayesian inference

Abstract

We have applied Bayesian inference for simultaneous inversion of multiple microseismic data to obtain event locations along with the subsurface velocity model. The traditional method of using a predetermined velocity model for event location may be subject to large uncertainties, particularly if the prior velocity model is poor. Our study indicated that microseismic data can help to construct the velocity model, which is usually a major source of uncertainty in microseismic event locations. The simultaneous inversion eliminates the requirement for an accurate predetermined velocity model in microseismic event location estimation. We estimate the posterior probability density of the velocity model and microseismic event locations with the maximum a posteriori estimation, and the posterior covariance approximation under the Gaussian assumption. This provides an efficient and effective way to quantify the uncertainty of the microseismic location estimation and capture the correlation between the velocity model and microseismic event locations. We have developed successful applications on both synthetic examples and real data from the Newberry enhanced geothermal system. Comparisons with location results based on a traditional predetermined velocity model method demonstrated that we can construct a reliable effective velocity model using only microseismic data and determine microseismic event locations without prior knowledge of the velocity model.

Microseismic hydraulic fracture imaging in the Marcellus Shale using head waves

“…The velocity model calibration and microseismic event location estimation was conducted with a microseismic event location program we previously developed (Zhang et al, 2017). It aims to minimize the misfit between the observations, which include arrival times and polarization directions, and the model predictions of these observations.…”

“…It aims to minimize the misfit between the observations, which include arrival times and polarization directions, and the model predictions of these observations. An objective function is minimized iteratively with a Gauss-Newton method (Zhang et al, 2017). The standard deviation of arrival-time picking uncertainties is assumed to be 1 ms for all phases, and P-wave polarization uncertainty is assumed to be 6°.…”

Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

“…They are also known as picking‐ or ray‐based methods (Z. Li & van der Baan, ; Pesicek et al, ). Many modifications have been introduced to improve the performance of the traveltime‐based methods, most of which lie in the construction of misfit function and inversion strategies, such as joint hypocenter location method (e.g., Douglas, ; Pujol, ), relative location method (e.g., Fitch, ; Grechka et al, ; Spence, ), plain grid search (e.g., Eisner et al, ), master‐station or station‐pair double difference method (e.g., Font et al, ; Zhang et al, ; Zhou, ), joint location and velocity inversion (e.g., Block et al, ; Jansky et al, ; Z. Zhang et al, ; Diekmann et al, ), double difference relocation method (Waldhauser & Ellsworth, ; H. Zhang & Thurber, ; Hauksson & Shearer, ; Waldhauser & Schaff, ; Kwiatek et al, ), and cluster‐based relocation methods (G. Lin et al, ; P. Shearer et al, ; Matoza et al, ; Trugman & Shearer, ).…”

“…Li & van der Baan, 2016;Pesicek et al, 2014). Many modifications have been introduced to improve the performance of the traveltime-based methods, most of which lie in the construction of misfit function and inversion strategies, such as joint hypocenter location method (e.g., Douglas, 1967;Pujol, 1992), relative location method (e.g., Fitch, 1975;Grechka et al, 2015;Spence, 1980), plain grid search (e.g., , masterstation or station-pair double difference method (e.g., Font et al, 2004;Zhang et al, 2010;Zhou, 1994), joint location and velocity inversion (e.g., Block et al, 1994;Jansky et al, 2010;Z. Zhang et al, 2017;Diekmann et al, 2019), double difference relocation method (Waldhauser & Ellsworth, 2000; H. Zhang Figure 2.…”

Simultaneous Inversion for Microseismic Event Location and Velocity Model in Vaca Muerta Formation

“…Even though usually suffering from difficulties in choosing prior Manuscript received by the Editor 5 January 2017; revised manuscript received 10 October 2017; published ahead of production 24 January 2018; published online 13 April 2018. probability and intensive computational effort, it has been widely used in subsurface inverse problems (Tarantola and Valette, 1982;Tarantola, 2005;Myers et al, 2007Myers et al, , 2009Poliannikov et al, 2013Poliannikov et al, , 2014Zhang et al, 2014). We have developed a Bayesian inference framework for simultaneous inversion and successfully applied it to a data set acquired from shallow borehole stations in the Newberry enhanced geothermal system (EGS) (Zhang et al, 2017). Studies show that a velocity model can be constructed with relatively high confidence using microseismic data only.…”