Microseismic event estimation and velocity analysis based on a source-focusing function

Song, Chao; Alkhalifah, Tariq; Wu, Zedong

doi:10.1190/geo2018-0205.1

Cited by 29 publications

(13 citation statements)

References 24 publications

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“…After obtaining the reverse double-difference time, we need to select an appropriate imaging condition for imaging. A reasonable imaging condition helps to obtain a highresolution source image and improve the accuracy of source localization [23][24][25][26][27]. To obtain more accurate location results, the imaging condition should make the difference of the imaging value between the seismic source and the nonsource as large as possible.…”

Section: A Calculation Of Reverse Double-difference Timementioning

confidence: 99%

Microseismic Source Location Estimation Using Reverse Double-Difference Time Imaging

Feng¹,

Pan²,

Han³

et al. 2021

IEEE Access

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To locate microseismic sources, the reverse double-difference time imaging method is proposed in this paper. The traditional time-reversal imaging method requires an accurate velocity model. To reduce the influence of the velocity model errors, this method uses the reverse travel time difference from adjacent receivers to each grid point of the velocity model and the arrival time difference of the microseismic event for imaging. We propose two imaging conditions for single source imaging and multisource imaging to obtain high-resolution images of the sources. These imaging conditions are robust and can eliminate the error caused by outliers in the reverse double-difference time. The k-means clustering algorithm is used to optimize positioning results. Numerical experiments for source location estimation demonstrate that the reverse double-difference time imaging method is stable and reliable, and can locate multiple seismic sources with high-precision when the velocity model has errors. Some factors affecting the accuracy of source location estimation in the reverse double-difference time imaging are tested using 2D numerical experiments.

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Section: A Calculation Of Reverse Double-difference Timementioning

confidence: 99%

Microseismic Source Location Estimation Using Reverse Double-Difference Time Imaging

Feng¹,

Pan²,

Han³

et al. 2021

IEEE Access

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“…These approaches do not require assumptions about the number or the nature of the events, but require a fairly accurate background velocity model. A few studies have been presented to jointly locate the events and update the subsurface velocity model (Song et al, 2019;Sun et al, 2016). Sun et al (2016) update the source and the velocity model in an alternating manner.…”

Section: Introductionmentioning

confidence: 99%

Accurate and efficient data-assimilated wavefield reconstruction in the time domain

2020

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Wavefield reconstruction inversion (WRI) mitigates cycle skipping in full-waveform inversion by computing wavefields that do not exactly satisfy the wave equation to match data with inaccurate velocity models. We refer to these wavefields as data assimilated wavefields because they are obtained by combining the physics of wave propagation and the observations. Then, the velocity model is updated by minimizing the wave-equation errors, namely, the source residuals. Computing these data-assimilated wavefields in the time domain with explicit time stepping is challenging. This is because the right-hand side of the wave equation to be solved depends on the back-propagated residuals between the data and the unknown wavefields. To bypass this issue, a previously proposed approximation replaces these residuals by those between the data and the exact solution of the wave equation. This approximation is questionable during the early WRI iterations when the wavefields computed with and without data assimilation differ significantly. We have developed a simple backward-forward time-stepping recursion to refine the accuracy of the data-assimilated wavefields. Each iteration requires us to solve one backward and one forward problem, the former being used to update the right side of the latter. An application to the BP salt model indicates that a few iterations are enough to reconstruct data-assimilated wavefields accurately with a crude velocity model. Although this backward-forward recursion leads to increased computational overheads during one WRI iteration, it preserves its capability to extend the search space.

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“…All imaging methods using TRI to locate microseismic sources su↵er from a misfit between the true velocity model and the one used for the back-propagation, which may lead to misfocused spatial images. Some techniques, such as migration velocity analysis (MVA) Shragge, 2015, 2017;Song et al, 2019a), can be introduced to improve the TRI robustness.…”

Section: Introductionmentioning

confidence: 99%

Regularized elastic passive equivalent source inversion with full-waveform inversion: Application to a field monitoring microseismic data set

et al. 2020

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One of the key goals of microseismic processing is the accurate estimation of the source location. Applying full waveform information on passive-source datasets can potentially delineate microseismic sources. The accuracy of both P- and S- wave velocities has a strong influence on the estimation of source locations and hence the reliability of the fracture detection. We propose a methodology for passive source and velocity inversion, in which the conventional source term of the elastic wave equation is represented by an equivalent source. The equivalent source term is composed of source images and source functions, as it is inspired by elastic reflection waveform inversion. Thus, we update the source locations, source functions and velocities simultaneously by using a waveform inversion scheme. In the 2D isotropic case, the source terms are defined by two source image components and three source function components. They provide an alternative source representation of its mechanism, usually defined by the moment tensor. Waveform inversion of passive events has severe nonlinearity due to the unknown source locations in space and their functions in time. We, thus, use a source-independent objective function, based on convolving reference traces with both modeled and observed data, to avoid cycle skipping caused by the unknown sources. We first synthetically test our method on a modified Marmousi model. Then, by applying a nested inversion for these variables, the proposed method also produces good estimation of the source and background velocity for real microseismic monitoring data. We use a ball-drop event to test the accuracy as the inverted source location should match the ball-seat location. For the uncontrolled events, the estimated source distribution using waveform inversion agrees with the local stress potential information. Though the proposed method has higher computational cost than traveltime or migration based methods, the estimated event locations have significantly improved accuracy.

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Microseismic event estimation and velocity analysis based on a source-focusing function

Cited by 29 publications

References 24 publications

Microseismic Source Location Estimation Using Reverse Double-Difference Time Imaging

Microseismic Source Location Estimation Using Reverse Double-Difference Time Imaging

Accurate and efficient data-assimilated wavefield reconstruction in the time domain

Regularized elastic passive equivalent source inversion with full-waveform inversion: Application to a field monitoring microseismic data set

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