Microseismic source locations with deconvolution migration

Wu, Shaojiang; Wang, Yibo; Zheng, Yikang; Chang, Xu

doi:10.1093/gji/ggx518

Cited by 31 publications

(8 citation statements)

References 37 publications

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“…Schuster et al [34] discussed the calculation methods of seismic interferometry and its potential application fields, such as structural imaging and source location. Artman et al [35], Witten and Shragge [36], and Wu et al [37] determined source location by employing cross-correlation interferometry, convolution interferometry and deconvolution interferometry. Furthermore, other interferometry techniques, such as weighted-elastic-wave interferometric imaging [38], isotime point [39], and weighted deconvolution imaging [40] have been introduced to enhance location imaging quality of seismic interferometry.…”

Section: Location Methods Based On Seismic Interferometrymentioning

confidence: 99%

Locating Mine Microseismic Events in a 3D Velocity Model through the Gaussian Beam Reverse-Time Migration Technique

Wang

Shang

Peng

2020

Sensors

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Microseismic (MS) source location is a fundamental and critical task in mine MS monitoring. The traditional ray tracing-based location method can be easily affected by many factors, such as multi-ray path effects, waveform focusing and defocusing of wavefield propagation, and low picking precision of seismic phase arrival. By contrast, the Gaussian beam reverse-time migration (GBRTM) location method can effectively and correctly model the influences of multi-path effects and wavefield focusing and defocusing in complex 3D media, and it takes advantages of the maximum energy focusing point as the source location with the autocorrelation imaging condition, which drastically reduces the requirements of signal-to-noise ratio (SNR) and picking accuracy of P-wave arrival. The Gaussian beam technique has been successfully applied in locating natural earthquake events and hydraulic fracturing-induced MS events in one-dimensional (1D) or simple two-dimensional (2D) velocity models. The novelty of this study is that we attempted to introduce the GBRTM technique into a mine MS event location application and considered utilizing a high-resolution tomographic 3D velocity model for wavefield back propagation. Firstly, in the synthetic test, the GBRTM location results using the correct 2D velocity model and different homogeneous velocity models are compared to show the importance of velocity model accuracy. Then, it was applied and verified by eight location premeasured blasting events. The synthetic results show that the spectrum characteristics of the recorded blasting waveforms are more complicated than those generated by the ideal Ricker wavelet, which provides a pragmatic way to evaluate the effectiveness and robustness of the MS event location method. The GBRTM location method does not need a highly accurate picking of phase arrival, just a simple detection criterion that the first arrival waveform can meet the windowing requirements of wavefield back propagation, which is beneficial for highly accurate and automatic MS event location. The GBRTM location accuracy using an appropriate 3D velocity model is much higher than that of using a homogeneous or 1D velocity model, emphasizing that a high-resolution velocity model is very critical to the GBRTM location method. The average location error of the GBRTM location method for the eight blasting events is just 17.0 m, which is better than that of the ray tracing method using the same 3D velocity model (26.2 m).

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Section: Location Methods Based On Seismic Interferometrymentioning

confidence: 99%

Locating Mine Microseismic Events in a 3D Velocity Model through the Gaussian Beam Reverse-Time Migration Technique

Wang

Shang

Peng

2020

Sensors

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“…Here, Ri(x;t;boldxbolds) is the receiver wavefield, which is approximated using a finite-difference solution of the wave equation [68,70,71]. For microseismic imaging, under the virtual source assumption, the source wavefield is eliminated by seismic interferometry using receiver wavefields [60,61,72].…”

Section: System Prototype Design Examplesmentioning

confidence: 99%

“…With a given velocity model, the time-reversed extrapolation of the observed wavefields can be calculated based on wave equations. The extrapolated wavefields from different receivers stack together to enlighten the location of seismic sources 74 , as illustrated in Figure 6. MMI [74][75][76][77][78][79][80] typically uses body waves and has two main steps: forward modeling and stacking (also called imaging condition) in processing and computing layer, respectively.…”

Section: Migration-based Microseismic Imagingmentioning

confidence: 99%

“…The extrapolated wavefields from different receivers stack together to enlighten the location of seismic sources 74 , as illustrated in Figure 6. MMI [74][75][76][77][78][79][80] typically uses body waves and has two main steps: forward modeling and stacking (also called imaging condition) in processing and computing layer, respectively.…”

Section: Migration-based Microseismic Imagingmentioning

confidence: 99%

See 1 more Smart Citation

Toward Creating a Subsurface Camera

Song

Valero

et al. 2019

Sensors

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In this article, the framework and architecture of a Subsurface Camera (SAMERA) are envisioned and described for the first time. A SAMERA is a geophysical sensor network that senses and processes geophysical sensor signals and computes a 3D subsurface image in situ in real time. The basic mechanism is geophysical waves propagating/reflected/refracted through subsurface enter a network of geophysical sensors, where a 2D or 3D image is computed and recorded; control software may be connected to this network to allow view of the 2D/3D image and adjustment of settings such as resolution, filter, regularization, and other algorithm parameters. System prototypes based on seismic imaging have been designed. SAMERA technology is envisioned as a game changer to transform many subsurface survey and monitoring applications, including oil/gas exploration and production, subsurface infrastructures and homeland security, wastewater and CO2 sequestration, and earthquake and volcano hazard monitoring. System prototypes for seismic imaging have been built. Creating SAMERA requires interdisciplinary collaboration and the transformation of sensor networks, signal processing, distributed computing, and geophysical imaging.

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“…For elastic waves, Haldorsen et al (2013) proposed the deconvolution imaging condition and Zhang and Zhang (2013) developed the crosscomponent cross-correlation imaging condition to extract the coherence of P-and Swaves. The second type of techniques is reverse time imaging (RTI) approaches (McMechan, 1982;Gajewski and Tessmer, 2005;Artman et al, 2010;Witten and Artman, 2012;Fish, 2012;Wu et al, 2017). Gajewski and Tessmer (2005) showed that the energy could be refocused by backward propagating the wavefields (McMechan, 1982).…”

Section: Introductionmentioning

confidence: 99%