3D passive wavefield imaging using the energy norm

Rocha, Daniel Guimarães Pedro; Sava, Paul; Shragge, Jeffrey; Witten, Benjamin

doi:10.1190/geo2018-0251.1

Cited by 19 publications

(3 citation statements)

References 60 publications

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“…The polarity corrections based on amplitude trend least‐squares fitting (Xu et al., 2020) and convolutional neural network determination (Tian et al., 2020) were also applied to surface microseismic data. In the latter case, the imaging conditions for TRI mainly contained integral imaging, PS cross‐correlation imaging (Artman et al., 2010), interferometric imaging (Li et al., 2014; Wang et al., 2013; Zhang & Zhang, 2022), the PS interferometric cross‐correlation imaging (Zhou & Zhang, 2017; Zhou et al., 2022), energy imaging (Oren & Shragge, 2019; Rocha et al., 2019), and the geometric‐mean imaging (Lyu & Nakata, 2020; Nakata & Beroza, 2015).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Efficient Microseismic Location Using Source Migration‐Based Imaging

Zhang

et al. 2022

JGR Solid Earth

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Migration‐based location methods (e.g., time‐reverse imaging based on wave equation, Kirchhoff summation, and diffraction stacking) can effectively locate events of low signal‐to‐noise ratios by stacking waveforms from many receivers. The methods have been widely applied for surface microseismic monitoring. However, these methods may not produce accurate results if there are polarity reversals in the surface records for a double‐couple or even a general moment tensor event. Various imaging conditions have been developed to solve the non‐focus image problem for a non‐explosive source. Here, we propose a deep convolutional neural network to predict a better‐focused image from a regular migration image that contains a quasi‐symmetric pattern in both space and time. To train the network, we first simulate a large number of surface records from sources with various locations and mechanisms. We then compute diffraction stacking images from the records and take the images as the input to the network. We define the corresponding training labels as images (with the same size as the input) with Gaussian distributions centered at the true sources. This network, trained by only synthetic datasets, works well in field data to detect source locations from images for unknown events. Both synthetic tests and field data applications demonstrate that the proposed method can effectively improve diffraction stacking images for efficient microseismic location.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Efficient Microseismic Location Using Source Migration‐Based Imaging

Zhang

et al. 2022

JGR Solid Earth

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“…However, these methods failed to consider the influence of the source radiation pattern, and corresponding FWI schemes still have high nonlinearity. By focusing different modes (PP, SS and PS) of the source images, a variety of methods for determining source locations as well as velocity models (Vp, Vs.) (Witten and Shragge, 2017;Rocha et al, 2019;Oren and Shragge, 2021;Oren and Shragge, 2022) have been developed using different imaging conditions. Since modeling elastic wavefields (both P-and S wave) is necessary for these methods, the computational cost is rather demanding, especially for 3D cases.…”

Section: Introductionmentioning

confidence: 99%

Waveform Energy Focusing Tomography With Passive Seismic Sources

Zhang

2022

Front. Earth Sci.

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By taking advantage of the information carried by the entire seismic wavefield, Full Waveform Inversion (FWI) is able to yield higher resolution subsurface velocity models than seismic traveltime tomography. However, FWI heavily relies on the knowledge of source information and good initial models, and could be easily trapped into local minima caused by cycle skipping issue because of its high nonlinearity. To mitigate these issues in FWI, we propose a novel method called Waveform Energy Focusing Tomography (WEFT) for passive seismic sources. Unlike conventional FWI, WEFT back-propagates the seismic records directly instead of the data residuals, and updates the velocity models by maximizing the stacking energy for all the moment tensor components from back-propagated wavefields around the sources. Therefore, except for source locations and origin times, WEFT does not require other source attributes in advance for the inversion. Since WEFT does not aim at fitting synthetic and observed waveforms, it has lower nonlinearity and is less prone to the cycle skipping issue compared to FWI. For the proof of concept, we have validated WEFT using several 2D synthetic tests to show it is less affected by inaccurate source locations and data noise. These advantages render WEFT more applicable for tomography using passive seismic sources when the source information is generally not accurately known. Although the inverted model from WEFT is inevitably influenced by the source distribution as well as its radiation patterns, and its resolution is likely lower than that of FWI, it can act as an intermediate step between traveltime tomography and FWI by providing a more reliable and accurate velocity model for the latter.

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“…Time-reversal-based methods [7]- [10] propagate the recoded data backward (reverse time) to reconstruct the wavefield using the wave equation. With an appropriate imaging condition, a source image can be obtained [11], [12]. This category of methods highly depends on the accuracy of the velocity model.…”

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confidence: 99%