Extended full waveform inversion with matching filter

Li, Yuanyuan; Alkhalifah, Tariq

doi:10.1111/1365-2478.13121

Cited by 15 publications

(3 citation statements)

References 48 publications

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“…Wolfcamp interval) because there is no influence from other components, as was the case in the P‐wave acoustic FWI or the PS‐wave FWI (Masmoudi et al., 2021). Creating a matching filter between the predicted and observed data, which has the potential of improving the data amplitude matching and the subsequent FWI results, is not implemented as this approach requires an extensive testing of regularization parameter (Li & Alkhalifah, 2021) and offset range selection (Kalinicheva, 2020) during the matching process.…”

Section: Discussionmentioning

confidence: 99%

Two‐dimensional SH‐wave and acoustic P‐wave full waveform inversion: A Midland Basin case study

Liu,

Simmons

2024

Geophysical Prospecting

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Building a reliable S‐wave velocity model remains challenging from P‐wave land seismic data as multi‐parameter elastic full waveform inversion which updates P‐ and S‐wave velocities simultaneously is not yet widely adopted due to its computational cost, highly nonlinear nature and noise contamination from land seismic data. To overcome this challenge, we propose implementing the SH‐wave full waveform inversion to obtain the S‐wave velocity model. The value of this approach has been examined by applying two‐dimensional time‐domain SH‐ and P‐wave acoustic full waveform inversion to a nine‐component three‐dimensional seismic survey acquired in the Midland Basin. The nine‐component seismic survey was acquired by using both vertical and horizontal component vibrators and receivers, which enables us to apply both SH‐ and P‐wave full waveform inversion to the raw and preprocessed shot gathers along a two‐dimensional line. The SH‐ and P‐wave full waveform inversion applied to the raw shot gathers provide more detailed P‐ and S‐wave velocities compared to the velocities from the stacking velocity analysis. Additionally, there are no problematic artefacts in the inverted S‐wave velocity model, which shows the stability of the SH‐wave inversion. Through the SH‐wave full waveform inversion from the preprocessed shot gathers, we reveal the lateral velocity variations in the Grayburg–San Andres interval, which coincides with the depositional environment changes suggested by the previous regional study mainly based on well log and cores. These variations demonstrate that SH‐wave full waveform inversion can identify additional geological features that are not observed in the inverted P‐wave velocity model. The inverted P‐ and SH‐wave velocities are validated by the flattening of the events observed in the common image gathers after Kirchhoff depth migration. The SH‐wave migration image further reveals the irregular geometries of carbonates in the Spraberry formation. Vp/Vs ratios calculated from the independently inverted P‐ and S‐wave velocity models show strong lateral variations in the Wolfcamp interval (key‐producing interval), which could be caused by the organic content variations between the reservoir and non‐reservoir rocks. The inversion results demonstrate that the SH‐wave full waveform inversion can be used to provide an S‐wave velocity model that is comparable to the P‐wave velocity model derived from acoustic full waveform inversion, which is widely used for P‐wave velocity model building from P‐wave land seismic data.

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Section: Discussionmentioning

confidence: 99%

Two‐dimensional SH‐wave and acoustic P‐wave full waveform inversion: A Midland Basin case study

Liu,

Simmons

2024

Geophysical Prospecting

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“…Considering the good inversion effect of the end‐to‐end inversion network, the idea of inverting observed data in a network parameter domain can be introduced into the process of conventional FWI. By transforming the search space from velocity models to the network weights, FWI results could also benefit from the additional degrees of freedom and the network regularization in the optimization process (Y. Li & Alkhalifah, 2021; Symes, 2008). For example, Wu and McMechan (2019) propose a CNN domain FWI and the features of the initial model extracted by CNN during the pre‐training play a vital role in regularizing the inversion results.…”

Section: Introductionmentioning

confidence: 99%

Full waveform inversion based on inversion network reparameterized velocity

Jiang

Wang

Ren

et al. 2022

Geophysical Prospecting

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Seismic velocity plays an important role in imaging and identifying underground geology. Conventional seismic velocity inversion methods, like full waveform inversion, directly update the velocity model based on the misfit between the observed and synthetic data. However, seismic velocity inversion is a highly nonlinear process, and the inversion effect greatly relies on the initial inversion model. In this paper, we propose a novel network‐domain full waveform inversion method. Different from the existing network‐domain full waveform inversion methods, which use random or fixed numbers as network input, we reparameterize the low‐dimensional acoustic velocity model in a high‐dimensional inversion network parameter domain with seismic observed data as the network input. In this way, the physical information within the observed data can be directly encoded into the inversion parameters, leading to a better inversion effect than the current network‐domain full waveform inversion method. Moreover, comparison experiments on the Society of Exploration Geophysicists and the European Association of Geoscientists and Engineers Overthrust model and the Marmousi model show the advantages of the proposed method over conventional full waveform inversion from the aspects of inversion accuracy, robustness to noisy data, and more complex geological structures. These advantages may benefit from the fact that reparameterization within the inversion network domain can empower the inversion process with the regularization ability of denoising and mitigating the cycle‐skipping issue. In the end, the potential of the proposed method in terms of network initialization is further discussed.

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“…In addition, the misfit functions based on the optimal transport provide an alternative in an attempt to overcome the cycle skipping issue (Engquist and Froese, 2013;Métivier et al, 2016;Yang and Engquist, 2018). Recently, the new methods related to non-physical model extensions, such as reconstructed wavefields (van Leeuwen and Herrmann, 2013;Wang et al, 2016;Alkhalifah and Song, 2019), time lag extension (Yang and Sava, 2013;Biondi and Almomin, 2014), and matching filter (Luo and Sava, 2011;Warner and Guasch, 2016;Huang et al, 2017;Sun and Alkhalifah, 2019;Li and Alkhalifah, 2021) are also attractive for their stronger resistance to cycle skipping.…”

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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Extended full waveform inversion with matching filter

Cited by 15 publications

References 48 publications

Two‐dimensional SH‐wave and acoustic P‐wave full waveform inversion: A Midland Basin case study

Two‐dimensional SH‐wave and acoustic P‐wave full waveform inversion: A Midland Basin case study

Full waveform inversion based on inversion network reparameterized velocity

Waveform Energy Focusing Tomography With Passive Seismic Sources

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