Deep Learning Tomography by Mapping Full Seismic Waveforms to Vertical Velocity Profiles

Kazei, Vladimir; Ovcharenko, Oleg; Plotnitskii, Pavel; Peter, Daniel; Zhang, X.; Alkhalifah, Tariq

doi:10.3997/2214-4609.202011980

Cited by 8 publications

(2 citation statements)

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“…In the age of digital transformation, deep learning models have been used widely in many seismic applications such as data processing (Ovcharenko et al, 2019;Kazei et al, 2019), modeling (Song et al, 2021), inversion (Araya-Polo et al, 2018;Kazei et al, 2020;Sun and Alkhalifah, 2020) and interpretation (Xiong et al, 2018;Sen et al, 2020). The type of neural network architecture needed depends on the application.…”

Section: Introductionmentioning

confidence: 99%

Time-lapse data matching using a recurrent neural network approach

et al. 2022

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Time-lapse seismic data acquisition is an essential tool to monitor changes in a reservoir due to fluid injection, such as CO2 injection. By acquiring multiple seismic surveys in the exact same location, we can identify the reservoir changes by analyzing the difference in the data. However, such analysis can be skewed by the near-surface seasonal velocity variations, inaccuracy and repeatability in the acquisition parameters, and other inevitable noise. The common practice (cross-equalization) to address this problem uses the part of the data where changes are not expected to design a matching filter and then apply it to the whole data, including the reservoir area. Like cross-equalization, we train a recurrent neural network on parts of the data excluding the reservoir area and then infer the reservoir-related data. The recurrent neural network can learn the time dependency of the data, unlike the matching filter that processes the data based on the local information obtained in the filter window. We demonstrate the method of matching the data in various examples and compare it with the conventional matching filter. Specifically, we start by demonstrating the ability of the approach in matching two traces and then test the method on a pre-stack 2D synthetic data. Then, we verify the enhancements of the 4D signal by providing RTM images. We measure the repeatability using normalized root mean square and predictability metrics and show that in some cases, our proposed method performed better than the matching filter approach.

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Section: Introductionmentioning

confidence: 99%

Time-lapse data matching using a recurrent neural network approach

et al. 2022

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“…Recently, deep learning has been utilized in many geophysical applications including modelling, processing, interpretation, and inversion, overcoming many limitations of the conventional methods (Huang et al, 2022;Song et al, 2021;Liu et al, 2022;Harsuko and Alkhalifah, 2022;AlAli and Anifowose, 2022;Zhou et al, 2020;Xiong et al, 2018;Kazei et al, 2020;Yang and Ma, 2019;Araya-Polo et al, 2018). In salt model building, deep learning contributes through two general approaches.…”

Section: Introductionmentioning

confidence: 99%

Integrating U-net with full-waveform inversion for an efficient salt body construction

Alaliand

Alkhalifah

2022

Second International Meeting for Applied Geoscience &Amp; Energy

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Full waveform inversion (FWI) often faces a lot of difficulty inverting for the subsurface when starting with a poor initial model, especially in complex subsurface regions, such as those containing salt bodies. Thus, human intervention is often needed to pick the salt boundary and improve the starting model for the inversion, which can be erroneous and time consuming. Recently, machine learning has greatly assisted us in interpreting the salt body from seismic images, which helps automate the salt building process. However, such ML algorithms require seismic images, which in turn requires a reasonable good velocity model. Here, we build the salt body using low frequency FWI with the aid of neural networks, specifically U-net. The inversion is implemented in a multi-scale fashion and the networks for flooding and unflooding the salt are applied after each scale. We start the inversion from a constant velocity model using low frequencies of 3-7 Hz. Then, we apply a U-net to improve the inversion and flood the salt. We repeat this process of FWI and flooding in the next frequency bandwidth. In the last frequency bandwidth, we use a U-net for unflooding and impliment a final FWI. The networks were trained in a supervised manner using 1D inverted velocity models. We show the potential of the approach on the center part of BP 2004 salt model.

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Integrating U-Nets Into a Multiscale Full-Waveform Inversion for Salt Body Building

Alali,

Alkhalifah

2023

IEEE Trans. Geosci. Remote Sensing

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Deep Learning Tomography by Mapping Full Seismic Waveforms to Vertical Velocity Profiles

Cited by 8 publications

References 0 publications

Time-lapse data matching using a recurrent neural network approach

Time-lapse data matching using a recurrent neural network approach

Integrating U-net with full-waveform inversion for an efficient salt body construction

Integrating U-Nets Into a Multiscale Full-Waveform Inversion for Salt Body Building

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