Seismic model low wavenumber extrapolation by a deep convolutional neural network

Plotnitskii, Pavel; Alkhalifah, Tariq; Ovcharenko, Oleg; Kazei, Vladimir

doi:10.1080/22020586.2019.12073206

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Here, we focus on initial velocity model building, which might be approached in data and model domain. The model-domain assumes prediction of a low-wavenumber velocity model directly from the data (Kazei et al, 2020b,a;Zwartjes, 2020;Plotnitskii et al, 2019). The data-domain approaches focus on extrapolation of the low-frequency content of seismic data, which then might be used by a classic imaging algorithm (Aharchaou et al, 2020;Ovcharenko et al, 2017Ovcharenko et al, , 2019Ovcharenko and Hou, 2020;Fabien-Ouellet, 2020;Hu et al, 2020;Wang et al, 2020;Demanet, 2019, 2020).…”

Section: Introductionmentioning

confidence: 99%

Dual-band generative learning for low-frequency extrapolation in seismic land data

Ovcharenko

Kazei

Peter

et al. 2021

First International Meeting for Applied Geoscience &Amp; Energy Expanded Abstracts

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The presence of low-frequency energy in seismic data is one of the desirable prerequisite for a successful full-waveform inversion. However, it requires costly machinery and operations to acquire such data. Extrapolation for the missing lowfrequency content in field data might be addressed in a datadriven framework. In particular, the deep learning models are commonly trained for this purpose on synthetic data and then used for inference on field data. However, it is always a challenge to switch the application domains. We propose the concept of generative dual-band learning to facilitate the knowledge transfer between synthetic and field seismic data applications of low-frequency data extrapolation. We first explain the two-step procedure for training of a generative adversarial network (GAN) that extrapolates low frequencies. Then we describe the workflow for synthetic dataset generation. Finally, we explore the feasibility of the dual-band learning concept on real near-surface land data acquired in Saudi Arabia.

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Section: Introductionmentioning

confidence: 99%

Dual-band generative learning for low-frequency extrapolation in seismic land data

Ovcharenko

Kazei

Peter

et al. 2021

First International Meeting for Applied Geoscience &Amp; Energy Expanded Abstracts

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“…Recently, deep learning (DL) models have been utilized in many geophysical applications such as seismic data pre-processing (e.g., Ovcharenko et al, 2017Ovcharenko et al, , 2019Kazei et al, 2019a;Sun and Demanet, 2019), and inversion (e.g., Araya-Polo et al, 2018;Sun and Alkhalifah, 2019;Plotnitskii et al, 2019;Kazei et al, 2019bKazei et al, , 2020Sun and Alkhalifah, 2020a,b). In the context of time-lapse, Yuan et al (2020) used a convolution neural network (CNN) to image the velocity changes in different vintages.…”

Section: Introductionmentioning

confidence: 99%

Cross-equalization of time-lapse seismic data using recurrent neural networks

Alali

Kazei

Sun

et al. 2020

SEG Technical Program Expanded Abstracts 2020

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Time-lapse seismic uses repetitive seismic surveys to monitor the fluid in the subsurface. Ideally, the time-lapse data should be identical except for at the target region (i.e., the reservoir), where the fluid changes occur. Unfortunately, it is almost impossible to have identical data for various reasons, such as the static changes in the near-surface or the varying positioning of sources and receivers between surveys. To increase the accuracy of the 4D signal and reduce the noise, we propose to process the time-lapse data using a machine-learning methodology. Specifically, we train a recurrent neural network (RNN) model to map the data from monitor to baseline. The learned RNN model would reveal 4D overburden changes. Therefore, the difference between the predicted baseline and the actual baseline data stets will represent the target signal. We validate the method on synthetic data and show the improvements of the 4D signal by imaging the reservoir and computing the normalized root mean square.

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Multi-scale seismic envelope inversion method based on sparse representation theory

Yang

Shan

et al. 2022

Journal of Applied Geophysics

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Seismic model low wavenumber extrapolation by a deep convolutional neural network

Cited by 5 publications

References 20 publications

Dual-band generative learning for low-frequency extrapolation in seismic land data

Dual-band generative learning for low-frequency extrapolation in seismic land data

Cross-equalization of time-lapse seismic data using recurrent neural networks

Multi-scale seismic envelope inversion method based on sparse representation theory

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