The separation of seismic surface-related multiples based on CAE-SAGAN

Tao, Liurong; Jiang, Jinsheng; Ren, Hongliang; Ye, Yueming; Wu, Bangyu

doi:10.1190/segam2021-3590505.1

Cited by 3 publications

(1 citation statement)

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“…Siahkoohi et al (2018) utilize CNNs to remove the free-surface multiples and numerical dispersion; Das et al (2019) use CNNs to obtain an elastic subsurface model using recorded normal-incidence seismic data; Wu et al (2019) use CNNs for three-dimensional seismic fault segmentation; Almuteri and Sava (2021) use CNNs to address to ghost removal from seismic data; Kiraz and Snieder (2022) utilize CNNs for one-dimensional (1D) wavefield focusing where the solution of the Marchenko equation is not needed to retrieve the Green's function once the network is trained. Recently, CNNs have been used to tackle free-surface multiples in various ways (Siahkoohi et al, 2018(Siahkoohi et al, , 2019Ovcharenko et al, 2021;Zhang et al, 2021;Liu-Rong et al, 2021). In this paper, we use a trace-by-trace CNN approach because training and prediction take shorter than higher dimensional CNN approaches.…”

Section: Convolutional Neural Network Architecture and Trainingmentioning

confidence: 99%

Attenuating free‐surface multiples and ghost reflection from seismic data using a trace‐by‐trace convolutional neural network approach

Kiraz,

Snieder,

Sheiman

2023

Geophysical Prospecting

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The presence of the air‐water interface (or free‐surface) creates two major problems in marine seismic data for conventional seismic processing and imaging: free‐surface multiples and ghost reflections. The attenuation of free‐surface multiples remains one of the most challenging noise attenuation problems in seismic data processing. Current solutions suffer from the removal of the primary events along with the multiple events especially when the primary and multiple events overlap (e.g., adaptive subtraction). The effective attenuation of ghost reflections (or deghosting) requires acquisition‐ and/or processing‐related solutions which generally address the source‐side and receiver‐side ghosts separately. Additionally, an essential requirement for a successful implementation of free‐surface multiple attenuation and seismic dehosting is the requirement of dense seismic data acquisition parameters which is not realistic for 2D and/or 3D marine cases. We present a Convolutional Neural Network (CNN) approach for free‐surface multiple attenuation and seismic deghosting. Unlike the existing solutions, our approach operates on a single trace at a time, and neither relies on the dense acquisition parameters nor requires a subtraction process to eliminate free‐surface multiples, and it removes both the source ghost and receiver ghost simultaneously. We train a network using subsets of the Marmousi and Pluto velocity models, and make predictions using subsets of the Sigsbee velocity model. We show that the CNN predictions give a correlation coefficient of 0.97 on average with the numerically modeled data for the synthetic examples. We illustrate the efficacy of our CNN‐based technique using the Mobil AVO Viking Graben field data set. The application of our algorithm demonstrates that our CNN‐based approach removes different orders of free‐surface multiples (e.g., 1 and 2 orders) and recovers the low‐frequency content of the seismic data (which is essential for, for instance, full‐waveform inversion applications and broadband processing) by successfully removing the ghost reflections while preserving and increasing the continuity of the primary reflections.This article is protected by copyright. All rights reserved

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Section: Convolutional Neural Network Architecture and Trainingmentioning

confidence: 99%

Attenuating free‐surface multiples and ghost reflection from seismic data using a trace‐by‐trace convolutional neural network approach

Kiraz,

Snieder,

Sheiman

2023

Geophysical Prospecting

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Generative adversarial networks review in earthquake-related engineering fields

Marano

Rosso

Aloisio

et al. 2023

Bull Earthquake Eng

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Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples’ characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions.

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