Image processing of seismic attributes for automatic fault extraction

Qi, Jie; Lyu, Bin; Alali, Abdulmohsen; Machado, Gabriel; Marfurt, Kurt J.; Hu, Ying

doi:10.1190/segam2018-2997854.1

Cited by 7 publications

(3 citation statements)

References 39 publications

(48 reference statements)

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“…Historically, seismic interpretation analysis are performed on post-stack seismic data and based on a long list of attributes. 219,220 These attributes include amplitude, 221 curvature, 222 gradient, coherence, 223 as well as texture information. A comprehensive review of these attribute-based works is provided by Chopra and Marfurt.…”

Section: A Image Classification For Seismic Interpretationmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

396

126

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Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of statistical techniques for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in five acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, seismic exploration, and environmental sounds in everyday scenes.

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Section: A Image Classification For Seismic Interpretationmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

396

126

View full text Add to dashboard Cite

show abstract

“…In the geoscience community, there is also a boom of research interest in ML and the application of ML algorithms, specifically to seismic exploration. For example, NN are trained to perform facies analysis (Wrona et al, 2018;Zhong et al, 2019;Liu et al, 2018;Qi et al, 2020), seismic event or first arrival picking (Zhu and Beroza, 2018;Qu et al, 2019;Hu et al, 2019a), fault detection (Xiong et al, 2018;Qi et al, 2019;Wu et al, 2019b), salt body interpretation (Di et al, 2018;Di and AlRegib, 2019;Morris et al, 2019;Ye et al, 2019), de-noising (Dong et al, 2019;Sun et al, 2019;Wu et al, 2019a;Yu et al, 2019;Zu et al, 2019;Li, 2020), interpolation (Jia and Ma, 2017;Jia et al, 2018;Wang et al, 2019a,b), 4D monitoring (Maharramov et al, 2019;Liu and Grana, 2019;Yuan et al, 2019), acquisition optimization (Chamarczuk et al, 2019;Jiang et al, 2019;Nakayama et al, 2019) and geo-engineering applications (Gu et al, 2018;Terry et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

ML-Misfit: Learning a Robust Misfit Function for Full-Waveform Inversion Using Machine Learning

Sun

Alkhalifah

2020

EAGE 2020 Annual Conference &Amp; Exhibition Online

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Most of the available advanced misfit functions, including those that utilize a matching filter or the optimal transport measure, are all hand-crafted. Tuning and improving those methods requires profound mathematical and physical knowledge from FWI practitioners.Besides, the performance of those misfit functions is data-dependent, and it is challenging to find a solution when these approaches fail to invert a specific dataset. We propose to learn a robust misfit function for FWI, entitled ML-misfit, based on machine learning. Inspired by the recently introduced optimal transport of the matching filter (OTMF) objective, we design a specific neural network (NN) architecture for the misfit function in a form similar to comparing the mean and variance for two distributions in OTMF. To guarantee the resulting learned misfit is a metric, we accommodate the symmetry of the misfit with respect to its input and a Hinge loss regularization term in the meta-loss function to satisfy the "triangle 1 arXiv:2002.03163v2 [physics.geo-ph] 18 Mar 2020 inequality" rule. In the framework of meta-learning, we train the network by running FWI to invert for randomly generated velocity models and update the parameters of the NN by minimizing a meta-loss, which is defined as the accumulated difference between the true and inverted models. The learning and improvement of such an ML-misfit are automatic, and the resulting ML-misfit is data-adaptive. We first illustrate the basic principles behind the ML-misfit for learning a convex misfit function for travel-time shifted signals. Further, we train the NN on 2D horizontally layered models, and we demonstrate the effectiveness and robustness of the learned ML-misfit by applying it to the well-known Marmousi model.

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“…Zhang et al 2014improved the coherence attributes by using a vein pattern recognition algorithm. Qi et al ( , 2019 built a workflow to enhance and skeletonize coherence fault images along fault planes. Wu and Zhu (2017) highlighted fault positions and construct fault surfaces by applying 2D exponential filters to the precomputed fault attribute volumes.…”

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confidence: 99%

Seismic fault attribute estimation using a local fault model

et al. 2019

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Faults in the subsurface can be an avenue of, or a barrier to, hydrocarbon flow and pressure communication. Manual interpretation of discontinuities on 3D seismic amplitude volume is the most common way to define faults within a reservoir. Unfortunately, 3D seismic fault interpretation can be a time-consuming and tedious task. Seismic attributes such as coherence help define faults, but suffer from “staircase” artifacts and nonfault-related stratigraphic discontinuities. We assume that each sample of the seismic data is located at a potential fault plane. The hypothesized fault divides the seismic data centered at the analysis sample into two subwindows. We then compute the coherence for the two subwindows and full analysis window. We repeat the process by rotating the hypothesized fault plane along a set of user-defined discrete fault dip and azimuth. We obtain almost the same coherence values for the subwindows and the full window if the analysis point is not located at a fault plane. The “best” fault plane results in maximum coherence for the subwindows and minimum coherence for the full window if the analysis point is located at a fault plane. To improve the continuity of the fault attributes, we finally smooth the fault probability attribute along the estimated fault plane. We illustrate the effectiveness of our workflow by applying it to a synthetic and two real seismic data. The results indicate that our workflow successfully produces a continuous fault attribute without staircase artifacts and stratigraphic discontinuities.

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Image processing of seismic attributes for automatic fault extraction

Cited by 7 publications

References 39 publications

Machine learning in acoustics: Theory and applications

Machine learning in acoustics: Theory and applications

ML-Misfit: Learning a Robust Misfit Function for Full-Waveform Inversion Using Machine Learning

Seismic fault attribute estimation using a local fault model

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