SEG Technical Program Expanded Abstracts 2018最新文献_第6页

Towards understanding common features between natural and seismic images 了解自然和地震图像之间的共同特征

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2996501.1

M. Shafiq, M. Prabhushankar, H. Di, G. AlRegib

{"title":"Towards understanding common features between natural and seismic images","authors":"M. Shafiq, M. Prabhushankar, H. Di, G. AlRegib","doi":"10.1190/SEGAM2018-2996501.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2996501.1","url":null,"abstract":"In this paper, we propose an unsupervised learning framework that aims at evaluating the applicability of the broad domain knowledge from natural images and videos in assisting seismic interpretation, such as seismic attributes, structural automation, and seismic image processing. Specifically, we propose a novel approach based on a data-driven sparse autoencoder architecture that can automatically recognize and extract salient geologic features from unlabeled 3D seismic volumes. It is superior in learning sparse features from natural images, which is not limited by the lack of labeled seismic images. By developing models based on prevalent features in both domains, we can not only automate the process of seismic interpretation but also develop new seismic attributes that highlight areas of interest in seismic sections and convey the most useful information in a compact manner. We show that the proposed approach can effectively detect salient areas within real and synthetic seismic datasets. The experimental results demonstrate the potential of the proposed method in highlighting important geological structures such as horizons, faults, salt domes, and seismic reflections at different orientations and can be effectively used for computer-aided extraction of other geologic features as well.","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124209681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Addressing the challenges in two unconventional basins 应对两个非常规盆地的挑战

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2984853.1

R. Mccann, Hui Fan, L. Bell, B. Lasscock, Jonathan W. Cheek

引用次数: 0

A more stable Q tomography for strong anomalies 更稳定的强异常Q层析成像

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2985969.1

Wang Lei, Liang Jiandong, Zhou Zhengzheng, Zhang Shaohua, Qian Zhongping, Jia Shaohui

引用次数: 1

Estimation of fluid mobility with frequency-dependent Bayesian inversion 基于频率相关贝叶斯反演的流体迁移率估计

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2997605.1

Li Lu, Xingyao Yin, Z. Zong, Kun Li

引用次数: 1

A traveltime-based offset-to-angle transform for borehole seismic survey data in anisotropic media 各向异性介质中基于行时的井眼地震测量数据偏移角度变换

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2989356.1

A. Padhi, M. Willis, Xiaomin Zhao

引用次数: 0

Analysis of the fractal convergence properties of geophysical inverse problems 地球物理反演问题的分形收敛性分析

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2997776.1

Brandon Dias, G. Cooper, R. Durrheim

引用次数: 0

Multicomponent deblending of marine data using a pattern-based approach 基于模式方法的海洋数据多分量分离

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2996253.1

Joseph Jennings, R. Chang, B. Biondi, S. Ronen

引用次数: 2

Imaging of 4D seismic during steam injection for shallow heavy oil reservoir in North Kuwait 科威特北部浅层稠油油藏注汽过程的四维地震成像

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2995307.1

A. El-Emam, Jarrah Al-Jenaie, H. Bayri, W. Zahran, C. Koeninger

引用次数: 0

Real-time seismic-image interpretation via deconvolutional neural network 基于反卷积神经网络的实时地震图像解释

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2997303.1

H. Di, Zhen Wang, G. AlRegib

{"title":"Real-time seismic-image interpretation via deconvolutional neural network","authors":"H. Di, Zhen Wang, G. AlRegib","doi":"10.1190/SEGAM2018-2997303.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2997303.1","url":null,"abstract":"Seismic interpretation is now serving as a fundamental tool for depicting subsurface geology and assisting activities in various domains, such as environmental engineering and petroleum exploration. In the past decades, a number of computer-aided tools have been developed for speeding the interpretation process and improving the interpretation accuracy. However, most of the existing interpretation techniques are designed for interpreting a certain seismic feature (e.g., faults and salt domes) in a seismic section or volume at a time; correspondingly, the rest features would be ignored. Full-feature interpretation becomes feasible with the aid of multiple classification techniques. When implemented into the seismic domain, however, the major drawback is the low efficiency particularly for a large dataset, since the classification need to be repeated at every seismic sample. To resolve such limitation, this study proposes implementing the deconvolutional neural network (DCNN) for the purpose of real-time seismic interpretation, so that all the important features in a seismic image can be identified and interpreted both accurately and simultaneously. The performance of the new DCNN tool is verified through application of segmenting the F3 seismic dataset into nine major features, including salt domes, strong reflections, steep dips, etc. Good match is observed between the results and the original seismic signals, indicating not only the capability of the proposed DCNN network in seismic image analysis but also its great potentials for realtime seismic feature interpretation of an entire volume.","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"350 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124352026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Low S/N seismicity detection using features of 3D particle motions of direct P-waves 利用直接纵波三维粒子运动特征进行低信噪比地震活动探测

SEG Technical Program Expanded Abstracts 2018 Pub Date : 2018-08-27 DOI: 10.1190/SEGAM2018-2965365.1

Y. Mukuhira, O. Poliannikov, M. Fehler, H. Moriya

引用次数: 1