Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2998470.1
L. Peruzzo, C. Chou, Yuxin Wu, B. Riley, P. Petrov, G. Newman, B. Dafflon, Eoin L. Brodie, S. Hubbard, E. Blancaflor, Xue‐Feng Ma, R. Versteeg, M. Schmutz
{"title":"Geoelectrical investigation of the root-soil interaction","authors":"L. Peruzzo, C. Chou, Yuxin Wu, B. Riley, P. Petrov, G. Newman, B. Dafflon, Eoin L. Brodie, S. Hubbard, E. Blancaflor, Xue‐Feng Ma, R. Versteeg, M. Schmutz","doi":"10.1190/SEGAM2018-2998470.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2998470.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"39 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":"131727076","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}
Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2985176.1
Xu Si, Yijun Yuan
{"title":"Random noise attenuation based on residual learning of deep convolutional neural network","authors":"Xu Si, Yijun Yuan","doi":"10.1190/SEGAM2018-2985176.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2985176.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"17 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":"130795210","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}
Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2969608.1
Stephen Brennan, L. Adam, L. Strachan
{"title":"Dynamic elasticity and the controlling physical properties of New Zealand’s coaly source rocks","authors":"Stephen Brennan, L. Adam, L. Strachan","doi":"10.1190/SEGAM2018-2969608.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2969608.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"51-52 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":"131003614","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}
Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2997555.1
P. Trinh, R. Brossier, L. Métivier, J. Virieux
Full waveform inversion (FWI) of onshore targets is very challenging due to the complex free-surface-related effects and 3D geometry representation. In such areas, the seismic wavefield is dominated by highly energetic and dispersive surface waves, converted waves and back-scattering energy. We use a timedomain spectral-element-based approach for elastic wavefield simulation in foothill areas. The challenges of the elastic multiparameter FWI in complex land areas are highlighted through the inversion of the pseudo-2D dip-line survey of the SEAM Phase II Foothill dataset. As the data is dominated by surface waves, it is mainly sensitive to the S-wave velocity. We then propose a two-steps data-windowing hierarchy to simultaneously invert for Pand S-wave speeds, focusing on early body waves before considering the whole data. By doing so, we aim at exploiting the maximum amount of information in the observed data and getting a reliable model parameters estimation, both in the near-surface and in deeper part. The model constraint that we introduce on the ratio of compressional and shear velocities also plays an important role to mitigate the ill-posedness of the inversion process.
{"title":"Data-oriented strategy and Vp/Vs model-constraint for simultaneous Vp and Vs reconstruction in 3D viscoelastic FWI: Application to the SEAM II Foothill dataset","authors":"P. Trinh, R. Brossier, L. Métivier, J. Virieux","doi":"10.1190/SEGAM2018-2997555.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2997555.1","url":null,"abstract":"Full waveform inversion (FWI) of onshore targets is very challenging due to the complex free-surface-related effects and 3D geometry representation. In such areas, the seismic wavefield is dominated by highly energetic and dispersive surface waves, converted waves and back-scattering energy. We use a timedomain spectral-element-based approach for elastic wavefield simulation in foothill areas. The challenges of the elastic multiparameter FWI in complex land areas are highlighted through the inversion of the pseudo-2D dip-line survey of the SEAM Phase II Foothill dataset. As the data is dominated by surface waves, it is mainly sensitive to the S-wave velocity. We then propose a two-steps data-windowing hierarchy to simultaneously invert for Pand S-wave speeds, focusing on early body waves before considering the whole data. By doing so, we aim at exploiting the maximum amount of information in the observed data and getting a reliable model parameters estimation, both in the near-surface and in deeper part. The model constraint that we introduce on the ratio of compressional and shear velocities also plays an important role to mitigate the ill-posedness of the inversion process.","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"41 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":"131135779","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}
Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2998354.1
C. Mapeli, L. Ou, M. Prasad
{"title":"Induced-polarization measurements of sandstones under elevated pressure","authors":"C. Mapeli, L. Ou, M. Prasad","doi":"10.1190/SEGAM2018-2998354.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2998354.1","url":null,"abstract":"","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":"130741235","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}
Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2998482.1
Ke Huang, G. Zhong, Liao-liang Wang, Yiqun Guo
{"title":"Seismic facies in the Pearl River Submarine Canyon, northern South China Sea","authors":"Ke Huang, G. Zhong, Liao-liang Wang, Yiqun Guo","doi":"10.1190/SEGAM2018-2998482.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2998482.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"1 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":"130914530","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}
Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2994775.1
R. Mittet
Implementing sharp internal interfaces in finite-difference schemes with high spatial accuracy is challenging. The implementations of interfaces are generally considered accurate to at best second order. The natural way to describe an abrupt change in material parameters is by the use of the Heaviside step function. However, the implementation of the Heaviside step function must be consistent with the discrete sampling on the finite-difference grid. Assuming that the step function takes on the value zero up to some node location and then unity from thereon results in an incorrect wavenumber representation of the Heaviside step function so this representation must be incorrect. However, starting with the proper wavenumber representation of the Heaviside step function and then transforming this spectrum to the space domain give much better accuracy. The interface location appears as a proportionality factor in the phase in the wavenumber domain and can be altered continuously. Thus, the interface can be located anywhere between two node locations. This is a key factor for avoiding stair-case effects from the fields when doing 2D and 3D finite-difference simulations. The proposed method can be used for all systems of partial differential equations that formally can be expressed as a material parameter times a dynamic field on one side of the equal sign and with spatial derivatives on the other side of the equal sign. For geophysical simulations the most important cases will be the Maxwell equations and the acoustic and elastic wave equations.
{"title":"Implementing internal interfaces in finite-difference schemes with the Heaviside step function","authors":"R. Mittet","doi":"10.1190/SEGAM2018-2994775.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2994775.1","url":null,"abstract":"Implementing sharp internal interfaces in finite-difference schemes with high spatial accuracy is challenging. The implementations of interfaces are generally considered accurate to at best second order. The natural way to describe an abrupt change in material parameters is by the use of the Heaviside step function. However, the implementation of the Heaviside step function must be consistent with the discrete sampling on the finite-difference grid. Assuming that the step function takes on the value zero up to some node location and then unity from thereon results in an incorrect wavenumber representation of the Heaviside step function so this representation must be incorrect. However, starting with the proper wavenumber representation of the Heaviside step function and then transforming this spectrum to the space domain give much better accuracy. The interface location appears as a proportionality factor in the phase in the wavenumber domain and can be altered continuously. Thus, the interface can be located anywhere between two node locations. This is a key factor for avoiding stair-case effects from the fields when doing 2D and 3D finite-difference simulations. The proposed method can be used for all systems of partial differential equations that formally can be expressed as a material parameter times a dynamic field on one side of the equal sign and with spatial derivatives on the other side of the equal sign. For geophysical simulations the most important cases will be the Maxwell equations and the acoustic and elastic wave equations.","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"23 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":"133464508","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}
Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2996783.1
J. Dramsch, M. Lüthje
In the 1950s neural networks started as a simple direct connection of several nodes in an input layer to several nodes in an output layer (Widrow and Lehr, 1990). In geophysics this puts us to the introduction of seismic trace stacking (Yilmaz, 2001). In 1989 the first idea of a convolutional neural network was born (Lecun, 1989) and back-propagation was formalized as an error-propagation mechanism (Rumelhart et al., 1988). In 2012 the paper (Krizhevsky et al., 2012) propelled the field of deep learning forward implementing essential components, namely GPU training, ReLu activation functions (Dahl et al., 2013) and dropout (Srivastava et al., 2014). They outperformed previous models in the ImageNet challenge (Deng et al., 2009) by almost halving the prediction error. Waldeland and Solberg (2016) showed that neural networks can be used to classify salt diapirs in 3D seismic data. Charles Rutherford Ildstad (2017) generalized this work to nD and beyond two classes of salt and ”else”.
在20世纪50年代，神经网络开始作为输入层的几个节点与输出层的几个节点的简单直接连接(Widrow和Lehr, 1990)。在地球物理学中，这使我们引入了地震道叠加(Yilmaz, 2001)。1989年，卷积神经网络的第一个想法诞生了(Lecun, 1989)，反向传播被形式化为一种错误传播机制(Rumelhart et al.， 1988)。2012年，该论文(Krizhevsky et al.， 2012)推动了深度学习领域向前发展，实现了基本组件，即GPU训练、ReLu激活函数(Dahl et al.， 2013)和dropout (Srivastava et al.， 2014)。它们在ImageNet挑战中的表现优于以前的模型(Deng et al.， 2009)，预测误差几乎减半。Waldeland and Solberg(2016)表明，神经网络可以用于对三维地震数据中的盐底辟进行分类。Charles Rutherford Ildstad(2017)将这项工作推广到nD和两类盐和“其他”之外。
{"title":"Deep-learning seismic facies on state-of-the-art CNN architectures","authors":"J. Dramsch, M. Lüthje","doi":"10.1190/SEGAM2018-2996783.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2996783.1","url":null,"abstract":"In the 1950s neural networks started as a simple direct connection of several nodes in an input layer to several nodes in an output layer (Widrow and Lehr, 1990). In geophysics this puts us to the introduction of seismic trace stacking (Yilmaz, 2001). In 1989 the first idea of a convolutional neural network was born (Lecun, 1989) and back-propagation was formalized as an error-propagation mechanism (Rumelhart et al., 1988). In 2012 the paper (Krizhevsky et al., 2012) propelled the field of deep learning forward implementing essential components, namely GPU training, ReLu activation functions (Dahl et al., 2013) and dropout (Srivastava et al., 2014). They outperformed previous models in the ImageNet challenge (Deng et al., 2009) by almost halving the prediction error. Waldeland and Solberg (2016) showed that neural networks can be used to classify salt diapirs in 3D seismic data. Charles Rutherford Ildstad (2017) generalized this work to nD and beyond two classes of salt and ”else”.","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"24 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":"126580218","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}
Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2995563.1
A. Willis, P. Busono
{"title":"Application of crosscorrelation of angle stacks for velocity quality control in seismic imaging","authors":"A. Willis, P. Busono","doi":"10.1190/SEGAM2018-2995563.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2995563.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"2 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":"127845684","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}
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