{"title":"A spontaneous dynamic fault rupture simulation without giving a priori rupture starting area and rupture stopping area","authors":"M. Yamada, K. Hada, R. Imai, H. Fujiwara","doi":"10.1190/segj2021-080.1","DOIUrl":"https://doi.org/10.1190/segj2021-080.1","url":null,"abstract":"","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116217343","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}
M. Liao, Bin Lin, Zhong Li, Yang Yang, Yanhui Fu, H. F. Yao
{"title":"Anisotropy-based wireline logging data normalization in shale gas horizontal wells and customized formation evaluation in Changning shale gas field","authors":"M. Liao, Bin Lin, Zhong Li, Yang Yang, Yanhui Fu, H. F. Yao","doi":"10.1190/segj2021-072.1","DOIUrl":"https://doi.org/10.1190/segj2021-072.1","url":null,"abstract":"","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121966972","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}
{"title":"Quantitative evaluation of the relative permittivity of artificial soil with altered soil types and water content","authors":"Toshinori Kanemitsu, Yohei Morifuji, Kenji Kubota","doi":"10.1190/segj2021-046.1","DOIUrl":"https://doi.org/10.1190/segj2021-046.1","url":null,"abstract":"","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130629594","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}
{"title":"High-resolution image acquired by deep sub-bottom profiling of small-scale features using complex attributes analysis on northeastern Hawaiian Arch","authors":"M. Yamashita, S. Miura, Ryôichi Miura","doi":"10.1190/segj2021-071.1","DOIUrl":"https://doi.org/10.1190/segj2021-071.1","url":null,"abstract":"","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"2268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130243659","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}
The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural networks have started to gain momentum. Convolutional neural networks are particularly efficient at pattern recognition within images, and this is why they are suitable for seismic facies classification and interpretation tasks. We experimented with three different architectures based on convolutional layers and compared them with different synthetic and field datasets in terms of quality of the seismic interpretation results and computational efficiency. The architectures used in our study were three deep fully convolutional architectures: a 3D convolutional network with a fully connected head; a 2D fully convolutional network, and U-Net. We found the U-Net architecture to be both robust and the fastest when performing classification at the prediction stage. The 3D convolutional model with a fully connected head was the slowest, while a fully convolutional model was unstable in its predictions.
{"title":"Shape carving methods of geologic body interpretation from seismic data based on deep learning","authors":"S. Petrov, T. Mukerji, Xin Zhang, Xinfei Yan","doi":"10.1190/segj2021-066.1","DOIUrl":"https://doi.org/10.1190/segj2021-066.1","url":null,"abstract":"The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural networks have started to gain momentum. Convolutional neural networks are particularly efficient at pattern recognition within images, and this is why they are suitable for seismic facies classification and interpretation tasks. We experimented with three different architectures based on convolutional layers and compared them with different synthetic and field datasets in terms of quality of the seismic interpretation results and computational efficiency. The architectures used in our study were three deep fully convolutional architectures: a 3D convolutional network with a fully connected head; a 2D fully convolutional network, and U-Net. We found the U-Net architecture to be both robust and the fastest when performing classification at the prediction stage. The 3D convolutional model with a fully connected head was the slowest, while a fully convolutional model was unstable in its predictions.","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130279594","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}
F. López, M. Navarro, P. Martínez-Pagán, A. García-Jerez, J. Pérez-Cuevas, T. Enomoto
{"title":"Statistical analysis of the Vs30 structure of Almeria city (southeast of Spain) inferred from topographic slope method","authors":"F. López, M. Navarro, P. Martínez-Pagán, A. García-Jerez, J. Pérez-Cuevas, T. Enomoto","doi":"10.1190/segj2021-088.1","DOIUrl":"https://doi.org/10.1190/segj2021-088.1","url":null,"abstract":"","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134236338","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}
{"title":"Robust time-lapse full-waveform inversion using boundary integral representation: Numerical examples for borehole and ambient seismic noise monitoring","authors":"S. Minato","doi":"10.1190/segj2021-065.1","DOIUrl":"https://doi.org/10.1190/segj2021-065.1","url":null,"abstract":"","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131030510","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}
Jingyi Sun, Y. Mukuhira, T. Nagata, T. Nonomura, H. Moriya, Takatoshi Ito
{"title":"Detection of P-S travel time for low SNR event by particle motion analysis","authors":"Jingyi Sun, Y. Mukuhira, T. Nagata, T. Nonomura, H. Moriya, Takatoshi Ito","doi":"10.1190/segj2021-034.1","DOIUrl":"https://doi.org/10.1190/segj2021-034.1","url":null,"abstract":"","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133694931","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}
{"title":"Surface displacement during the periods before and after the 2018 northern Osaka earthquake estimated by PSInSAR analysis","authors":"Y. Shigemitsu, K. Ishitsuka, Weiren Lin","doi":"10.1190/segj2021-037.1","DOIUrl":"https://doi.org/10.1190/segj2021-037.1","url":null,"abstract":"","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114676752","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}
{"title":"Anisotropic seismic reservoir characterization: Practical applications","authors":"M. Asaka","doi":"10.1190/segj2021-062.1","DOIUrl":"https://doi.org/10.1190/segj2021-062.1","url":null,"abstract":"","PeriodicalId":414700,"journal":{"name":"Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117102340","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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