Pub Date : 2018-08-27DOI: 10.1190/SEGAM2018-2997833.1
Herbert Wang, D. Fratta, N. Lord, Xiangfang Zeng, T. Coleman
{"title":"Distributed acoustic sensing (DAS) field trials for near-surface geotechnical properties, earthquake seismology, and mine monitoring","authors":"Herbert Wang, D. Fratta, N. Lord, Xiangfang Zeng, T. Coleman","doi":"10.1190/SEGAM2018-2997833.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2997833.1","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":"128437595","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/SEGAM2018-2992237.1
Y. Sripanich, I. Vasconcelos, K. Wapenaar, J. Trampert
The Marchenko method represents a constructive technique to obtain Green’s functions between the acquisition surface and any arbitrary point in the medium. The process generally involves solving an inversion starting with a direct-wave Green’s function from the desired subsurface position, which is typically obtained using an approximate velocity model. In this study, we first propose to formulate the Marchenko method in the time-imaging domain. We recognize that the traveltime of the direct-wave Green’s function is related to the Cheop’s traveltime pyramid commonly used in time-domain processing and can be readily obtained from the local slopes of the common-midpoint (CMP) gathers. This observation allows us to substitute the need for a prior velocity model with the data-driven slope estimation process. Moreover, we show that working in the time-imaging domain allows for the specification of the desired subsurface position in terms of vertical time, which is connected to the Cartesian depth position via the timeto-depth conversion. Our results suggest that the prior velocity model is only required when specifying the position in depth but this requirement can be circumvented by making use of the time-imaging domain and its usual assumptions. Provided that those assumptions are satisfied, the estimated Green’s functions from the proposed method have comparable quality to those obtained with the knowledge of a prior velocity model.
{"title":"Velocity-independent Marchenko method in time- and depth-imaging domains","authors":"Y. Sripanich, I. Vasconcelos, K. Wapenaar, J. Trampert","doi":"10.1190/SEGAM2018-2992237.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2992237.1","url":null,"abstract":"The Marchenko method represents a constructive technique to obtain Green’s functions between the acquisition surface and any arbitrary point in the medium. The process generally involves solving an inversion starting with a direct-wave Green’s function from the desired subsurface position, which is typically obtained using an approximate velocity model. In this study, we first propose to formulate the Marchenko method in the time-imaging domain. We recognize that the traveltime of the direct-wave Green’s function is related to the Cheop’s traveltime pyramid commonly used in time-domain processing and can be readily obtained from the local slopes of the common-midpoint (CMP) gathers. This observation allows us to substitute the need for a prior velocity model with the data-driven slope estimation process. Moreover, we show that working in the time-imaging domain allows for the specification of the desired subsurface position in terms of vertical time, which is connected to the Cartesian depth position via the timeto-depth conversion. Our results suggest that the prior velocity model is only required when specifying the position in depth but this requirement can be circumvented by making use of the time-imaging domain and its usual assumptions. Provided that those assumptions are satisfied, the estimated Green’s functions from the proposed method have comparable quality to those obtained with the knowledge of a prior velocity model.","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"91 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":"124668118","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/SEGAM2018-2996306.1
O. Gramstad, M. Nickel
{"title":"Automated interpretation of top and base salt using deep-convolutional networks","authors":"O. Gramstad, M. Nickel","doi":"10.1190/SEGAM2018-2996306.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2996306.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"114 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":"124789562","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/SEGAM2018-2998168.1
A. Garg, D. Verschuur
{"title":"Including internal multiples in joint migration inversion and redatuming of North Sea field data","authors":"A. Garg, D. Verschuur","doi":"10.1190/SEGAM2018-2998168.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2998168.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"11 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":"130401759","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/segam2018-2997865.1
Yazeed Alaudah, Shan Gao, G. AlRegib
Recently, there has been increasing interest in using deep learning techniques for various seismic interpretation tasks. However, unlike shallow machine learning models, deep learning models are often far more complex and can have hundreds of millions of free parameters. This not only means that large amounts of computational resources are needed to train these models, but more critically, they require vast amounts of labeled training data as well. In this work, we show how automatically-generated weak labels can be effectively used to overcome this problem and train powerful deep learning models for labeling seismic structures in large seismic volumes. To achieve this, we automatically generate thousands of weak labels and use them to train a deconvolutional network for labeling fault, salt dome, and chaotic regions within the Netherlands F3 block. Furthermore, we show how modifying the loss function to take into account the weak training labels helps reduce false positives in the labeling results. The benefit of this work is that it enables the effective training and deployment of deep learning models to various seismic interpretation tasks without requiring any manual labeling effort. We show excellent results on the Netherlands F3 block, and show how our model outperforms other baseline models.
{"title":"Learning to label seismic structures with deconvolution networks and weak labels","authors":"Yazeed Alaudah, Shan Gao, G. AlRegib","doi":"10.1190/segam2018-2997865.1","DOIUrl":"https://doi.org/10.1190/segam2018-2997865.1","url":null,"abstract":"Recently, there has been increasing interest in using deep learning techniques for various seismic interpretation tasks. However, unlike shallow machine learning models, deep learning models are often far more complex and can have hundreds of millions of free parameters. This not only means that large amounts of computational resources are needed to train these models, but more critically, they require vast amounts of labeled training data as well. In this work, we show how automatically-generated weak labels can be effectively used to overcome this problem and train powerful deep learning models for labeling seismic structures in large seismic volumes. To achieve this, we automatically generate thousands of weak labels and use them to train a deconvolutional network for labeling fault, salt dome, and chaotic regions within the Netherlands F3 block. Furthermore, we show how modifying the loss function to take into account the weak training labels helps reduce false positives in the labeling results. The benefit of this work is that it enables the effective training and deployment of deep learning models to various seismic interpretation tasks without requiring any manual labeling effort. We show excellent results on the Netherlands F3 block, and show how our model outperforms other baseline models.","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"48 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":"126995184","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/SEGAM2018-2992968.1
Yang Zhao, H. Zhang, T. Fei, Jidong Yang, Hejun Zhu
{"title":"Reducing artifacts of elastic reverse time migration with de-primary","authors":"Yang Zhao, H. Zhang, T. Fei, Jidong Yang, Hejun Zhu","doi":"10.1190/SEGAM2018-2992968.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2992968.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"3 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":"123841289","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/SEGAM2018-2995134.1
R. Streich, Akshat Abhishek
{"title":"Magnetotelluric imaging for exploration in fold-and-thrust belt settings: A feasibility and case study","authors":"R. Streich, Akshat Abhishek","doi":"10.1190/SEGAM2018-2995134.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2995134.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"12 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":"124009924","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/SEGAM2018-2998379.1
A. Mattheis, B. Schneider
{"title":"Intersectional approaches to counter harassment and discrimination in geophysics","authors":"A. Mattheis, B. Schneider","doi":"10.1190/SEGAM2018-2998379.1","DOIUrl":"https://doi.org/10.1190/SEGAM2018-2998379.1","url":null,"abstract":"","PeriodicalId":158800,"journal":{"name":"SEG Technical Program Expanded Abstracts 2018","volume":"27 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":"124030799","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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