{"title":"全波形反演(FWI)是利用混合几何图形获取地震数据的及时方法","authors":"K. Florez, J. G. Mantilla, Ana B. Ramirez","doi":"10.1109/STSIVA.2016.7743314","DOIUrl":null,"url":null,"abstract":"This work presents the development of a FWI method in time, that uses seismic data acquired using a blended geometry. Blended geometry involves temporal and spatial overlap of multiple shots, randomly located in the same acquisition, whereas the traditional acquisition uses regular spacing of the receivers and one single shot at a time. The FWI method uses the acoustic wave equation with constant density 2D to find the modeled data, and a ℓ2-error norm as misfit function between the observed and modeled data. The blended geometry acquisition was designed to obtain synthetically the seismic data at the surface with 5 shots simultaneous, using the Marmousi model of size 3.025 km × 12.425 Km (with a grid of 121 × 497 points) as true subsurface velocity model. The FWI method estimates the velocity using an smoothed version of the Marmousi as initial model, and it updates the velocity model iteratively using a gradient descent method. The FWI method for blended and traditional geometries was implemented and tested on the same computer under controlled conditions, for the same number of shots and iterations. The experimental results of the velocity models obtained using blended and traditional geometries have similar quadratic error norm, and the execution time of the FWI for the blended acquisition is up to 1.88 times faster than the FWI method for the traditional acquisition.","PeriodicalId":373420,"journal":{"name":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Full waveform inversion (FWI) in time for seismic data acquired using a blended geometry\",\"authors\":\"K. Florez, J. G. Mantilla, Ana B. Ramirez\",\"doi\":\"10.1109/STSIVA.2016.7743314\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work presents the development of a FWI method in time, that uses seismic data acquired using a blended geometry. Blended geometry involves temporal and spatial overlap of multiple shots, randomly located in the same acquisition, whereas the traditional acquisition uses regular spacing of the receivers and one single shot at a time. The FWI method uses the acoustic wave equation with constant density 2D to find the modeled data, and a ℓ2-error norm as misfit function between the observed and modeled data. The blended geometry acquisition was designed to obtain synthetically the seismic data at the surface with 5 shots simultaneous, using the Marmousi model of size 3.025 km × 12.425 Km (with a grid of 121 × 497 points) as true subsurface velocity model. The FWI method estimates the velocity using an smoothed version of the Marmousi as initial model, and it updates the velocity model iteratively using a gradient descent method. The FWI method for blended and traditional geometries was implemented and tested on the same computer under controlled conditions, for the same number of shots and iterations. The experimental results of the velocity models obtained using blended and traditional geometries have similar quadratic error norm, and the execution time of the FWI for the blended acquisition is up to 1.88 times faster than the FWI method for the traditional acquisition.\",\"PeriodicalId\":373420,\"journal\":{\"name\":\"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STSIVA.2016.7743314\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STSIVA.2016.7743314","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
这项工作提出了一种实时的FWI方法,该方法使用使用混合几何形状获得的地震数据。混合几何涉及多个镜头的时空重叠,随机位于同一采集中,而传统的采集使用常规间隔的接收器和一次一个镜头。FWI方法采用二维等密度声波方程求解模型数据,并以2-误差范数作为观测数据与模型数据之间的失拟函数。采用尺寸为3.025 km × 12.425 km(网格为121 × 497点)的Marmousi模型作为真实地下速度模型,设计了混合几何采集方法,以5次射击同时综合获取地面地震数据。FWI方法使用平滑版本的Marmousi作为初始模型来估计速度,并使用梯度下降法迭代更新速度模型。混合几何形状和传统几何形状的FWI方法在同一台计算机上进行了测试,在受控条件下进行了相同次数的射击和迭代。混合几何速度模型与传统几何速度模型的实验结果具有相似的二次误差范数,混合几何速度模型的执行时间比传统几何速度模型的执行时间快1.88倍。
Full waveform inversion (FWI) in time for seismic data acquired using a blended geometry
This work presents the development of a FWI method in time, that uses seismic data acquired using a blended geometry. Blended geometry involves temporal and spatial overlap of multiple shots, randomly located in the same acquisition, whereas the traditional acquisition uses regular spacing of the receivers and one single shot at a time. The FWI method uses the acoustic wave equation with constant density 2D to find the modeled data, and a ℓ2-error norm as misfit function between the observed and modeled data. The blended geometry acquisition was designed to obtain synthetically the seismic data at the surface with 5 shots simultaneous, using the Marmousi model of size 3.025 km × 12.425 Km (with a grid of 121 × 497 points) as true subsurface velocity model. The FWI method estimates the velocity using an smoothed version of the Marmousi as initial model, and it updates the velocity model iteratively using a gradient descent method. The FWI method for blended and traditional geometries was implemented and tested on the same computer under controlled conditions, for the same number of shots and iterations. The experimental results of the velocity models obtained using blended and traditional geometries have similar quadratic error norm, and the execution time of the FWI for the blended acquisition is up to 1.88 times faster than the FWI method for the traditional acquisition.