{"title":"TESTING THE ALGORITHM OF SURFACE-CONSISTENT COMPENSATION ON SEISMIC AMPLITUDES","authors":"R. S. Kushnarev, N. Goreyavchev, G. Mitrofanov","doi":"10.25205/978-5-4437-1251-2-164-167","DOIUrl":null,"url":null,"abstract":"This work is concerned with the algorithm of surface-consistent compensation on seismic data. The algorithm allows you to determine and correct variations in the seismic signal associated with near-surface heterogeneity. Near-surface heterogeneity complicates the conditions of seismic excitation and reception of the signal. We created the synthetic three-dimension data to test the algorithm. The synthetic data includes anomalies of near-surface section has a volume of 10,5 million observations.","PeriodicalId":203470,"journal":{"name":"Trofimuk Readings - 2021: Proceedings of the All-Russian Youth Scientific Conference with the Participation of Foreign Scientists","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trofimuk Readings - 2021: Proceedings of the All-Russian Youth Scientific Conference with the Participation of Foreign Scientists","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25205/978-5-4437-1251-2-164-167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This work is concerned with the algorithm of surface-consistent compensation on seismic data. The algorithm allows you to determine and correct variations in the seismic signal associated with near-surface heterogeneity. Near-surface heterogeneity complicates the conditions of seismic excitation and reception of the signal. We created the synthetic three-dimension data to test the algorithm. The synthetic data includes anomalies of near-surface section has a volume of 10,5 million observations.