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Multicomponent Joint Elastic Reverse Time Migration

Posted on:2013-07-19Degree:MasterType:Thesis
Country:ChinaCandidate:Y T ZhuFull Text:PDF
GTID:2250330422458769Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
The seismic waves are elastic vector waves that include P-and S-waves. To get thetrue structure and amplitude of the migration images, it is necessary to use the elastictheory and to study the imaging methods that are designed for multicomponent seismicdata. However, the conventional processing technologies for multicomponent seismic dataare scalar methods, which cannot correctly keep the elastic vector characteristic for bothwave form and energy. Thus, based on the idea of “multicomponent-joint”, we carry outour research on elastic reverse time migration (ERTM) that is specially designed formulticomponent seismic data.In this paper, we begin by summarizing the elastic reverse time migration in a moregeneral form. The key steps for ERTM are:(1) reconstruction of the underground vectorwavefields of source and receiver,(2) separation of the vector wavefields into P-andS-wave components, and (3) application of the elastic crosscorrelation imaging conditionsfor the pure wave modes. We also present two work flows “Forward-Save-Reverse” and“Forward-Reverse-Reverse” for the reconstruction of the source wavefields.Next, we discuss the two kinds of imaging conditions for ERTM: the “zero lag”imaging conditions (include the excitation imaging condition, deconvolution imagingcondition, and cross-correlation imaging condition with source illumination) and “non-zerolag” extended imaging conditions. For those extended imaging conditions, we analyze theangle decomposition for elastic case; thus, illustrate the method to compute angle domaincommon-image gathers (CIGs) for ERTM.Then, for the polarity reversal of PS and SP images, we derive a new correctionmethod in the common-shot domain. Based on the analysis of the polarity distributions ofPS and SP images, we find that the key aspect for polarity reversal correction is thedistribution of S-wave component. We introduce a sign factor to represent the distribution of S-wave component and compute the sign factor by including the energy flux densityvectors. The polarity reversal is corrected by multiplying the PS and SP images with thesign factor at every time step when an elastic imaging condition is applied. We also designa filter algorithm for the sign factor to improve its consistency along an event and therebyto diminish the impact of the inaccuracy of the energy flux density vector and to improvethe imaging results.After that, we analyze the mechanism and essence of the low wave-number noise forRTM. According to different characteristic of the low wave-number noise, we classify thenoise suppression methods into two kinds and evaluate each method. Thereby, we obtainthe optimal decision strategies of those noise suppression methods for different computingenvironments and imaging requirements.Finally, we evaluate the imaging results, polarity reversal correction, angle gathers,and noise suppression through numerical examples. The results have shown theeffectiveness and adaptability of our methods.
Keywords/Search Tags:multicomponent joint, elastic reverse time migration, imaging condition, polarity reversal correction, noise suppression
PDF Full Text Request
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