Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-02-20T22:47:58Z No. of bitstreams: 1 OdmaksuelAnisioBezerraDantas_DISSERT.pdf: 5356247 bytes, checksum: a2a17290abc4832c7f0cd9ef68881d06 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-02-24T22:03:21Z (GMT) No. of bitstreams: 1 OdmaksuelAnisioBezerraDantas_DISSERT.pdf: 5356247 bytes, checksum: a2a17290abc4832c7f0cd9ef68881d06 (MD5) / Made available in DSpace on 2017-02-24T22:03:21Z (GMT). No. of bitstreams: 1 OdmaksuelAnisioBezerraDantas_DISSERT.pdf: 5356247 bytes, checksum: a2a17290abc4832c7f0cd9ef68881d06 (MD5) Previous issue date: 2016-08-15 / Os sinais ruidosos registrados durante o monitoramento de opera??es de fraturamento hidr?ulico num campo de petr?leo podem trazer informa??es importantes sobre a estrutura do subsolo. Tais informa??es s?o extra?das atrav?s de um conjunto de procedimentos de an?lise e processamento de dados, baseado na t?cnica de Interferometria S?smica de Ru?do Ambiente (ISRA). Na pr?tica, a t?cnica envolve a realiza??o de duas etapas: a correla??o cruzada, que ? equivalente a convolu??o de um dos sinais com o outro reverso no tempo, e o empilhamento (stacking) dos resultados gerados. A resposta encontrada pela mesma ? equivalente a Fun??o de Green emp?rica do meio convolvida com a wavelet da fonte e por esse motivo, a Interferometria S?smica tamb?m ? chamada de recupera??o da Fun??o de Green. Neste trabalho, novas respostas s?smicas foram obtidas atrav?s da combina??o de duas t?cnicas de correla??o cruzada (correla??o cruzada cl?ssica normalizada geometricamente - CCGN e correla??o cruzada de fase - PCC) com duas t?cnicas de empilhamento (empilhamento linear - LS e o empilhamento n?o linear ou ponderado por fase - PWS). Consequentemente, quatro abordagens foram alcan?adas, que ap?s submetidas a um fluxo de processamento padr?o de dados s?smicos resultou em quatro se??es s?smicas empilhadas (LS-PCC - empilhamento linear com correla??o cruzada de fase, LS-CCGN - empilhamento linear com correla??o cruzada cl?ssica normalizada geometricamente, PWS-PCC - empilhamento n?o linear com correla??o cruzada de fase e PWS-CCGN - empilhamento n?o linear com correla??o cruzada cl?ssica normalizada geometricamente). Para interpreta??o dos resultados, uma modelagem s?smica direta foi realizada a fim de obter uma se??o s?smica sint?tica. A interpreta??o dos resultados com o uso de informa??es de dados sint?ticos e da geologia mostrou que alguns eventos correspondentes a marcadores geol?gicos foram recuperados. Isto contribuiu para a comprova??o de que ? poss?vel recuperar as reflex?es de um meio em subsuperf?cie utilizando registros de ru?do s?smico ambiente e a t?cnica de Interferometria S?smica. / The noisy signals recorded during the monitoring hydraulic fracturing operations in an oil field can provide important information on the structure of the subsoil. Such information is extracted through a set of procedures for analyzing and processing data, based on the technique of of Ambient Noise Interferometry Seismic (ANSI). In practice, the technique involves the realization of two steps: the cross-correlation, which is equivalent to a convolution of the signals with each other in reverse time and the stacking the results generated. The answer is found by this is equivalent to empirical Green function convolved the medium of the source wavelet and therefore, the seismic interferometry recovery is also called the Green function. In this work, new seismic responses were obtained by combining two cross-correlation techniques (classical cross-correlation geometrically normalized - CCGN and phase cross-correlation - PCC) with two stacking techniques (linear stack - LS and the nonlinear stack or phase-weighting stack - PWS). Consequently, four approaches have been reached which, after undergoing a standard processing flow of seismic data resulted in four stacked seismic sections (LS-PCC - linear stack with phase cross-correlation, TS-CCGN - linear stack with classical cross-correlation geometrically normalized, PWS-PCC - nonlinear stack with phase cross-correlation and PWS-CCGN - nonlinear stack with classical cross-correlation geometrically normalized). To interpret the results, a direct seismic modeling was performed to obtain a synthetic seismic section. Interpretation of the results with the use of synthetic data information and geology showed that some events corresponding to geological markers were recovered. This adds to the confirmation that is possivel retrieve the reflections of an environment in the subsurface using ambient seismic noise records and seismic interferometry technique.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-03-02T23:01:37Z No. of bitstreams: 1 RafaelaCarreiroDias_DISSERT.pdf: 7493649 bytes, checksum: 18d66dcedc96371bd5f40830e9aec1ba (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-03-04T21:54:12Z (GMT) No. of bitstreams: 1 RafaelaCarreiroDias_DISSERT.pdf: 7493649 bytes, checksum: 18d66dcedc96371bd5f40830e9aec1ba (MD5) / Made available in DSpace on 2016-03-04T21:54:12Z (GMT). No. of bitstreams: 1 RafaelaCarreiroDias_DISSERT.pdf: 7493649 bytes, checksum: 18d66dcedc96371bd5f40830e9aec1ba (MD5) Previous issue date: 2014-03-06 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico - CNPq / O ru?do s?smico tem sido tradicionalmente considerado como uma perturba??o n?o desejada do ambiente que ?contamina? a aquisi??o de dados de terremotos. Mas ao longo da ?ltima d?cada tem sido mostrado que informa??es coerentes sobre a estrutura do subsolo podem ser extra?das a partir de correla??es cruzadas do ru?do s?smico de ambiente. Neste contexto, as regras s?o reversas, sendo os terremotos o que necessitamos excluir dos dados. Os principais causadores do ru?do s?smico de ambiente s?o os microssismos oce?nicos e perturba??es atmosf?ricas. A per?odos menores que 30 s, o espectro do ru?do s?smico de ambiente ? dominado por energia micross?smica. O microssismo ? o sinal s?smico mais cont?nuo da Terra e pode ser classificado como prim?rio (observado na faixa 10-20 s) e secund?rio (observado na faixa 5-10 s). A fun??o de Green do meio de propaga??o entre dois receptores pode ser reconstru?da atrav?s da correla??o cruzada do ru?do s?smico de ambiente registrado simultaneamente nesses dois receptores. A reconstru??o da fun??o de Green ? geralmente proporcional ? por??o de ondas de superf?cie do campo de onda s?smico, j? que a energia micross?smica viaja principalmente como ondas de superf?cie. Neste trabalho, s?o apresentadas 194 fun??es de Green obtidas a partir de correla??es cruzadas de 1 m?s de registro da componente vertical do ru?do s?smico de ambiente para diferentes pares de esta??es s?smicas do Nordeste do Brasil. As correla??es cruzadas di?rias foram empilhadas utilizando a t?cnica n?o linear tf-PWS que real?a sinais coerentes fracos atrav?s da redu??o de ru?do incoerente. As correla??es cruzadas mostram que o sinal emergido ? dominado por ondas Rayleigh nas componentes verticais e que as velocidades de dispers?o podem ser medidas confiavelmente para uma faixa de per?odos entre 5 e 20 s. O estudo inclui tanto esta??es permanentes para monitoramento s?smico, quanto esta??es tempor?rias de experimentos passivos na regi?o, formando uma rede combinada de 33 esta??es separadas por dist?ncias entre 60 e 1311 km, aproximadamente. Estas medidas de velocidades de dispers?o de ondas Rayleigh em seguida s?o usadas na elabora??o de imagens tomogr?ficas da Prov?ncia Borborema do Nordeste do Brasil. As tomografias de ru?do s?smico obtidas aqui permitem mapear satisfatoriamente fei??es estruturais existentes na regi?o. As imagens tomogr?ficas de per?odos mais curtos (~5 s) mostram a estrutura crustal rasa e claramente definem as bacias sedimentares marginais e intracontinentais, bem como as partes de zonas de cisalhamento importantes que atravessam a Prov?ncia Borborema. As imagens tomogr?ficas de per?odos mais longos (10 - 20 s) atingem profundidades da crosta superior e a maior parte das anomalias desaparece. Algumas delas localizada no interior da Prov?ncia Borborema, no entanto, persistem. A evolu??o Cenoz?ica da Prov?ncia Borborema foi marcada por epis?dios de vulcanismo Cenoz?ico e eleva??o, mas nenhuma correla??o ? observada com estas caracter?sticas Cenoz?icas e as anomalias profundas. As anomalias n?o se correlacionam com mapas dispon?veis de fluxo de calor superficial na Prov?nica Borborema, e a origem das anomalias profundas permanece enigm?tica. / Ambient seismic noise has traditionally been considered as an unwanted perturbation in seismic data acquisition that "contaminates" the clean recording of earthquakes. Over the last decade, however, it has been demonstrated that consistent information about the subsurface structure can be extracted from cross-correlation of ambient seismic noise. In this context, the rules are reversed: the ambient seismic noise becomes the desired seismic signal, while earthquakes become the unwanted perturbation that needs to be removed. At periods lower than 30 s, the spectrum of ambient seismic noise is dominated by microseism, which originates from distant atmospheric perturbations over the oceans. The microsseism is the most continuous seismic signal and can be classified as primary ? when observed in the range 10-20 s ? and secondary ? when observed in the range 5-10 s. The Green?s function of the propagating medium between two receivers (seismic stations) can be reconstructed by cross-correlating seismic noise simultaneously recorded at the receivers. The reconstruction of the Green?s function is generally proportional to the surface-wave portion of the seismic wavefield, as microsseismic energy travels mostly as surface-waves. In this work, 194 Green?s functions obtained from stacking of one month of daily cross-correlations of ambient seismic noise recorded in the vertical component of several pairs of broadband seismic stations in Northeast Brazil are presented. The daily cross-correlations were stacked using a timefrequency, phase-weighted scheme that enhances weak coherent signals by reducing incoherent noise. The cross-correlations show that, as expected, the emerged signal is dominated by Rayleigh waves, with dispersion velocities being reliably measured for periods ranging between 5 and 20 s. Both permanent stations from a monitoring seismic network and temporary stations from past passive experiments in the region are considered, resulting in a combined network of 33 stations separated by distances between 60 and 1311 km, approximately. The Rayleigh-wave, dispersion velocity measurements are then used to develop tomographic images of group velocity variation for the Borborema Province of Northeast Brazil. The tomographic maps allow to satisfactorily map buried structural features in the region. At short periods (~5 s) the images reflect shallow crustal structure, clearly delineating intra-continental and marginal sedimentary basins, as well as portions of important shear zones traversing the Borborema Province. At longer periods (10 ? 20 s) the images are sensitive to deeper structure in the upper crust, and most of the shallower anomalies fade away. Interestingly, some of them do persist. The deep anomalies do not correlate with either the location of Cenozoic volcanism and uplift - which marked the evolution of the Borborema Province in the Cenozoic - or available maps of surface heat-flow, and the origin of the deep anomalies remains enigmatic.