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Spectral recomposition splits a seismic spectrum into Ricker components. It provides a tool for imaging and mapping temporal bed thicknesses and geologic discontinuities. I propose an application of separable, nonlinear, least-squares estimation in spectral recomposition. Employing the Gauss-Newton method, this approach estimates fundamental signal parameters such as peak frequencies and amplitudes. I applied spectral recomposition to multicomponent seismic data, which provides new perspectives of seismic attributes and multicomponent data interpretation. Correlating S-wave reflection with P -wave reflection is one of the very first steps in multicomponent data interpretation. In a given stratigraphic interval of a geologic section, registration correlates P and S-wave profiles to determine ts /tp ratios, which are equivalent to Vp /Vs ratios for vertical propagation paths. The registration process is largely driven by the availability of dipole sonic logs. However, dipole sonic logs are not as common as standard sonic logs and tend to be affected by various borehole factors. Therefore, new techniques are needed for accurate P P and P S correlation and registration. Assuming P P and P S reflection events have been correctly positioned laterally in migrated images, and the difference between P P wave image and P S wave image can be explained only by vertical transformation, I adopt a multistep approach to register PP and PS images automatically. Setting PP time as a coordinate system, I was able to squeeze P S traces accordingly while keeping the signal pattern of P S wave data. Local seismic attributes, such as the local similarity, help improve registration accuracy.

Spectral recomposition splits a seismic spectrum into Ricker components. It provides a tool for imaging and mapping temporal bed thicknesses and geologic discontinuities. I propose an application of separable, nonlinear, least-squares estimation in spectral recomposition. Employing the Gauss-Newton method, this approach estimates fundamental signal parameters such as peak frequencies and amplitudes. I applied spectral recomposition to multicomponent seismic data, which provides new perspectives of seismic attributes and multicomponent data interpretation. Correlating S-wave reflection with P -wave reflection is one of the very first steps in multicomponent data interpretation. In a given stratigraphic interval of a geologic section, registration correlates P and S-wave profiles to determine ts /tp ratios, which are equivalent to Vp /Vs ratios for vertical propagation paths. The registration process is largely driven by the availability of dipole sonic logs. However, dipole sonic logs are not as common as standard sonic logs and tend to be affected by various borehole factors. Therefore, new techniques are needed for accurate P P and P S correlation and registration. Assuming P P and P S reflection events have been correctly positioned laterally in migrated images, and the difference between P P wave image and P S wave image can be explained only by vertical transformation, I adopt a multistep approach to register PP and PS images automatically. Setting PP time as a coordinate system, I was able to squeeze P S traces accordingly while keeping the signal pattern of P S wave data. Local seismic attributes, such as the local similarity, help improve registration accuracy.

Various climate models have been developed to analyze and predict climate change; however, model uncertainties cannot be easily overcome. A statistical approach has been presented in this paper to calculate the distributions of future climate change based on an ensemble of the Weather Research and Forecasting (WRF) models. Wavelet analysis has been adopted to de-noise the WRF model output. Using the de-noised model output, we carry out Bayesian analysis to decrease uncertainties in model CAM_KF, RRTM_KF and RRTM_GRELL for each downscaling region.