PENGGUNAAN METODE INVERSI CONSTRAINED DIX UNTUK MEMPERBAIKI MODEL KECEPATAN DALAM PROSES MIGRASI KAWASAN KEDALAMAN DAN TOMOGRAFI SEISMIK

Velocity model is a very important parameter in imaging subsurface structures of seismic reflection method. Accurate velocity model will provide accurate subsurface information, otherwise if the velocity model is not appropriate, it will cause errors in interpretation. This study discussed the use o...

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Bibliographic Details
Main Authors: , BAGUS ARIFIANTO, , Sudarmaji, M.Si.
Format: Thesis
Published: [Yogyakarta] : Universitas Gadjah Mada 2014
Subjects:
ETD
Description
Summary:Velocity model is a very important parameter in imaging subsurface structures of seismic reflection method. Accurate velocity model will provide accurate subsurface information, otherwise if the velocity model is not appropriate, it will cause errors in interpretation. This study discussed the use of constrained dix inversion to improve the velocity model which is usually constructed using conventional dix transform (unconstrained dix inversion) for pre-stack depth migration (PSDM) and seismic tomography. Generally, the velocity model for PSDM was built by dix transform, there are still a lot of noise (the value of sticking velocity). Then, to improve the velocity model, we used constrained dix inversion method which utilizes the velocity trend misfit and damping mechanism to eliminate the noise, so we get a more stable of velocity model. The initial velocity model is used to pre-stack depth migration, using Kirchhoff migration method. If seismic section in depth domain is not matched with the depth of well marker, then the velocity model will be fixed again using model based tomography method. Interval velocity results of constrained dix inversion can minimize the noise from dix transform results. Seismic section result of pre-stack depth migration using the velocity model from constrained dix inversion as input, gives better results than migration using the velocity model from conventional dix transform as input.