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CRS-based velocity-depth model building and migration – A real data example
Hendriyana A.a, Sule R.a, Jaya M.b, Adriansyah A.c
a Institute of Technology Bandung, India
b GFZ Potsdam, India
c Pertamina EPTC, India
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Processing sequences based on CRS-stack attributes has been employed for complex geological structures with the aims is to gain an improved stacked section quality, to use straightforward way to generate velocity-depth model and to take the advantages of minimum aperture Kirchhoff migration method. The CRS attributes which required during stacking could be utilised for further processing such as velocity model building and migration aperture determination. NIP-wave tomography that utilised CRS attributes offers promising, efficient and accurate way to derive velocity model because kinematic information picked from stacked section with improved signal-to-noise ratio. Besides that, CRS attributes can be used to predict stationary point and Fresnel zone width which helpful for migration process. Application of CRS-stack, NIP-wave tomography and minimum-aperture Kirchhoff migration to real data example has gained interesting results.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Geological structures,Kinematic information,Kirchhoff migration,Migration process,Section quality,Stationary points,Velocity model building,Velocity-depth model[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.3997/2214-4609.20149420[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]