A seismic survey identified a basalt flow that could consist of cap rock of CO₂ storage beneath saline aquifer sediment in the Southern Continental Shelf of Korea. To determine the precise depth of the basalt flow, specific depth-domain data processing of migration velocity analysis (MVA) was applied to the seismic survey data. The accurate depth measurement of a target structure provides crucial information when storing and stabilizing injected CO₂ beneath basalt cap rock. Strong reflections of seismic amplitude at the volcanic mounds were adjusted from the time domain to the exact depth domain by the iterated velocity using MVA. The confidence of the updated velocity was verified by the horizontal alignment of seismic events sorted according to their common reflection point (CRP). The depth difference in volcanic mounds before and after MVA application ranged from 32.5 to 60 m along the vertical axis, showing the eruption shape on the strong-amplitude contour map in detail. The eruption shape of the top of volcanic mounds was verified with spatial continuity in 3D geological interpretation. The presented results provide suitable information that can be used to locate drilling sites and to prepare CO₂ injection. The geological model obtained from both time- and depth-domain processing can significantly influence the calculation of the storage volume and can be useful for history matching studies.

In this study, seismic data were acquired using Tamhae2 R/V to identify the subsurface fault structures in the Hupo basin. The seismic data were generated by the air-gun source (30 cu. in.). The source distance is 12.5 m, and the receiver distance is 6.25 m. The number of channels is 32. The offset range of the seismic data is 50 to 250 m. The data processing for short-offset seismic data is mainly applied with simple processing techniques such as frequency filter, trace editing, and velocity analysis in consideration of cost efficiency. However, these simple data processing techniques cannot accurately image complex subsurface structures because it is difficult to remove severe noise and water-bottom (WB) multiples effectively. Therefore, in order to accurately identify the geological structures, it is necessary to apply high-resolution signal processing techniques that can remove severe random noise and WB multiples included in raw seismic data. Severe noise was removed by applying data processing techniques such as a low-cut filter, trace editing, swell noise attenuation, and random noise attenuation. In addition, predictive deconvolution, SRME, and Radon filter were applied to effectively attenuate WB multiples that cause difficulties in geological interpretation. Finally, pre-stack Kirchhoff time migration was applied to more accurately image the subsurface structures. From the data processing results, we confirmed that the high-resolution signal processing techniques applied in this study greatly improved the signal-to-noise ratio of seismic data and effectively eliminated WB multiples.

In this study, seismic data were acquired using the research vessel Tamhae 2 (KIGAM) to investigate the complex geological structures of the Pohang offshore area in the East Sea. The acquisition setup included a 900-meter streamer with a receiver spacing of 12.5 meters. The seismic source had a volume of 1,254 cubic inches, with a source spacing of 25 meters and an offset range from 75 to 975 meters. To accurately image complex subsurface geological structures, various seismic data processing techniques were applied. These techniques aim to remove different types of noise present in raw seismic data and effectively attenuate multiples. In this study, noise removal was achieved through methods such as low-cut filtering, static correction, trace editing, swell noise attenuation, and random noise attenuation to enhance the signal-to-noise ratio. Additionally, techniques like SRME (Surface-Related Multiple Elimination), SRWEMR (Surface-Related Wave Equation Multiple Removal), predictive deconvolution, broadband de-ghost filtering, and parabolic Radon filtering were employed to eliminate WB (water bottom) multiples, which otherwise impede stratigraphic interpretation. Finally, Kirchhoff time migration was used to image the complex subsurface structures. We confirmed that the signal processing techniques applied in this study effectively removed noise and WB (water bottom) multiples. Additionally, signal processing enabled us to derive a more accurate velocity model. Through the final stack section generated with this velocity model, we could confirm that the complex subsurface structures were well imaged.

In order to obtain subsurface velocity for field seismic data, a time processing based on semblance velocity analysis has been performed so far. However, since the results of the time processing do not provide velocity information in the depth domain, it is difficult to know the exact subsurface velocity. In this study, to generate accurate velocity, the depth processing using the migration velocity analysis (MVA), which generates more reasonable subsurface velocity structure than the result from the time processing, is applied to the field marine seismic data obtained from Ulleung basin (offshore Korea). A marine seismic source is generated by air-gun (2,289 cu. in.). The long-offset (5.7 km) multichannel seismic (MCS) data were recorded by 456 receivers. The source and receiver spacings are 25 m and 12.5 m, respectively. The seismic survey line is about 168 L-km. The MVA workflow is composed of building a starting velocity model, sorting data to common offset gathers, Kirchhoff prestack depth migration (PSDM), sorting to common reflection point (CRP) gathers, picking residual moveout (RMO), and updating the velocity model. We repeatedly applied the MVA workflow until the remarkable events in the CRP gather were flat. From the results, we could confirm that the depth processing using MVA is successfully applied to field dataset and generates reasonable velocity structure in depth.