• Economic and Environmental Geology
• /
• v.46 no.6
• /
• pp.485-494
• /
• 2013

$CO_2$ Monitoring, Mitigation and Verification (MMV) is the essential part in the Carbon Capture and Storage (CCS) project in order to assure the storage permanence economically and environmentally. In large-scale CCS projects in the world, the seismic time-lapse survey is a key technology for monitoring the behavior of injected $CO_2$. In this study, we developed a basic process procedure for 3-D seismic baseline data from the Aquistore project, Estevan, Canada. Major target formations of Aquistore CCS project are the Winnipeg and the Deadwood sandstone formations located between 1,800 and 1,900 ms in traveltime. The analysis of trace energy and similarity attributes of seismic data followed by spectral decomposition are carried out for the characterization of $CO_2$ injection zone. High trace energies are concentrated in the northern part of the survey area at 1,800 ms and in the southern part at 1,850 ms in traveltime. The sandstone dominant regions are well recognized with high reflectivity by the trace energy analysis. Similarity attributes show two structural discontinuities trending the NW-SE direction at the target depth. Spectral decomposition of 5, 20 and 40 Hz frequency contents discriminated the successive E-W depositional events at the center of the research area. Additional noise rejection and stratigraphic interpretation on the baseline data followed by applying appropriate imaging technique will be helpful to investigate the differences between baseline data and multi-vintage monitor data.

• Korean Journal of Optics and Photonics
• /
• v.18 no.6
• /
• pp.452-460
• /
• 2007

Ultra-low loss ZERODUR and fused silica mirrors were manufactured and their light scattering characteristics were investigated. For this purpose, ZERODUR and fused silica substrates were super-polished by the bowl feed method. The surface roughness were 0.292 ${\AA}$ and 0.326 ${\AA}$ in rms for ZERODUR and fused silica, respectively. To obtain the high reflectivity, 22 thin film layers of $SiO_2$ and $Ta_2O_5$ were deposited by Ion Beam Sputtering. The measured light scattering of ZERODUR and fused silica mirror were 30.9 ppm and 4.6 ppm, respectively. This shows that the substrate surface roughness is not the only parameter which determines the light scattering of the mirror. In order to investigate the mechanism for additional light scattering of the ZERODUR mirror, the surface roughness of the mirror was measured by AFM and was found to be 2.3 times higher than that of the fused silica mirror. It is believed that there is some mismatch at the interface between the substrate and the first thin film layer which leads to the increased mirror surface roughness. To clarify this, the contact angle measurements were performed by SEO 300A, based on the Giriflaco-Good-Fowkes-Young method. The fused silica substrates with 0.46 ${\AA}$ in its physical surface roughness shows lower contact angle than that of the ZERODUR substrate with 0.31 ${\AA}$. This indicates that the thin film surface roughness is determined by not only its surface roughness but also the surface energy of the substrate, which depends on the chemical composition or crystalline orientation of the materials. The surface energy of each substrate was calculated from a contact angle measurement, and it shows that the higher the surface energy of the substrate, the better the surface roughness of the thin film.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.695-713
• /
• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.