Estimating the Shear-Wave Velocities of Shallow Sediments in the Yellow Sea Using Ocean-Bottom-Seismometer Multicomponent Scholte-Wave Data

Abstract

Scholte-wave dispersion analysis is effective at imaging the relatively low shear-wave velocity of shallow marine sediments in marginal seas. The combination of a four-component ocean-bottom-seismometer (OBS) and a towed air-gun source can economically and effectively acquire the marine dispersive seismic data. Extracting higher-order dispersive Scholte wave modes is the most critical problem in the dispersion analysis method. The extremely low shear-wave velocity and severe attenuation in the top hundreds of meters of marginal sea sediment provide an uneven dispersive energy distribution for the four components of the Scholte wave data. The fundamental mode dispersive energy dominates in the vertical component and higher-order modes dominate in the horizontal component. We developed the method of the four-component OBS Scholte velocity-spectra stacking, which can effectively, rapidly, and robustly extract higher-order modes. We imaged the shear-wave velocity structure of complicated shallow marine sediment in the North Yellow Sea using an active OBS seismic profile with a large-volume air-gun array. The fourth higher-order Scholte wave mode can be imaged with the four-component velocity-spectra stacking method with a lower frequency range of 1.0–7.0 Hz. Only the second-order mode can be recognized from the dispersion energy image of the single vertical component. The joint inversion of multimode dispersion curves can provide more accuracy and deeper constraints for the inverted model; thus, the constraint depth with five modes increases by a factor of 1.9 compared with single fundamental mode inversion. The inverted profile suggests a low shear-wave velocity of 123–670 m/s and strong lateral variations within 350 m. The main regional geological structures are shown by the inverted shear-wave velocity structure.

Challenges in submarine fiber-optic earthquake monitoring

“…Frequencies above 0.2 Hz are dominated by slow-moving Scholte waves (Scholte, 1958) with speeds between 200 m/s and 600 m/s. Other studies with submarine DAS cable setups have observed similarly slow Scholte waves traveling along the cable, some of which induced by strong earthquakes (Spica et al, 2020;Wang et al, 2022;Cheng et al, 2021;Williams et al, 2021Williams et al, , 2019. The symmetry of the secondary microseismic signal in the f -k domain plot in Figure 3 suggests that the Scholte waves travel with equal strength in both directions along the cable.…”

Electroseismic Scholte‐wave analysis: A potential method for estimating shear‐wave velocity structure of shallow‐water seabed sediments

“…To simplify the inversion problem, we assume the true thicknesses for each layer beneath the seafloor and then update the P‐wave velocity, density and shear‐wave velocities of seabed sediments in each iteration (Bohlen et al., 2004; Wang et al., 2022). The minimum and maximum shear‐wave velocities of each layer from the 20 initial models are adopted as the velocity bounds for the V s model updates in the inversion (refer to the grey dashed lines in Figure 8c or f).…”

“…In marine surface wave exploration, the seismic v z field is also commonly employed to image the dispersion spectrum for extracting Scholte-wave dispersion curves (e.g. Kugler et al, 2005Kugler et al, , 2007Ritzwoller & Levshin, 2002;Wang et al, 2022). Hence, it is crucial to compare the frequency-velocity spectrum of the ES pressure field with that of the seismic v z field to assess the potential for extracting more overtones from it.…”

Shear Wave Velocity Estimation by Deep Reinforcement Learning: A Case Study

“…Knowledge of shear-wave velocity in marine sediments is important for geotechnical applications and exploration engineering [2,3]. In general, the shear wave velocity profile in the seabed can be estimated by inverting the dispersion curves of the seismic interface waves (i.e., Scholte waves in this paper) [4]. The conventional approach for geoacoustic inversion is the optimization-based approach, which exploits an optimization method to iteratively search in the high-dimensional parameter space and find out the solution best fits the observed data [5].…”