Estimation of seafloor reflectivity in shallow water based on seismic data of sparker sources

Zheng, Jianglong; Xu, Jian; Tong, Siyou; Huang, Yarong; Zhou, Huawei

doi:10.1007/s11001-021-09456-8

Cited by 2 publications

(2 citation statements)

References 47 publications

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“…Figure 5b illustrates the reflection amplitudes of the cavity and seabed extracted from the sub-bottom profile. Consequently, by analyzing the arrival time and amplitude of the cavity reflection wave and the seabed reflection wave, it is possible to calculate the seabed reflection coefficient (R S ) using the following formula [53]:…”

Section: Methods 221 Calculation Of Seabed Reflection Coefficientmentioning

confidence: 99%

Inversion of Sub-Bottom Profile Based on the Sediment Acoustic Empirical Relationship in the Northern South China Sea

Zhou,

Li,

Zheng

et al. 2024

Remote Sensing

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This study focuses on the inversion of sub-bottom profile (SBP) data in the northern South China Sea using an empirical relationship derived from sediment acoustic data. The sub-bottom profile is primarily utilized for various marine applications, such as geological mapping and resource exploration. In this research, we present a study conducted in the northern slope canyon of the South China Sea. Firstly, we obtained the seabed reflection coefficient from sub-bottom profiles obtained by the autonomous underwater vehicle (AUV) detection system. Secondly, we utilized the acoustic empirical relationship in the northern South China Sea to establish relationship equations between the seabed reflection coefficient and the porosity, density, and average particle size of the sediment at a main frequency of 4 kHz (the AUV shallow profile main frequency). Then, using these equations, we were able to invert the physical parameters such as porosity, density, and average particle size of the seabed surface sediments. Finally, the inverted results are compared and analyzed by using the sediment samples test data. The overall deviation rate of the inverted physical parameters is within the range of ±10% when compared. The inverted results closely match the measured values, accurately reflecting the dynamic changes in the physical properties of seabed surface sediments. Notably, the average grain size is a direct indicator of the sediment particles size with smaller particles found in deeper water. The variation characteristics of sediment physical parameters align well with the variation of sediment types in the canyon, which is consistent with changes in the water depth, topography, and hydrodynamic conditions of the area. This further demonstrates the reliability of the inversion results.

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Section: Methods 221 Calculation Of Seabed Reflection Coefficientmentioning

confidence: 99%

Inversion of Sub-Bottom Profile Based on the Sediment Acoustic Empirical Relationship in the Northern South China Sea

Zhou,

Li,

Zheng

et al. 2024

Remote Sensing

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“…They can be safely and effectively used even in relatively shallow waters and on small vessels. Particularly in marine geoengineering studies in shallow shelf areas, these systems are highly suitable for high-resolution site surveys, revealing potential geo-hazards such as faults, shallow gas accumulations, and landslides (e.g., Trabant 1984; Huang et al 2015; Zheng et al 2021). However, limitations in very shallow waters and the substantial in uence of sea conditions on data quality constitute signi cant drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Optimal Processing of Single-Channel Sparker Marine Seismic Data

Nasif

2023

Preprint

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Single-channel sparker seismic reflection systems are currently preferred in offshore geo-engineering studies due to their cost-effectiveness, ease of use in shallow areas, their high-resolution data, and straightforward data processing. However, the distinctive characteristics of sparker data introduce specific challenges in the processing of single-channel seismic datasets. These include (i) unavailability of the stacking process for single-channel seismic data, which is highly effective in eliminating random and coherent noise, (ii) inability to derive subsurface velocity distribution from single-channel seismic profiles, (iii) limitations imposed by ghost reflections and bubble effects as well as random noise amplitudes, and (iv) the suitability of only predictive deconvolution for suppressing multiple reflections. Applications demonstrate that the inability to apply the stacking process to single-channel seismic data poses a significant challenge in suppressing both random and coherent noise, and increasing the signal-to-noise (S/N) ratio. The F-X prediction filter has proven highly effective in mitigating random noise in sparker data. Appropriate determination of operator length and prediction lag parameters allows predictive deconvolution to effectively suppress multiple reflections, despite some residual multiple amplitudes in the output. Spiking deconvolution significantly eliminates ghost reflections and bubble effects, enhancing temporal resolution by eliminating the ringy appearance of the input signal. However, transforming the source wavelet to its minimum phase or zero phase equivalent is essential prior to deconvolution. Trace mixing is a crucial data processing step for enhancing sparker data resolution. The method can be applied as weighted mix for random noise suppression or as trimmed mix for suppressing high-amplitude spike-like noises.

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Estimation of seafloor reflectivity in shallow water based on seismic data of sparker sources

Cited by 2 publications

References 47 publications

Inversion of Sub-Bottom Profile Based on the Sediment Acoustic Empirical Relationship in the Northern South China Sea

Inversion of Sub-Bottom Profile Based on the Sediment Acoustic Empirical Relationship in the Northern South China Sea

Optimal Processing of Single-Channel Sparker Marine Seismic Data

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