Prediction for potential landslide zones using seismic amplitude in Liwan gas field, northern South China Sea

Li, Xishuang; Liu, Baohua; Liu, Lejun; Zheng, Jiewen; Zhou, Songwang; Zhou, Qing

doi:10.1007/s11802-017-3308-6

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…The water depth in the north slope canyon area ranges from 400 to 2500 m, and it is known for its abundant oil and gas resources [29][30][31]. This area is also prone to submarine landslide disasters, particularly in the submarine canyon area on the continental slope [32,33]. Additionally, the seabed sediments in the northern slope area of the South China Sea exhibit distinct zonation characteristics.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…The geological interpretation of sub-bottom profiles is based on echo characteristics and morphology. This technique is commonly used in preliminary geological investigations for offshore wind power site surveys, submarine routing pipeline laying, and other offshore engineering constructions due to its high detection resolution, fast and convenient nature, and low cost [8][9][10][11]. However, conventionally engineered geological exploration processes the sub-bottom profile in a simple manner, resulting in low data utilization, low interpretation accuracy and precision, and other issues [12,13].…”

Section: Introductionmentioning

confidence: 99%

Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis

Zhou

Liu

et al. 2023

Remote Sensing

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The classification of marine sediment based on acoustic data is crucial for various applications such as marine resource exploitation, marine engineering construction, and marine ecological environment maintenance. It serves as a valuable alternative to limited geological sampling. However, the accuracy of sediment classification is limited due to constraints in acoustic data detection methods, data quality, and classification techniques. To address this issue, this study proposes an automatic classification method for marine sediment using an improved U-convolutional neural network and K-means clustering algorithm. In the coding part, a spatial pyramid pool layer is introduced to fuse low-dimensional feature data of different scales with the features of each level of the corresponding coding layer. This fusion method enhances the accuracy of the constructed relationship between the physical property parameters of the seabed bottom. The K-means clustering algorithm is optimized through selecting the point at the density center as the initial clustering center during the initial clustering center selection stage. This approach solves the sensitivity problem of the initial clustering center of K-means, improves the edge extraction effect of sediment types, and enhances the classification accuracy of sediment types. To validate the proposed method, an application test is conducted in the Northern Slope area of the South China Sea. The mean grain size of sediments in the study area is predicted using the improved U-Net neural network and the seafloor reflection intensity of the sub-bottom profile. Compared to the standard U-Net network results, the mean grain size prediction results show an increase of 4.9% and 2.8%, respectively. The sediment with the predicted mean grain size is then classified using the K-means clustering algorithm, resulting in the division of five sediment types: gravelly sand, sand, silty sand, sandy silt, and clayey silt. These classifications align well with the South China Sea sediment type map. The findings of this study not only provide an important supplement to existing marine sediment classification methods but also contribute significantly to understanding the sedimentary environment and processes in the Northern Slope of the South China Sea.

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“…The results showed that the machine learning models outperformed the multivariate statistical model and that the SVM model was found to be ideal for the case study area. Li et al (2017) investigated the prediction of landslide zones using seismic amplitude in Liwan gas field in the northern part of the South China Sea based on statistical analysis results. The results indicated that the areas have a high potential for shallow landslides on slopes exceeding 15° when the thickness of the loose sediment exceeds 8 m. Chen et al (2017) studied on the prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratios, a generalized additive model, and SVM techniques.…”

Section: Introductionmentioning

confidence: 99%

Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran

2021

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Landslides can cause extensive damage, particularly those triggered by earthquakes. The current study used back propagation of an artificial neural network (ANN) to conduct risk studies on landslides in the area affected by the Manjil-Rudbar earthquake in Iran in 1990 (M = 7.7). Newmark displacement analysis was used to develop an earthquake-induced landslide hazard map for the blocks representing Chahar-Mahal and Chalkasar near the earthquake epicenter, an area of 309 km 2 . The input data included soil cohesion, soil friction angle, unit weight of soil, unit weight of water, distance from hypocenter, slope, and earthquake magnitude as effective parameters for landslide occurrence. The hazard map was compared with an inventory map and other research findings. The results indicated that the landslides predicted by ANN covered 50% of the inventory map of the study area (2088 of 4097 slide cells). The results of the current study suggest that the ANN method is relatively efficient for accurate prediction of landslides.

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Prediction for potential landslide zones using seismic amplitude in Liwan gas field, northern South China Sea

Cited by 9 publications

References 27 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

Classification of Marine Sediment in the Northern Slope of the South China Sea Based on Improved U-Net and K-Means Clustering Analysis

Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran

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