越南西北部Phan Si Pan地区Ngoi Chi岩组的锆石U-Pb年龄和Hf同位素组成

本文对越南西北部Phan Si Pan地区变质地体中的一个Ngoi Chi片麻岩进行了锆石CL内部结构分析、LA-(MC)-ICP-MS锆石U-Pb定年和Hf同位素分析。CL图象和Th/U比值特征显示该片麻岩样品中的锆石主要为岩浆锆石,有少量窄的变质边。岩浆锆石的年龄为~2.9 Ga,表明该样品是越南西北部Phan Si Pan地区的基底岩石。它们的εHf(t)值为–4.70±0.92,二阶段Hf模式年龄为~3.5 Ga,表明其为更古老的(3.5 Ga)冥太古代地壳物质部分熔融作用形成。变质边部锆石给出了~1.8 Ga的年龄,表明变质作用发生在古元古代早期,Phan Si Pan地区在这一时期可能经历了一次重要的构造热事件。     

Effects of riverbed incision on the hydrology of the Vietnamese Mekong Delta

The hydrogeomorphology of the Vietnamese Mekong Delta (VMD) has been significantly altered by natural and anthropogenic drivers. In this study, the spatiotemporal changes of the flow regime were examined by analysing the long-term daily, monthly, annual and extreme discharges and water levels from 1980 to 2018, supported by further investigation of the long-term annual sediment load (from the 1960s to 2015), river bathymetric data (in 1998, 2014 and 2017) and daily salinity concentration (from the 1990s to 2015) using various statistical methods and a coupled numerical model. Then, the effects of riverbed incision on the hydrology were investigated. The results show that the dry season discharge (i.e., in March–June) of the Tien River increased by up to 23% from the predam period (1980–1992) to the postdam period (1993–2018) but that the dry season water level at My Thuan decreased by up to −46%. The annual mean and monthly water levels in June at Tan Chau and in January and June–October at My Thuan in the Tien River decreased statistically, even though the respective discharges increased significantly. These decreased water levels instead of the increased discharges were attributed to the accelerated riverbed incision upstream from My Thuan, which increased by more than three times, from a mean rate of −0.16 m/year (−16.7 Mm³/year) in 1998–2014 to −0.5 m/year (−52.5 Mm³/year) in 2014–2017. This accelerated riverbed incision was likely caused by the reduction in the sediment load of the VMD (from 166.7 Mt/year in the predam period to 57.6 Mt/year in the postdam period) and increase in sand mining (from 3.9 Mm³ in 2012 to 13.43 Mm³ in 2018). Collectively, the decreased dry season water level in the Tien River is likely one of the main causes of the enhanced salinity intrusion.     

Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed,China

Natural Hazards - This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow...     

Integrated Geological–Geophysical and Oceanographic Research in the South China Sea: Cruise 88 of the R/V “Akademik M.A. Lavrentyev”

Oceanology - Abstract—The paper gives brief results of comprehensive studies in the South China Sea obtained from a joint Russian–Vietnamese expedition in November 2019 (cruise 88 of...     

Ionosphere probing with simultaneous GNSS radio occultations

GPS Solutions - Radio occultation (RO) is a powerful technique for providing vertical profiles of refractivity, temperature, pressure, and water vapor of the neutral atmosphere and electron density...     

Seismic ground-roll noise attenuation using deep learning

We propose to adopt a deep learning based framework using generative adversarial networks for ground-roll attenuation in land seismic data. Accounting for the non-stationary properties of seismic data and the associated ground-roll noise, we create training labels using local time–frequency transform and regularized non-stationary regression. The basic idea is to train the network using a few shot gathers such that the network can learn the weights associated with noise attenuation for the training shot gathers. We then apply the learned weights to test ground-roll attenuation on shot gathers, that are not a part of training input to obtain the desired signal. This approach gives results similar to local time–frequency transform and regularized non-stationary regression but at a significantly reduced computational cost. The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Tests on field-data examples verify the effectiveness of the proposed approach.     

The effect of long-term aerial exposure on intertidal mudflat erodibility

Intertidal zones by definition are exposed to air at low tide, and the exposure duration can be weeks (e.g. during neap tides) depending on water level and bed elevation. Here we investigated the effect of varying exposure duration (6 h to 10 days) on intertidal mudflat erosion (measured using the EROMES device), where the effects of water content and biofilm biomass (using chlorophyll-a content as a proxy, Chl-a μg g⁻¹) were taken into account. Sediments were collected between spring and summer (in October 2018, January 2019 and February 2019) from an intertidal site in the Firth of Thames, New Zealand. Longer exposure duration resulted in more stable sediments [higher erosion threshold (Ƭ_cr, N m⁻²) and lower erosion rate (ER, g m⁻² s⁻¹)]. After 10 days, exposure increased Ƭ_cr by 1.7 to 4.4 times and decreased ER by 11.6 to 21.5 times compared with 6 h of exposure. Chl-a and water content changed with exposure duration and were significantly correlated with changes in Ƭ_cr and ER. The stability of sediments after two re-submersion periods following exposure was also examined and showed that the stabilizing effect of exposure persisted even though water content had increased to non-exposure levels. Re-submersion was associated with an increase in Chl-a content, which likely counteracted the destabilizing influence of increased water content. A site-specific model, which included the interplay between evaporation and biofilm biomass, was developed to predict water content as a function of exposure duration. The modelled water content (W_Mod.) explained 98% of the observed variation in water content (W_Obs.). These results highlight how the exposure period can cause subtle changes to erosion regimes of sediments. An understanding of these effects (e.g. in sediment transport modelling) is critical to predicting the resilience of intertidal zones into the future, when sea-level rise is believed to exacerbate erosion in low-lying areas. © 2020 John Wiley & Sons, Ltd.     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

Modeling undertow due to random waves

A numerical model of undertow due to random waves is developed. The model includes three sub-models: (i) a model for multi-directional and multi-frequency random wave transformation, (ii) a surface roller evolution model, and (iii) a model for calculating the vertical distribution and the mean value of the undertow velocity. The calculation of wave trough level is performed based on a theory for the wave asymmetry. The model was successfully validated against small- and large-scale laboratory experiments. Thus, the model is expected to provide reliable input for the modeling of sediment transport and morphological change due to waves and currents.     

FEM × DEM modelling of cohesive granular materials: Numerical homogenisation and multi-scale simulations

The article presents a multi-scale modelling approach of cohesive granular materials, its numerical implementation and its results. At microscopic level, Discrete Element Method (DEM) is used to model dense grains packing. At the macroscopic level, the numerical solution is obtained by a Finite Element Method (FEM). In order to bridge the micro- and macro-scales, the concept of Representative Elementary Volume (REV) is applied, in which the average REV stress and the consistent tangent operators are obtained in each macroscopic integration point as the results of DEM’s simulation. In this way, the numerical constitutive law is determined through the detailed modelling of the microstructure, taking into account the nature of granular materials. We first elaborate the principle of the computation homogenisation (FEM × DEM), then demonstrate the features of our multiscale computation in terms of a biaxial compression test. Macroscopic strain location is observed and discussed.