Origin of sulphate in the unsaturated zone and groundwater of a loess aquifer

The use of the sulphate mass balance (SMB) between precipitation and soil water as a supplementary method to estimate the diffuse recharge rate assumes that the sulphate in soil water originated entirely from atmospheric deposition; however, the origin of sulphate in soil and groundwater is often unclear, especially in loess aquifers. This study analysed the sulphur (δ³⁴S-SO₄) and oxygen (δ¹⁸O-SO₄) isotopes of sulphate in precipitation, water-extractable soil water, and shallow groundwater samples and used these data along with hydrochemical data to determine the sources of sulphate in the thick unsaturated zone and groundwater of a loess aquifer. The results suggest that sulphate in groundwater mainly originated from old precipitation. When precipitation percolates through the unsaturated zone to recharge groundwater, sulphates were rarely dissolved due to the formation of CaCO₃ film on the surface of sulphate minerals. The water-extractable sulphate in the deep unsaturated zone (>10 m) was mainly derived from the dissolution of evaporite minerals and there was no oxidation of sulphide minerals during the extraction of soil water by elutriating soil samples with deionized water. The water-extractable concentration of SO₄ was not representative of the actual SO₄ concentration in mobile soil water. Therefore, the recharge rate cannot be estimated by the SMB method using the water-extractable concentration of SO₄ in the loess areas. This study is important for identifying sulphate sources and clarifying the proper method for estimating the recharge rate in loess aquifers.     

On the linear elastic responses of the 2D bonded discrete element model

The bonded discrete element model (DEM) is a numerical tool that is becoming widely used when studying fracturing, fragmentation, and failure of solids in various disciplines. However, its abilities to solve elastic problems are usually overlooked. In this work, the main features of the 2D bonded DEM which influence Poisson's ratio and Young's modulus, and accuracy when solving elastic boundary value problems, are investigated. Outputs of numerical simulations using the 2D bonded DEM, the finite element method, a hyper elasticity analysis, and the distinct lattice spring model (DLSM) are compared in the investigation. It is shown that a shear interaction (local) factor and a geometric (global) factor are two essential elements for the 2D bonded DEM to reproduce a full range of Poisson's ratios. It is also found that the 2D bonded DEM might be unable to reproduce the correct displacements for elastic boundary value problems when the represented Poisson's ratio is close to 0.5 or the long-range interaction is considered. In addition, an analytical relationship between the shear stiffness ratio and the Poisson's ratio, derived from a hyper elasticity analysis and applicable to discontinuum-based models, provides good agreement with outputs from the 2D bonded DEM and DLSM. Finally, it is shown that the selection of elastic parameters used the 2D bonded DEM has a significant effect on fracturing and fragment patterns of solids.     

Modelling the time‐dependent behaviour of granular material with hypoplasticity

This paper presents a constitutive model for time‐dependent behaviour of granular material. The model consists of 2 parts representing the inviscid and viscous behaviour of granular materials. The inviscid part is a rate‐independent hypoplastic constitutive model. The viscous part is represented by a rheological model, which contains a high‐order term denoting the strain acceleration. The proposed model is validated by simulating some element tests on granular soils. Our model is able to model not only the non‐isotach behaviour but also the 3 creep stages, namely, primary, secondary, and tertiary creep, in a unified way.     

Inference of in situ stress from thermoporoelastic borehole breakouts based on artificial neural network

The determination of in situ stresses is very important in petroleum engineering. Hydraulic fracturing is a widely accepted technique for the determination of in situ stresses nowadays. Unfortunately, the hydraulic fracturing test is time-consuming and expensive. Taking advantage of the shape of borehole breakouts measured from widely available caliper and image logs to determine in situ stress in petroleum engineering is highly attractive. By finite element modeling of borehole breakouts considering thermoporoelasticity, the authors simulate the process of borehole breakouts in terms of initiation, development, and stabilization under Mogi-Coulomb criterion and end up with the shape of borehole breakouts. Artificial neural network provides such a tool to establish the relationship between in situ stress and shape of borehole breakouts, which can be used to determine in situ stress based on different shape of borehole breakouts by inverse analysis. In this paper, two steps are taken to determine in situ stress by inverse analysis. First, sets of finite element modeling provide sets of data on in situ stress and borehole breakout measures considering the influence of drilling fluid temperature and pore pressure, which will be used to train an artificial neural network that can eventually represent the relationship between the in situ stress and borehole breakout measures. Second, for a given measure of borehole breakouts in a certain drilling fluid temperature, the trained artificial neural network will be used to predict the corresponding in situ stress. Results of numerical experiments show that the inverse analysis based on finite element modeling of borehole breakouts and artificial neural network is a promising method to determine in situ stress.     

Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models

Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.

     

Geochemical characteristics of hadal sediment in the northern Yap Trench

Journal of Oceanology and Limnology - Two sediment cores were retrieved in the hadal zone of the Yap Trench, and their concentrations of six major elements Mg, Al, Ca, Ti, Mn and Fe and nine trace...     

The Characteristics of Storm Wave Behavior and Its Effect on Cage Culture Using the ADCIRC+SWAN Model in Houshui Bay,China

The current storm wave hazard assessment tends to rely on a statistical method using wave models and fewer historical data which do not consider the effects of tidal and storm surge.In this paper,the wave-current coupled model ADCIRC+SWAN was used to hindcast storm events in the last 30 years.We simulated storm wave on the basis of a large set of historical storms in the North-West Pacific Basin between 1985 and 2015 in Houshui Bay using the wave-current coupled model ADCIRC+SWAN to obtain the storm wave level maps.The results were used for the statistical analysis of the maximum significant wave heights in Houshui Bay and the behavior of wave associated with storm track.Comparisons made between observations and simulated results during typhoon Rammasun(2014)indicate agreement.In addition,results demonstrate that significant wave height in Houshui Bay is dominated by the storm wind velocity and the storm track.Two groups of synthetic storm tracks were designed to further investigate the worst case of typhoon scenarios.The storm wave analysis method developed for the Houshui Bay is significant in assisting government's decision-making in rational planning of deep sea net-cage culture.The method can be applied to other bays in the Hainan Island as well.     

Laboratory Investigation into the Evaporation of Natural-Gas Condensate Oils: Hints for the Sanchi Oil Spill

The Sanchi oil tanker collision in the East China Sea on January 6th, 2018 has caused worldwide attention due to its uniqueness. A considerable amount of h     

关于发展人工影响天气数值模式的一些问题

人工影响天气的学科基础是中小尺度天气动力学与云降水物理学,需要将天气-动力-云降水物理耦合为一体。考虑到目前将天气动力学性质的基础数值模式用于人工影响天气中的问题,从数值模式动力方程、模式分辨率、云物理过程、数值求解方案、初边值条件等方面系统地探索了发展人工影响天气数值模式中一些需要重点解决、且不可忽视的特色问题,并举例对相关问题提出了解决思路和方法。期望提出的问题有助于构思更适合于人工影响天气数值模式,使数值模式功能真正向满足人工影响天气的要求靠近一步。