平庄盆地六家立井地区找铀目标层岩石学特征及其找矿意义

六家立井地区位于平庄盆地北缘,白垩系下统阜新组上段是该区砂岩型铀矿床目标层位,具有较好找铀前景。为深入了解该区砂体的岩石学特征,通过野外钻探工作、岩心编录、样品采集及岩矿测试方法,详细研究了目标层碎屑岩类型、碎屑岩成分、结构构造等岩石学方面特征。结果表明目标层碎屑岩成分成熟度和结构成熟度均较低,具有搬运距离短、沉积速度快的特点,其物源较单一,初步判断物源来自盆地东北部和西北部隆起带酸性花岗岩,同时也为铀成矿提供了良好的铀源。     

川南煤田龙潭组煤系泥页岩气与煤层气组合模式及合采前景分析

文中研究的上二叠统龙潭组煤层及其顶底板泥页岩含气性特征、组合模式及合采对煤系气勘探开发意义重大。为改变煤层气产业低产低效长周期的现状,实现综合效益的目标,将龙潭组作为目标层进行研究;简述了川南煤田龙潭组含气泥页岩与煤层的组合模式,并初步探讨了上述组合模式合探合采的必要性、条件和前景,并提出了开展综合勘探开采的建议。     

From micro scale to boundary value problem: using a micromechanically based model

Acta Geotechnica - A 3D multi-scale approach is presented to investigate the mechanical behavior of a macroscopic specimen consisting of a granular assembly, as a boundary value problem. The core...     

Identifying parameters of advanced soil models using an enhanced transitional Markov chain Monte Carlo method

Acta Geotechnica - Parameter identification using Bayesian approach with Markov Chain Monte Carlo (MCMC) has been verified only for certain conventional simple constitutive models up to now. This...     

700年以来冲绳海槽西南部粘土矿物来源及其对沉积环境的响应

利用X射线衍射（XRD）技术对冲绳海槽南部HOBAB4-S2站700年以来的黏土矿物组成以及结晶学特征进行了研究和分析,并对该区的沉积特征、物质来源以及黏土矿物所记录的东亚季风的演化历史进行了探讨。研究发现冲绳海槽南部HOBAB4-S2站黏土矿物组合总体是以伊利石（59–77 %,平均含量69 %）和绿泥石（11–17 %,平均含量14 %）为主,而蒙脱石（5–23 %,平均含量12 %）和高岭石含量（2–6 %,平均含量4 %）则相对较低。通过黏土矿物研究对物源进行分析,发现冲绳海槽南部研究站位的黏土矿物中,蒙脱石主要是来自于长江或者东海大陆架的再沉积。伊利石,绿泥石以及高岭石主要来自于台湾河流,尤其是兰阳溪。蒙脱石/（伊利石+绿泥石）比值的大小可以用来指示东亚夏季风的强弱变化。1320 AD以来的黏土矿物组合的变化较好地记录了部分中世纪暖期后期、小冰期以及现代暖期3个时期在百年时间尺度上的气候波动。该指标显示,相对于中世纪暖期后期,东亚季风强度在小冰期（1405–1850 AD）和在现代暖期（1955 AD）均存在明显的减弱,指示该时期台湾东北部-冲绳海槽南部地区处于相对湿润强降雨的气候环境。     

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.