Prediction of creep characteristic of rock under varying environment

The strain developed due to creep is mainly proportional to the logarithm of the time under load, and is mostly proportional to the stress and temperature. At higher temperature the creep rate falls slowly with respect to time, and the creep strain is proportional to a fractional power of time, with the exponent increasing as the temperature increases and reaching a value approximately one-third at temperatures of about 0.5°C. At these temperatures, the creep increases with stress according to a power greater than unity and possibly exponentially. It increases with temperature as (−U/kT), where U is an activation energy and k is Boltzman’s constant. There are different methods to determine the creep strain and the energy of Jog (B) including experimental methods, multivariate regression analysis, and by numerical simulation. These methods are less cumbersome and time consuming. In the present investigation, artificial neural network technique has been used for prediction of the creep strain and energy of Jog (B). Two different networks have been tested and validated. Both the networks have four input neurons and one hidden layer with five neurons, and one output neuron. The data for different rocks at temperatures up to 750°C under conditions of compressive or tortional stress are taken from the literatures. The training and testing data sets used were 163 and 14, respectively. To deal with the problem of overfitting of data, Bayesian regulation has been used and network is trained with suitable training epochs. The coefficients of correlation among the predicted and observed values are found high and they improve the confidence of the users. The mean absolute percentage error obtained are also very low.     

Fault geometry and mechanics of marly carbonate multilayers: An integrated field and laboratory study from the Northern Apennines,Italy

Sealing layers are often represented by sedimentary sequences characterized by alternating strong and weak lithologies. When involved in faulting processes, these mechanically heterogeneous multilayers develop complex fault geometries. Here we investigate fault initiation and evolution within a mechanical multilayer by integrating field observations and rock deformation experiments. Faults initiate with a staircase trajectory that partially reflects the mechanical properties of the involved lithologies, as suggested by our deformation experiments. However, some faults initiating at low angles in calcite-rich layers (θ_i = 5°–20°) and at high angles in clay-rich layers (θ_i = 45°–86°) indicate the important role of structural inheritance at the onset of faulting. With increasing displacement, faults develop well-organized fault cores characterized by a marly, foliated matrix embedding fragments of limestone. The angles of fault reactivation, which concentrate between 30° and 60°, are consistent with the low friction coefficient measured during our experiments on marls (μ_s = 0.39), indicating that clay minerals exert a main control on fault mechanics. Moreover, our integrated analysis suggests that fracturing and faulting are the main mechanisms allowing fluid circulation within the low-permeability multilayer, and that its sealing integrity can be compromised only by the activity of larger faults cutting across its entire thickness.     

Displacement velocity effects on rock fracture shear strengths

Triaxial shear tests are performed to assess the effects of displacement velocity and confining pressure on shear strengths and dilations of tension-induced fractures and smooth saw-cut surfaces prepared in granite, sandstone and marl specimens. A polyaxial load frame is used to apply confining pressures between 1 and 18 MPa with displacement velocities ranging from 1.15 × 10⁻⁵ to 1.15 × 10⁻² mm/s. The results indicate that the shearing resistances of smooth saw-cut surfaces tend to be independent of the displacement velocity and confining pressure. Under each confinement the peak and residual shear strengths and dilation rates of rough fractures increase with displacement velocities. The sheared-off areas increase when the confining pressure increases, and the displacement rate decreases. The velocity-dependent shear strengths tend to act more under high confining pressures for the rough fractures in strong rock (granite) than for the smoother fractures in weaker rocks (sandstone and marl). An empirical criterion that explicitly incorporates the effects of shear velocity is proposed to describe the peak and residual shear strengths. The criterion fits well to the test results for the three tested rocks.     

Modeling discontinuous rock mass based on smoothed finite element method

This paper aims at developing a method for modeling rock mass with preexisting multiple discontinuities within the framework of the smoothed finite element method (SFEM). The discontinuity is simulated by an interface element with zero thickness, the stiffness matrix of which are derived explicitly based on the SFEM. An elastic damage constitutive relation with residual strength is introduced in order to describe the nonlinear mechanical behavior of the discontinuities. The computation codes of the present method were developed. The present method has been verified to be a sound approach for modeling discontinuous rock mass, inheriting the advantages of the SFEM.     

藏南马扎拉金锑矿床围岩蚀变及矿化特征

马扎拉金锑矿位于西藏自治区山南地区措美县东南部古堆乡,东临隆子县,南近错那县,区域上位于藏南拆离断裂（陈智梁等,1996）以北的特提斯喜马拉雅（或称特提斯喜马拉雅）褶皱造山带（许志琴等,2006）南部,属于隆子逆冲推覆断裂的前缘。自2014年开始,成都地质调查中心作为扎西康整装勘查的主要实施单位接受了西藏雪域矿业开发有限公司的委托,在矿区开展地质找矿工作,现已基本查明了金锑矿体围岩蚀变和矿化特征。     

基于岩石图像深度学习的岩性自动识别与分类方法

岩石岩性的识别与分类对于地质分析极为重要,采用机器学习的方法建立识别模型进行自动分类是一条新的途径。基于Inception-v3深度卷积神经网络模型,建立了岩石图像集分析的深度学习迁移模型,运用迁移学习方法实现了岩石岩性的自动识别与分类。采用此方法对所采集的173张花岗岩图像、152张千枚岩图像和246张角砾岩图像进行了学习和识别分类研究,通过训练学习建立岩石图像深度学习迁移模型,并分别采用训练集和测试集中的岩石图像对模型进行了检验分析。对于训练集中的岩石图像,每组岩石分别用3张图像测试,三种岩石的岩性分类均正确,且分类概率值均达到90%以上,显示了模型良好的鲁棒性;对于测试集中的岩石图像,每组岩石分别采用9张图像进行识别分析,三种岩石的岩性分类均正确,并且千枚岩组图像分类概率均高于90%,但是花岗岩组2张图像和角砾岩组的1张图像分类概率值不足70%,概率值较其他岩石图像低,推测其原因是训练集中相同模式的岩石图像较少,导致模型的泛化能力减小。为了提高识别精确度,对准确率较低的岩石图像进行截取,分别取其中的3张图像加入训练集进行再训练,增加与测试图像具有相同模式的训练样本;在新的模型中,对3张图像进行二次检验,测试概率值均达到85%以上,说明在数据足够的状况下模型具有良好的学习能力。与传统的机器学习方法相比,所提出的岩石图像深度学习方法具有以下优点:第一,模型通过搜索图像像素点提取物体特征,不需要手动提取待分类物体特征;第二,对于图像像素大小,成像距离及光照要求低;第三,采用适当的训练集可获得较好的识别分类效果,并具有良好鲁棒性和泛化能力。     

华北板块南缘伏牛山花岗岩锆石LA-MC-ICP-MS U-Pb定年、Lu-Hf同位素特征及岩石成因

伏牛山花岗岩体出露于华北板块南缘,南召县城以北,面积超过4200km~2。岩体的岩石组合为花岗岩+花岗闪长岩+石英闪长岩,具有I型花岗岩的特征。花岗岩锆石的LA-MC-ICP-MS U-Pb定年得到145.4±1.0Ma和118.5±0.6Ma,说明岩体形成于燕山期,并经历了至少两期岩浆活动。锆石Hf同位素分析表明,第一期花岗岩的ε_(Hf)(t)平均值为-16.53,二阶段模式年龄(t_(DM2))平均为2216Ma,表明其源岩以壳源物质为主;第二期花岗岩的源岩分为两个部分,一部分花岗岩的ε_(Hf)(t)平均值为-13.67,二阶段模式年龄(t_(DM2))平均为2044Ma,表明其源岩以壳源物质为主,另一部分花岗岩的ε_(Hf)(t)平均值为1.61,二阶段模式年龄(t_(DM2))平均为1073Ma,表明其源岩以新生地壳为主。根据研究结果及区域地质构造分析,认为第一期岩浆作用是由于太平洋板块俯冲导致秦岭造山带断裂构造再活动,发生部分熔融形成小规模的岩浆作用;而第二期岩浆作用是由于太平洋板块俯冲导致岩石圈拆沉,使地幔软流圈的物质上升,形成巨大的热场,引起大陆地壳大规模的部分熔融形成花岗岩浆。最终形成的花岗岩浆沿着华北板块与扬子板块之间的断裂上侵至地壳浅处,形成了伏牛山复式岩体。     

鲜水河断裂带乾宁段岩石特征与内部结构及其物理-化学性质

断裂带的变形行为和断层滑移机制是目前地震研究关注的热点,断裂带岩石特征、内部结构与物理化学性质是确定断层蠕滑或粘滑行为以及断层滑移机制的基础和关键。本文以鲜水河断裂带乾宁段地表出露的断裂岩为研究对象,通过野外地质调查、室内光学显微镜、扫描电镜、粒度统计、粉末X射线衍射分析（XRD）和薄片X射线荧光光谱分析（XRF）等多种研究方法,对鲜水河断裂带岩石特征、结构构造、物性、矿物成分及化学元素分布开展了详细的分析,并探讨了相关变形行为和滑移机制。分析表明：（1）断裂带核部主要由黑色断层泥、浅黄色及黄色断层角砾岩和灰色碎裂岩、灰色断层角砾岩组成,呈单核对称结构;（2）黑色断层泥厚3~5cm,具有快速滑动结构特征,表现为断层粘滑行为。断层泥可划分出13个滑移带,最窄滑移带厚约40μm,至少代表13期古地震事件;（3）断层泥主要由伊利石、高岭石和石英等矿物组成,其中边部伊蒙混层含量异常高,为最新一次古地震的主滑移带。由于伊利石和伊蒙混层（或蒙脱石）为主要黏土矿物的断层泥渗透率低、孔隙流体压力大,以及发现断层泥楔入脉,表明地震过程中断层滑动存在热增压弱化机制;（4）从断层泥不同滑动带中碎块蚀变程度和矿物分布特征来看,地震主滑动带有向碎裂岩方向迁移的趋势。推测断层在滑动过程中,更趋向于向弱矿物含量高（如伊利石、伊蒙混层）、强矿物含量低（如方解石、高岭石）的围岩一侧迁移。     

金沙江下虎跳滑石板滑坡破坏机理及稳定性分析

1996年10月28日发生在金沙江下虎跳峡口的滑石板滑坡,因堵塞金沙江、摧毁公路而引起高度重视。野外调查发现,滑石板滑坡是典型结构面控制的顺层基岩滑坡,层面和两组近于垂直层面的节理将边坡厚层灰岩切割成不同规模的菱形块体,长期的风化作用、重力、降雨、人工开挖公路等因素作用下,这些菱形块体从顺层稳定状态演化为稳定性较差的悬挂体,受河谷边坡斜向割切影响,表现为梯级叠次悬挂,最外边的悬挂体稳定性最差,最容易发生快速滑动。因此,该滑坡每次发生破坏滑动的体积不会很大,但是具有多次重复发生的特点,值得高度注意。