锦屏电站地下厂房卸荷岩体参数研究

It is a basic work to ascertain the parameters of rock mass for evaluation about stability of the engineering. Anisotropism、inhomogeneity and discontinuity characters of the rock mass arise from the existing of the structural plane. Subjected to water、weathering effect、off-loading, mechanical characters of the rock mass are greatly different from rock itself, Determining mechanical parameters of the rock mass becomes so difficult because of structure effect、dimension effect、rheological character, ‘Can’t give a proper parameter’ becomes one of big problems for theoretic analysis and numerical simulation. With the increment of project scale, appraising the project rock mass and ascertaining the parameters of rock mass becomes more and more important and strict. Consequently, researching the parameters of rock mass has important theoretical significance and actual meaning. The Jin-ping hydroelectric station is the first highest hyperbolic arch dam in the world under construction, the height of the dam is about 305m, it is the biggest hydroelectric station at lower reaches of Yalong river. The length of underground factory building is 204.52m, the total height of it is 68.83m, the maximum of span clearance is 28.90m. Large-scale excavation in the underground factory of Jin-ping hydroelectric station has brought many kinds of destructive phenomenon, such as relaxation、spilling, providing a precious chance for study of unloading parameter about rock mass. As we all know, Southwest is the most important hydroelectric power base in China, the construction of the hydroelectric station mostly concentrate at high mountain and gorge area, basically and importantly, we must be familiar with the physical and mechanical character of the rock mass to guarantee to exploit safely、efficiently、quickly, in other words, we must understand the strength and deformation character of the rock mass. Based on enough fieldwork of geological investigation, we study the parameter of unloading rock mass on condition that we obtain abundant information, which is not only important for the construction of Jin-ping hydroelectric station, but also for the construction of other big hydroelectric station similar with Jin-ping. This paper adopt geological analysis、test data analysis、experience analysis、theory research and Artificial Neural Networks （ANN） brainpower analysis to evaluate the mechanical parameter, the major production is as follows: (1)Through the excavation of upper 5-layer of the underground powerhouse and the statistical classification of the main joints fractures exposed, We believe that there are three sets of joints, the first group is lay fracture, the second group and the fourth group are steep fracture. These provide a strong foundation for the following calculation of and analysis; (2)According to the in-situ measurement about sound wave velocity、displacement and anchor stress, we analyses the effects of rock unloading effect，the results show a obvious time-related character and localization features of rock deformation. We determine the depth of excavation unloading of underground factory wall based on this. Determining the rock mass parameters according to the measurement about sound wave velocity with characters of low- disturbing、dynamic on the spot, the result can really reflect the original state, this chapter approximately the mechanical parameters about rock mass at each unloading area; (3)Based on Hoek-Brown experienced formula with geological strength index GSI and RMR method to evaluate the mechanical parameters of different degree weathering and unloading rock mass about underground factory, Both of evaluation result are more satisfied; (4)From the perspective of far-field stress, based on the stress field distribution ideas of two-crack at any load conditions proposed by Fazil Erdogan (1962)，using the strain energy density factor criterion (S criterion) proposed by Xue changming(1972)，we establish the corresponding relationship between far-field stress and crack tip stress field, derive the integrated intensity criterion formula under the conditions of pure tensile stress among two line coplanar intermittent jointed rock，and establish the corresponding intensity criterion for the exploratory attempt; (5)With artificial neural network, the paper focuses on the mechanical parameters of rock mass that we concerned about and the whole process of prediction of deformation parameters, discusses the prospect of applying in assessment about the parameters of rock mass，and rely on the catalog information of underground powerhouse of Jinping I Hydropower Station, identifying the rock mechanics parameters intellectually，discusses the sample selection, network design, values of basic parameters and error analysis comprehensively. There is a certain significance for us to set up a set of parameters evaluation system，which is in construction of large-scale hydropower among a group of marble mass.

龙游石窟洞室群稳定性分析及最佳旅游路线选择

The large ancient underground rock caverns in Longyou is an important component of grotto cultural. Current task facing the long-term preservation of these unmovable cultural relics is arduous and challenging. The deformation failure of the caverns' surrounding rock is deteriorating. The weathering velocity of these caverns is accelerating. With the strength of caverns' surrounding rock worsening, critical rocks were generated in local regions of the caverns' vault and posing a threat to the security of people passing by. Selection of a maximum-security route and construction a aisle in the caverns might be an efficient way to ensure the security of tourists and reach the target of long-term preservation. The deformation and destruction of the ancient underground caverns is primarily dominated by geological conditions and the special structure of caverns. Based on field investigation, several fundamental conditions for deformation and failure are recognized, and nine deformation and fracture patterns of the Longyou grotto are proposed. In order to judge the stability of caverns’ surrounding rock, the element safety coefficient method is presented. An explicit explanation for the meaning of the method is deduced using Mohr-Coulomb strength criterion. Numerical analyses are carried out in the dissertation through FLAC3D code. Through numerical analysis, the stress distribution regularities of the caverns’ roofs, piles and public side wall are analysed, and the stability properties of caverns’ surrounding rock are also assessed. At the same time, the element safety coefficient method is introduced to contrast the stability degree of different regions in caverns. The above analyses are bases for choosing the optimal tourism routes in the caverns of Longyou grotto. The impact of surface load on the stability of shallow buried cavities in Longyou grotto is evaluated, the results show that building load has significant influence on the stability of the No.1 cavern’s roof, pile and public side wall between the No.1 cavern and the No.2 cavern, pedestrian load has less impact on the stability of surrounding rock than building load. The principles for choosing the optimal tourism routes in the caverns are discussed. With these principles, the dissertation makes a systematic research on the geological analytic method, numerical analytic method and meeting tourism requirements method, which are used in selecting the optimal tourism routes in the caverns. In order to achieve the best effect in the process of tourism routes selection, the above three method are integrated through Theory of Engineering Geomechanics Meta-system(EGMS). According to field investigations, numerical analyses, tourism requirements and expert experiences, the optimal tourism routes through No.1 to No.5 cavern are determined preliminarily. The obtained results from the research work are useful for the security aisle's construction, they also have reference value to other projects in practice.

陆相砂泥岩层序的高精度检测方法研究及应用

Ordos Basin is a typical cratonic petroliferous basin with 40 oil-gas bearing bed sets. It is featured as stable multicycle sedimentation, gentle formation, and less structures. The reservoir beds in Upper Paleozoic and Mesozoicare are mainly low density, low permeability, strong lateral change, and strong vertical heterogeneous. The well-known Loess Plateau in the southern area and Maowusu Desert, Kubuqi Desert and Ordos Grasslands in the northern area cover the basin, so seismic data acquisition in this area is very difficult and the data often takes on inadequate precision, strong interference, low signal-noise ratio, and low resolution. Because of the complicated condition of the surface and the underground, it is very difficult to distinguish the thin beds and study the land facies high-resolution lithologic sequence stratigraphy according to routine seismic profile. Therefore, a method, which have clearly physical significance, based on advanced mathematical physics theory and algorithmic and can improve the precision of the detection on the thin sand-peat interbed configurations of land facies, is in demand to put forward.Generalized S Transform (GST) processing method provides a new method of phase space analysis for seismic data. Compared with wavelet transform, both of them have very good localization characteristics; however, directly related to the Fourier spectra, GST has clearer physical significance, moreover, GST adopts a technology to best approach seismic wavelets and transforms the seismic data into time-scale domain, and breaks through the limit of the fixed wavelet in S transform, so GST has extensive adaptability. Based on tracing the development of the ideas and theories from wavelet transform, S transform to GST, we studied how to improve the precision of the detection on the thin stratum by GST.Noise has strong influence on sequence detecting in GST, especially in the low signal-noise ratio data. We studied the distribution rule of colored noise in GST domain, and proposed a technology to distinguish the signal and noise in GST domain. We discussed two types of noises: white noise and red noise, in which noise satisfy statistical autoregression model. For these two model, the noise-signal detection technology based on GST all get good result. It proved that the GST domain noise-signal detection technology could be used to real seismic data, and could effectively avoid noise influence on seismic sequence detecting.On the seismic profile after GST processing, high amplitude energy intensive zone, schollen, strip and lentoid dead zone and disarray zone maybe represent specifically geologic meanings according to given geologic background. Using seismic sequence detection profile and combining other seismic interpretation technologies, we can elaborate depict the shape of palaeo-geomorphology, effectively estimate sand stretch, distinguish sedimentary facies, determine target area, and directly guide oil-gas exploration.In the lateral reservoir prediction in XF oilfield of Ordos Basin, it played very important role in the estimation of sand stretch that the study of palaeo-geomorphology of Triassic System and the partition of inner sequence of the stratum group. According to the high-resolution seismic profile after GST processing, we pointed out that the C8 Member of Yanchang Formation in DZ area and C8 Member in BM area are the same deposit. It provided the foundation for getting 430 million tons predicting reserves and unite building 3 million tons off-take potential.In tackling key problem study for SLG gas-field, according to the high-resolution seismic sequence profile, we determined that the deposit direction of H8 member is approximately N-S or NNE-SS W. Using the seismic sequence profile, combining with layer-level profile, we can interpret the shape of entrenched stream. The sunken lenticle indicates the high-energy stream channel, which has stronger hydropower. By this way we drew out three high-energy stream channels' outline, and determined the target areas for exploitation. Finding high-energy braided river by high-resolution sequence processing is the key technology in SLG area.In ZZ area, we studied the distribution of the main reservoir bed-S23, which is shallow delta thin sand bed, by GST processing. From the seismic sequence profile, we discovered that the schollen thick sand beds are only local distributed, and most of them are distributary channel sand and distributary bar deposit. Then we determined that the S23 sand deposit direction is NW-SE in west, N-S in central and NE-SW in east. The high detecting seismic sequence interpretation profiles have been tested by 14 wells, 2 wells mismatch and the coincidence rate is 85.7%. Based on the profiles we suggested 3 predicted wells, one well (Yu54) completed and the other two is still drilling. The completed on Is coincident with the forecastThe paper testified that GST is a effective technology to get high- resolution seismic sequence profile, compartmentalize deposit microfacies, confirm strike direction of sandstone and make sure of the distribution range of oil-gas bearing sandstone, and is the gordian technique for the exploration of lithologic gas-oil pool in complicated areas.