水源钻机在超深土层绳索取心钻进中的应用          下载免费PDF全文

杨联锋  段云星 《探矿工程》2020,47(11):44-50

山西省清徐县区域地质调查项目设计800、2000、3000 m科学钻探孔，以调查填补新生界底板埋深控制空白区，各孔钻入基岩30 m完钻。要求全孔取心，岩心采取率≮85%，岩心直径≮60 mm，采用塑料保护管采取原状岩样。针对超深软土层、各组地层特性及厚度未知、钻遇基岩完钻深度未知、大直径高保真全孔取心、项目价格远低于目前市场成本等难题，经过“水源钻机+大提钻取心+长裸眼孔段”实施800 m孔、“岩心钻机+绳索取心+套管固井”实施640 m参数对比孔，创新性使用“水源钻机+绳索取心+长裸眼孔段”工艺完成了2000 m孔的施工。该工艺岩心采取率达到93%，孔径和孔斜符合地质要求，为3000 m孔顺利施工打下了坚实的基础，为同类型项目提供了经验和借鉴。  相似文献   

利用短期静态PPP结果反演海潮负荷位移     

魏国光  王琪洁  彭葳  郭良琦 《大地测量与地球动力学》2019,39(6):607-612

采用香港11个GPS测站的观测资料进行1 h、2 h、3 h和4h静态PPP解算，获得4组PPP坐标序列，利用调和分析求取11个测站处8个主要分潮的负荷位移参数（振幅和相位），将其与海潮模型计算的负荷位移参数进行对比，并比较分析PPP反演值与海潮模型值改正海潮负荷信号的效果。结果表明，垂直和水平方向上，不同PPP结果反演8个分潮的负荷位移分别具有约5 mm和7 mm的差异；PPP反演8个分潮垂向负荷位移优于全球海潮模型，但水平方向上的反演效果稍弱。  相似文献   

基于深度学习理论的无人机影像分类     

阳成 《北京测绘》2020,(4):481-484

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。  相似文献   

基于深度置信网络的GNSS-IR土壤湿度反演     

陈堃  沈飞  曹新运  朱逸凡 《测绘通报》2020,(9):100-105

基于大地测量型GNSS接收机获取的反射信号反演土壤湿度是GNSS领域的研究热点。为克服常规线性回归和BP神经网络算法等的缺陷，本文提出了一种基于深度置信网络的GNSS-IR土壤湿度反演方法。试验结果表明，基于该方法得到的决定系数、土壤湿度平均绝对误差和均方根误差分别为0.909 8、0.017、0.021，与线性回归和BP神经网络算法相比，与实测数据吻合度更高，可有效提高土壤湿度反演精度，证明了方法的有效性和可靠性。  相似文献   

基于离散Gabor变换的磁暴识别     

王静  贺巍  汪伟明 《地震地磁观测与研究》2020,41(4):64-69

以地磁秒数据为研究对象，通过离散Gabor变换将时域的地磁数据转换至二维时频面，提取Gabor变换谱图的均值和方差作为特征值，使用支持向量机实现地磁正常数据与磁暴干扰数据的自动分类识别。对5个地磁台的200组地磁秒数据进行计算分析，结果表明该方法对测试样本数据的识别率可达94%。  相似文献   

西太平洋深海海水中乙醛降解菌多样性及群落结构研究     

高波良  商谢谢  李丽  狄文婕  曾润颖 《海洋学报(英文版)》2018,37(8):54-64

As a major aldehyde pollutant widely existing in industry and our daily life, acetaldehyde is more and more harmful to human health. As characteristic habitat niche, bacteria from deep sea environments are abundant and distinctive in heredity, physiology and ecological functions. Thus, the development of acetaldehyde-degrading bacteria from deep sea provides a new method to harness acetaldehyde pollutant. Firstly, in this study,acetaldehyde-degrading bacteria in the deep sea water of the West Pacific Ocean were enriched in situ and in the laboratory respectively, and then the diversity of uncultured bacteria was studied by using 16 S r RNA genes. Then acetaldehyde-degrading strains were isolated from two samples, including enrichment in situ and enrichment in laboratory samples of deep sea water from the West Pacific Ocean using acetaldehyde as the sole carbon source,and then the ability of acetaldehyde degradation was detected. Our results showed that the main uncultured bacteria of two samples with different enrichment approaches were similar, including Proteobacteria,Actinobacteria, Firmicutes, Cyanobacteria, but the structure of bacterial community were significant different.Four subgroups, α, γ, δ and ε, were found in Proteobacteria group. The γ-Proteobacteria was dominant(63.5%clones in laboratory enriched sample, 75% clones in situ enriched sample). The species belonged to γ-Proteobacteria and their proportion was nearly identical between the two enrichment samples, and Vibrio was the predominant genus(45% in laboratory enriched sample, 48.5% in situ enriched sample), followed by Halomonas(9% in situ enriched sample) and Streptococcus(6% in laboratory enriched sample). A total of 12 acetaldehyde-degrading strains were isolated from the two samples, which belonged to Vibrio, Halomonas,Pseudoalteromonas, Pseudomonas and Bacillus of γ-Proteobacteria. Strains ACH-L-5, ACH-L-8 and ACH-S-12,belonging to Vibrio and Halomonas, have strong ability of acetaldehyde degradation, which could tolerate 1.5 g/L acetaldehyde and degrade 350 mg/L acetaldehyde within 24 hours. Our results indicated that bacteria of γ-Proteobacteria may play an important role in carbon cycle of deep sea environments, especial the bacteria belonging to Vibrio and Halomonas and these strains was suggested for their potentials in government of aldehyde pollutants.  相似文献   

Developing constitutive models from EPR‐based self‐learning finite element analysis          下载免费PDF全文

Ali Nassr  Akbar Javadi  Asaad Faramarzi 《国际地质力学数值与分析法杂志》2018,42(3):401-417

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.  相似文献   

地震油气储层的小样本卷积神经网络学习与预测   总被引：2，自引：0，他引：2       下载免费PDF全文

林年添  张栋  张凯  王守进  付超  张建彬  张冲 《地球物理学报》2018,61(10):4110-4125

地震储层预测是油气勘探的重要组成部分，但完成该项工作往往需要经历多个环节，而多工序或长周期的研究分析降低了勘探效率.基于油气藏分布规律及其在地震响应上所具有的特点，本文引入卷积神经网络深度学习方法，用于智能提取、分类并识别地震油气特征.卷积神经网络所具有的强适用性、强泛化能力，使之可以在小样本条件下，对未解释地震数据体进行全局优化提取特征并加以分类，即利用有限的已知含油气井段信息构建卷积核，以地震数据为驱动，借助卷积神经网络提取、识别蕴藏其中的地震油气特征.将本方案应用于模型数据及实际数据的验算，取得了预期效果.通过与实际钻井信息及基于多波地震数据机器学习所预测结果对比，本方案利用实际数据所演算结果与实际情况有较高的吻合度.表明本方案具有一定的可行性，为缩短相关环节的周期提供了一种新的途径.  相似文献   

Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer   总被引：1，自引：1，他引：0  

Basant Yadav  Shashi Mathur  Sudheer Ch  Brijesh Kumar Yadav 《水文科学杂志》2018,63(2):210-226

Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L.  相似文献   

Modelling the effect of changing precipitation inputs on deep soil water utilization          下载免费PDF全文

Ji Qi  Daniel Markewitz  David Radcliffe 《水文研究》2018,32(5):672-686

Forests in the Southeastern United States are predicted to experience future changes in seasonal patterns of precipitation inputs as well as more variable precipitation events. These climate change‐induced alterations could increase drought and lower soil water availability. Drought could alter rooting patterns and increase the importance of deep roots that access subsurface water resources. To address plant response to drought in both deep rooting and soil water utilization as well as soil drainage, we utilize a throughfall reduction experiment in a loblolly pine plantation of the Southeastern United States to calibrate and validate a hydrological model. The model was accurately calibrated against field measured soil moisture data under ambient rainfall and validated using 30% throughfall reduction data. Using this model, we then tested these scenarios: (a) evenly reduced precipitation; (b) less precipitation in summer, more in winter; (c) same total amount of precipitation with less frequent but heavier storms; and (d) shallower rooting depth under the above 3 scenarios. When less precipitation was received, drainage decreased proportionally much faster than evapotranspiration implying plants will acquire water first to the detriment of drainage. When precipitation was reduced by more than 30%, plants relied on stored soil water to satisfy evapotranspiration suggesting 30% may be a threshold that if sustained over the long term would deplete plant available soil water. Under the third scenario, evapotranspiration and drainage decreased, whereas surface run‐off increased. Changes in root biomass measured before and 4 years after the throughfall reduction experiment were not detected among treatments. Model simulations, however, indicated gains in evapotranspiration with deeper roots under evenly reduced precipitation and seasonal precipitation redistribution scenarios but not when precipitation frequency was adjusted. Deep soil and deep rooting can provide an important buffer capacity when precipitation alone cannot satisfy the evapotranspirational demand of forests. How this buffering capacity will persist in the face of changing precipitation inputs, however, will depend less on seasonal redistribution than on the magnitude of reductions and changes in rainfall frequency.  相似文献