Prediction of sea ice edge in the Antarctic using GVF Snake model

Antarctic sea ice cover plays an important role in shaping the earth’s climate, primarily by insulating the ocean from the atmosphere and increasing the surface albedo. The convective processes accompanied with the sea ice formation result bottom water formation. The cold and dense bottom water moves towards the equator along the ocean basins and takes part in the global thermohaline circulation. Sea ice edge is a potential indicator of climate change. Additionally, fishing and commercial shipping activities as well as military submarine operations in the polar seas need reliable ice edge information. However, as the sea ice edge is unstable in time, the temporal validity of the estimated ice edge is often shorter than the time required to transfer the information to the operational user. Hence, an accurate sea ice edge prediction as well as determination is crucial for fine-scale geophysical modeling and for near-real-time operations. In this study, active contour modelling (known as Snake model) and non-rigid motion estimation techniques have been used for predicting the sea ice edge (SIE) in the Antarctic. For this purpose the SIE has been detected from sea ice concentration derived using special sensor microwave imager (SSM/I) observations. The 15% sea ice concentration pixels are being taken as the edge pixel between ice and water. The external force, gradient vector flow (GVF), of SIE for total the Antarctic region is parameterised for daily as well as weekly data set. The SIE is predicted at certain points using a statistical technique. These predicted points have been used to constitute a SIE using artificial intelligence technique, the gradient vector flow (GVF). The predicted edge has been validated with that of SSM/I. It is found that all the major curvatures have been captured by the predicated edge and it is in good agreement with that of the SSM/I observation.     

广义高斯模型阈值的南极冰盖冻融探测

在微波散射计的简单冰盖融化物理模型的基础上, 提出了不依赖于实地观测数据的自动阈值分割的南极冰盖冻融探测的新算法, 即利用广义高斯模型对通过微波散射计简单冰盖融化物理模型变换得到的数据做直方图统计, 得到干湿雪划分的最优阈值, 从而得到干湿雪的冻融分布图. 这种算法不但解决了物理模型算法需依赖实测冰盖融化信息才能进行冰盖冻融探测的问题, 而且能自动选择较多的样本, 提高了冰盖冻融探测的计算效率、 实用性和可操作性. 算法也能被应用到其他地区, 为全球的冰盖冻融探测提供了方法学的支持和补充.     

小波结合幂次变换方法在边界识别中的应用

在位场数据边界识别传统方法理论的基础上,前人研究了各种场源边界增强技术来提高信噪比和定位精度,但仍然存在受噪声影响大、场源边界不够收敛等问题。本文在传统边界识别方法的基础上进行改进,利用小波变换与传统方法相结合来增强对噪声的压制能力,并且提出了幂次变换法对识别出的边界进行有效收敛。将改进方法与传统方法应用于地质体的边界识别;同时选取3种传统数值计算方法,结合模型数据、四川盆地重力异常数据及朱日和地区磁异常数据进行对比分析。结果表明：小波结合幂次变换法能够有效识别出研究区域内地质体的边界,能很好地起到压制噪声的作用;并且识别出的边界收敛,提高了边界识别的精度,在边界识别中取得了良好的效果。     

南极地区SSM/I微波辐射计亮温时间序列分析

简单介绍了微波辐射的原理与应用现状,利用美国国防气象卫星计划DMSP F系列卫星携带的SSM/I辐射计南极地区极投影网格亮温数据进行了分析与处理。结合微波亮温等温线图和南极等高线图分析了南极地区亮温分布的特点。选取8个特征区域,对1992-2000年的日亮温数据进行了时间序列分析,分析了冰盖和冰架的亮温特性、季节和年度变化、短期波动等特征。研究表明南极大陆外围冰架和南极半岛地区的亮温呈增高趋势,而内陆冰盖地区则保持相对稳定;揭示了近年来随全球气候的变暖,南极冰架和南极半岛的融化正在加剧的趋势。     

地下介质横向变化的地震多尺度边缘检测技术

本文提出的地震多尺度边缘检测技术以坎尼最佳边缘检测准则和小波变换的多分辨分析为理论基础,能在地震记录上较方便而可靠地识别地下介质的横向变化。这些横向变化包括不能用肉眼在地震同相轴上直接发现而又实际存在的隐蔽特征。对地层尖灭,透镜体、岩性变化和孔洞、裂缝发育带均可进行识别。模型计算和实际地震资料的处理结果显示该方法具有实际应用价值     

基于钻探约束的瞬变电磁解释方法及应用

随着我国城镇建设的飞速发展,城镇建筑用地范围不断扩大,所面临的近地表地质构造越来越复杂,常规钻探法难以满足地基勘查的需要。为此,基于优势互补理念,将瞬变电磁法空间采样率高、地质信息丰富、地质体横向边界刻画准确等优势与钻探垂向测深精度高、分层定位准确等优势相结合,提出基于钻探约束的瞬变电磁法解释新方法,较好地克服了常规瞬变电磁法计算深度精度不足的局限,提高了视电阻率剖面的纵向解释能力。通过浩金元住宅建设区采空区探测的应用试验,圈定了测区中东部存在一个面积约0.01 km2的采空积水区,中部存在一个面积约0.009 75 km2的非采空区,其他部分均为采空区,取得了良好的应用效果。      

Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data.     

干旱区融雪径流模拟的研究进展与展望

融雪径流模拟是干旱区水文水资源研究的热点问题,对干旱区春季融雪洪水风险评估和流域水资源管理至关重要。结合文献查询及资料分析,重点讨论了不同类型融雪径流模型的特征和发展情况,比较了不同融雪径流模型在干旱区一些典型河流的应用情况,并对其功能及优缺点进行了评估。结合对目前流域融雪径流模拟研究中存在的问题的分析,提出未来融雪径流模拟要注重提高数据分辨率的观点。借助多源遥感数据驱动获取更为精确的输入数据,在数据获取难度减小、精度提高的基础上山区融雪径流模拟将更多地以基于能量平衡的物理性模拟为主。模型的构建要充分考虑由气候变暖所带来的其它参数的变化,精确描述山区融雪过程,以提高对融雪径流的模拟精度。     

重磁边界识别方法在西准噶尔地区三维地质填图中的应用

以3种前人所提出的重磁边界识别方法与笔者提出的归一化偏差法作为技术手段(其中归一化偏差法对于数据噪声有着较好的抵抗力)能够较为准确全面地识别边界位置.在分析对比方法原理及应用特性的基础上,通过理论模型试验证明了方法的可靠性和实用性;并针对断裂和地质体边界深部倾向问题,开展了多方法联合及多尺度识别手段试验,获取了更多地质构造及边界的信息.通过将该技术应用于克拉玛依后山地区实际资料的处理解释之中,获得了研究区域内剖面与平面上的断裂和岩体的位置分布及它们的深部倾向信息,并对主要断裂与岩体三维空间特征进行了描述,为该地区三维地质填图和三维地质模型构建提供了重要依据.      

利用水平与垂直导数的相关系数进行位场数据的边界识别

为了解决现今边界识别方法中普遍存在的不能同时识别不同深度地质体边界的问题,采用相关系数法进行边界识别。该方法是利用总水平导数与垂直导数的相关系数来进行边界的识别。由于不同地质体边界处的水平与垂直导数的相关系数幅值几乎一样,因此该方法可以有效地同时识别出不同深度地质体的边界。理论模型试验表明:该方法能同时识别出不同深度地质体的边界,且与模型边界相吻合;将其应用于实际数据的处理中,该方法能识别出更多的地质细节。     

利用多尺度边缘检测研究碳酸盐岩裂缝分布

裂缝检测与图像分析中的边缘检测有许多相似之处,因此,能够借助某些边缘检测理论并结合实际地震资料形成有效的裂缝检测方法。作者基于小波多尺度边缘检测理论,结合地震波场对裂缝的多尺度特性,提出了碳酸岩裂缝预测的多尺度边缘检测方法。方法的主要思想是：利用小波变换械的局部极值,检测出三维地震记录裂缝点特征位置,并通过调整模值在图像中的灰度,自动识别裂缝发育区的范围。     

Ground-penetrating radar for monitoring the distribution of near-surface soil water content in the Gurbantünggüt Desert

In the Gurbantünggüt Desert, snowmelt-induced high soil water contents briefly create favorable conditions for the germination and growth of plants every spring. Monitoring the rapidly changing conditions in this time period demands fast and efficient methods for measuring soil water contents at the field scale. For this study, a series of ground-penetrating radar (GPR) measurements were carried out on sites characterized by semi-vegetated dunes both in April 2010 and 2011. We compare water contents calculated from the GPR direct ground wave signal to both point scale validation measurements by time-domain reflectometry (TDR) and gravimetric sampling. Our results show that GPR is an effective method to rapidly obtain a detailed image of the field scale soil water content distribution in the Gurbantünggüt Desert with an accuracy similar to TDR. Observed large scale soil water content variations are dominated by dune topography: During snow melting, melt water was found to trickle slowly from the dune ridges to interdune valleys, increasing the soil water content there while the dune ridges quickly started to dry down. In dune valleys, smaller scale near-surface soil water content changes were dominated by variations in the vegetation coverage, leading to snowmelt funnels at distinct locations: The snowmelt initially occurred around the stems and branches of plants, forming funnel-shaped melt water induced holes through the snow cover and leading to an increasing amount of melt water collected around these plant roots. Our comparison of data from 2010 to 2011 furthermore suggests a temporally stable distribution of near-surface soil water content. This has important ecological significance for controlling desertification and for restoring and reconstructing vegetation in the Gurbantünggüt Desert.     

基于图像处理的岩体结构面迹线半自动检测

针对一种基于图像处理的岩体结构面迹线半自动检测的方法进行了研究。该法的基本思路是在图像中人工指定少量难以正确识别的主要裂隙控制点,然后将图像一分为二,分别对二者进行边缘检测,得到两张包含结构面迹线和伪边缘的分段线图片,之后根据距离、角度原则分别对两张图片中的分段线进行迭代连接,最终将检测结果叠加,得到完整裂隙迹线检测结果。该法将主要裂隙与次要裂隙区域分开处理,能够更好地减少二者之间的干扰,所以能够更好地保证主次裂隙识别的完整性与正确性,因而具有更高的精确性。     

广义S变换时频分析在广域电磁法数据处理中的应用

广义S变换是一种可逆的局部时频分析方法,是短时傅里叶变换和小波变换的延伸和推广。利用广义S变换对广域电磁时间序列数据进行分析,除了可以划定噪声区域便于实施滤波去噪之外,还可以提高数据频谱分析精度。针对广域电磁数据特点,基于广义S变换提出了适合广域电磁法的广义S变换处理流程,进行了信号的仿真实验。同时,也对广域电磁法实测数据进行了处理,高信噪比数据的处理结果证实了广义S变换的有效性和准确性。强干扰数据处理的结果表明,获取的信号质量比基于傅里叶变换的传统方法有明显提高。处理效果表明,提出的方法适用于广域电磁法数据处理。     

用形态学滤波从电导率图像中提取缝洞孔隙度谱

为了更好地实现对缝洞型储层孔隙结构和孔隙度的精细评价,基于高覆盖率和高分辨率电成像测井的电导率数据,用多尺度形态学滤波方法分离了基质孔、裂缝和溶蚀孔洞,提取了缝洞孔隙度谱。首先分析了电成像测井对裂缝和溶蚀孔洞的响应模式;其次,在简单介绍数学形态学算子的基础上,给出了结构元素选择和滤波算子构造的方法,用于电成像测井数据的噪声压制和缝洞异常电导率信息的提取;再次,基于缝洞发育处电导率异常的边缘检测结果,用椭圆形及不规则多边形函数拟合溶蚀孔洞,用多项式插值函数拟合裂缝边界,继而提取缝洞分布多类属性参数,获得缝洞孔隙度谱;最后,用实测数据对文中算法进行了测试,验证了多尺度数学形态学滤波方法用于电成像测井资料缝洞孔隙度谱计算的有效性。     

中国1996年7月南方暴雨水灾星载微波SSM/I遥感数据分析

以1996年7月我国南方暴雨水灾为例,研究了暴雨水灾时星载微波SSM/I的遥感数据,分析了SSM/I7个通道辐射亮度温度的极化散射和辐射特征,定义了识别降雨的散射指数SI,水灾警戒指数FI,和辐射极化指数PI.用SSM/I的观察数据,讨论了暴雨水灾时这些指数的时间和空间的特征性变化,给出了SSM/I数据对于1996年7月暴雨和水灾的识别,以及全月降雨异常值的空间分布。     

基于改进的交叉极化比率算法的南极冻融探测

针对交叉极化比率（cross-polarized gradient ratio,XPGR）算法在冰盖冻融探测中存在的问题,提出了不依赖于实地观测数据的、自动阈值分割的、改进的XPGR微波辐射计南极冰盖冻融探测方法,即运用广义高斯模型对XPGR的长时间序列数据做直方图统计,计算出干湿雪划分的最优阈值,从而得到南极冰盖冻融分布图。试验结果表明：改进前后的结果虽然相差不多,但改进后的方法由于不依赖于实地观测数据,不仅提高了冰盖冻融探测的计算效率、实用性和可操作性,而且还在一定程度上提高了冰盖冻融探测的精度。     

改进的倾斜角法在位场场源边界识别中的应用

位场场源边界识别是位场数据分析解释中的重点内容之一。相邻异常源由于场的叠加而难以区分,尤其是对于叠加异常中不同深度的场源边界的识别。倾斜角法是位场数据处理中常用的边界增强方法之一,能有效突出低振幅、短波长的异常特征。本文基于倾斜角法定义了新的位场边界探测方法,该方法利用倾斜角方程中位场二阶垂直导数与垂向导数水平导数之比进行计算,能有效区分相邻异常源产生的叠加异常,突出叠加异常中不同深度场源的边界特征。通过综合模型和实测数据,并与现有的基于导数的位场边界探测方法进行比较,评价了该方法的应用效果。结果表明,该方法对场源深度的变化不太敏感,它可以更准确地确定场源边界,区分相邻异常源的叠加异常,尤其对密集型组合地质构造的识别有较好的效果,即使是在场源埋深较大情况下,也能突出叠加异常中不太明显的微弱变化的线性异常特征,以及弱异常源的形态特征,可为精细研究局部地质构造提供技术支持。     

使用积雪遥感面积数据改善山区春季融雪径流模拟精度

随着寒区水文模拟研究的深入,春季融雪径流模拟误差大这一问题越来越明显.针对此问题,以八宝河流域为研究区,利用积雪衰减曲线将MODIS积雪面积比例产品转化为雪水当量,并用其更新分布式水文模型GBHM(Geomorphology-Based Hydrological Model)中模拟的雪水当量,达到提高春季融雪径流模拟精度的目的.利用GBHM模型对八宝河流域2005-2007年进行了模型预热,参数率定,同时选择2008年进行模型检验.结果表明:GBHM模型在八宝河流域有较好的径流模拟精度,年均纳什效率系数(NSE)达到0.64.但模型对春季融雪过程的模拟效果较差,通过引入积雪遥感数据,这一问题得到很大改善.加入积雪遥感数据后,2008年3-6月径流模拟精度NSE和相对偏差Bias分别由-1.0、-0.45改进为0.58、0.06,单点雪水当量模拟精度获得提高,流域水量平衡也更加合理.     

Micro-seismic Event Detection of Hot Dry Rock based on the Gated Recurrent Unit Model and a Support Vector Machine

Micro-seismic monitoring is one of the most critical technologies that guide hydraulic fracturing in hot dry rock resource development. Micro-seismic monitoring requires high precision detection of micro-seismic events with a low signal-to-noise ratio. Because of this requirement, we propose a recurrent neural network model named gated recurrent unit and support vector machine (GRU_SVM). The proposed model ensures high accuracy while reducing the parameter number and hardware requirement in the training process. Since micro-seismic events in hot dry rock produce large wave amplitudes and strong vibrations, it is difficult to reverse the onset of each individual event. In this study, we utilize a support vector machine (SVM) as a classifier to improve the micro-seismic event detection accuracy. To validate the methodology, we compare the simulation results of the short-term-average to the long-term-average (STA/LTA) method with GRU_SVM method by using hot dry rock micro-seismic event data in Qinghai Province, China. Our proposed method has an accuracy of about 95% for identifying micro-seismic events with low signal-to-noise ratios. By ignoring smaller micro-seismic events, the detection procedure can be processed more efficiently, which is able to provide a real-time observation on the types of hydraulic fracturing in the reservoirs.