轮胎子午线检测系统的匹配算法研究

本文结合轮胎的实际图像,在大量实验的基础上,总结出了适合轮胎子午线检测的方法.首先对图像进行预处理,包括灰度变换、阈值变换、边缘检测、轮廓提取和种子填充5个步骤,其次由于原始模板匹配算法存在计算量大、匹配速度慢、不精确;我们提出二次精确匹配算法从而解决了此问题,最终得到满意结果.     

基于支持向量机的信号自动检测算法

STA/LTA算法是信号自动检测中的经典算法. 这种算法中检测阈值的分布范围在0~infin;之间， 合适的检测阈值不但要根据实验反复调试， 而且要在误检率和漏检率之间取得平衡. 针对这一问题， 从模式识别的角度出发， 给出了一种基于支持向量机的信号检测算法. 讨论了该算法中数据预处理和模式特征提取的方法， 以及支持向量机中核函数的选择问题. 利用实际地震数据， 分析了这种算法的检测性能. 结果表明, 这种算法简化了检测阈值的选择. 在准确检测信号的同时，其误检率相对于STA/LTA算法可以降低约85％， 并且具有较强的抗噪性能.      

基于尺度空间技术的归一化Facet模型位场边界识别

边界识别是位场数据处理解释中的重要环节,传统边界识别方法通常不能均衡深、浅部地质体边界.基于尺度空间技术和归一化的Facet模型检测算子,本文开发了一种带通空间滤波和边缘检测相结合的边界识别方法,有效地提高位场数据边界识别的精度和可靠性.为了验证本文算法的有效性和稳定性,分析了不同尺度空间函数和检测算子对算法的影响,并且对比了传统边界识别方法的效果.理论模拟和实际数据分析表明,利用位场垂向二阶导数进行的基于尺度空间技术的归一化Facet模型边界识别方法不仅算法的稳定性强,而且可以避免高阶导数对噪声干扰放大作用,同时均衡深部和浅部地质体边界,从而可以更精确地识别地质体的形态.     

基于遗传算法的大地电磁阻抗张量分解方法研究

本文针对Groom-Bailey分解法在消除地表电性不均匀体对MT数据的畸变效应时存在的问题引入遗传算法,对基于传统线性最优化方法的GB分解算法进行改进,提出基于遗传算法的大地电磁阻抗张量分解方法.通过对理论合成数据以及三维/二维模型正演数据的分解试验,并且对青藏高原北缘阿尔金断裂地区实际MT数据进行分解处理,证明了基于遗传算法的GB分解能够更加有效地校正三维/二维情况下近地表三维电性不均匀体所造成的畸变影响.最后,在已有算法基础上,研究了基于遗传算法的多频率GB分解算法和MT数据静校正算法,并通过实际MT数据的处理证明了这些算法的有效性.     

三角网格有限差分法叠前逆时偏移方法研究

随着CPU/GPU机群性能的提高及广泛应用,近几年来逆时偏移得到迅速发展完善并已在国内外投入大规模生产.由于逆时偏移采用双程波波动方程,在逆掩断层、推覆体等复杂地质构造成像方面明显优于单程波偏移方法.本文尝试将三角网格算法引入逆时偏移中,使其可直接处理起伏地表采集的数据,而无需预先做静校正.基于三角网格的有限差分算法保持了差分算法的简单性和有限元算法的精确性.与“波场下延累加”法单程波偏移不同的是,基于矩形网格的逆时偏移算法在直接处理复杂地表数据时,极易出现不稳定的情况,难以实用.而基于三角网格的逆时偏移算法能够保证波场外推过程的稳定性,并很好地使近地表漫散射收敛.本文通过对模型数据和实际资料的测试验证了该方法的有效性.     

基于加权一致性的蚁群算法在断层检测中的应用

突出地震数据振幅空间不连续性的断层增强属性体是断层自动解释的基础,而如何压制噪声、地层残余响应、角度不整合接触等伪断层的影响是断层自动检测的关键问题之一.在指纹图像边缘检测处理中,一致性是对指示局部方向场信息各向异性强度的一个度量,本文将其作为一种新的断层增强属性引入到地震数据处理中.为了更有效地压制噪声,本文通过对一致性进行加权处理,再结合蚁群算法,提出了新的断层自动检测方法——基于加权一致性的蚁群算法.理论地震模型和实际地震数据的处理结果表明,对于相同的断层检测过程,对比常规的基于方差属性的蚁群算法和基于C3相干属性的蚁群算法,本文所提方法不仅更有效地压制了噪声和地层残余响应产生的伪断层信息,而且更完整地展现了断层的延伸长度,同时具有更高的稳定性和计算效率.     

体数据特征点检测算法综述

三维图像处理技术在当今得到了广泛的应用,体数据是一种重要的三维图像数据类型,基于体数据的三维特征点检测技术作为三维图像配准、分类和识别等方法的第一步,具有特殊的重要意义。本文阐述了基于体数据的特征点检测算法的特点,归纳总结了检测流程,对具有代表性的检测器进行了举例分析,最后给出了检测算法相应的评价方法。     

医学图像三维表面模型重建

三维表面模型重建是医学图像处理的重要内容,是将CT和MRI等医疗影像设备获得的二维图像重建成三维立体表面显示图像的过程.本文重点研究了其重建算法的具体实现:(1)在建模上采用了基于体素的三维物体体积重建;(2)在显示上提出了基于Ray-Casting的三维物体二维直接显示技术.所以,和传统采用三角面拟合的方法相比,具有图像重建过程无需生成中间数据、无需进行3D物体的边界检测等优点,同时该算法并行性好,易于采用硬件方法实现加速.因此,本研究为医学图像三维表面模型重建应用软件的开发打下了理论基础.     

改进的高精度匹配追踪方法研究及应用

本文在常规算法的基础上,提出了一种改进的匹配追踪方法.该方法通过引入新的完备库构建策略,并以雷克子波作为原子,利用原始地震数据最大相关性估计完备库的原子的位置和能量,使得分解精度进一步提高,极大地提高了算法的适应性;为避免原子间隔过小问题,引进了最小原子间距,使得分解效率和分解质量进一步提高.模型试算和实际资料应用表明,本文算法不仅提高了信号稀疏表示的质量,加快了收敛速度,而且算法的适应性和分解精度也得到提高;该方法能够较好地挖掘地震有效信息,提高地震解释精度.     

基于结构导向的梯度属性边缘检测技术

地震数据中的不连续性检测对于识别和刻画地下地质体边界具有重要意义.随着勘探目标向岩性油藏和复杂断块油藏的转变,解释人员需要对小断裂、微裂缝等微幅地质体边界信息进行精细刻画.常规边缘检测算子由于算法的局限性不能满足上述需求.本文通过引入能量归一化和沿层检测思想对常规检测算子进行改进,提出一种基于结构导向的梯度属性边缘检测技术.该技术通过结构张量估计得到局部地层区域的倾角和方位角,利用得到的倾角和方位角信息作为约束,使用二维边缘检测算子实现三维数据中的沿层不连续性检测.经过实际资料处理,并与结构导向方差属性对比分析,证实该方法在地层不连续性检测方面具有很好效果,检测得到的不连续性信息在细节上表现更丰富,可以作为地层横向差异和地质体边界识别的有力工具.     

一种工业CT点云数据的NURBS曲面重构算法

虽然CT技术已经发展得相当成熟,但保证检测数据重构的精度还存在较大困难.本文将工业CT图像转化为三维散乱点云数据,研究数据预处理算法;对处理后数据进行NURBS曲面插值,完成了对NURBs曲面的董构.以某工件工业CT图像为例,通过VG软件转化为点云数据,利用VC++和OpenGL编程实现数据预处理算法和NURBS曲面拟...     

Convolutional neural networks for automated microseismic detection in downhole distributed acoustic sensing data and comparison to a surface geophone array

Distributed acoustic sensing is a growing technology that enables affordable downhole recording of strain wavefields from microseismic events with spatial sampling down to ∼1 m. Exploiting this high spatial information density motivates different detection approaches than typically used for downhole geophones. A new machine learning method using convolutional neural networks is described that operates on the full strain wavefield. The method is tested using data recorded in a horizontal observation well during hydraulic fracturing in the Eagle Ford Shale, Texas, and the results are compared to a surface geophone array that simultaneously recorded microseismic activity. The neural network was trained using synthetic microseismic events injected into real ambient noise, and it was applied to detect events in the remaining data. There were 535 detections found and no false positives. In general, the signal-to-noise ratio of events recorded by distributed acoustic sensing was lower than the surface array and 368 of 933 surface array events were found. Despite this, 167 new events were found in distributed acoustic sensing data that had no detected counterpart in the surface array. These differences can be attributed to the different detection threshold that depends on both magnitude and distance to the optical fibre. As distributed acoustic sensing data quality continues to improve, neural networks offer many advantages for automated, real-time microseismic event detection, including low computational cost, minimal data pre-processing, low false trigger rates and continuous performance improvement as more training data are acquired.     

A neural network approach for the real-time detection of faults

Fault detection is an essential part of the operation of any chemical plant. Early detection of faults is important in chemical industry since a lot of damage and loss can result before a fault present in the system is detected. Even though fault detection algorithms are designed and implemented for quickly detecting incidents, most these algorithms do not have an optimal property in terms of detection delay with respect to false alarm rate. Based on the optimization property of cumulative sum (CUSUM), a real-time system for detecting changes in dynamic systems is designed in this paper. This work is motivated by combining two fault detection (FD) strategies; a simplified procedure of the incident detection problem is formulated by using both the artificial neural networks (ANN) and the CUSUM statistical test (Page–Hinkley test). The design of a model-based residual generator is intended to reveal any drift from the normal behavior of the process. In order to obtain a reliable model for the normal process dynamics, the neural black-box modeling by means of a nonlinear auto-regressive with eXogenous input (NARX) model has been chosen in this study. This paper also shows the choice and the performance of the neural network in the training and test phases. After describing the system architecture and the proposed methodology of the fault detection, we present a realistic application in order to show the technique’s potential. The purpose is to develop and test the fault detection method on a real incident data, to detect the change presence, and pinpoint the moment it occurred. The experimental results demonstrate the robustness of the FD method.     

A system identification approach to the detection of changes in both linear and non-linear structural parameters

This paper explores the potential of a new time domain identification procedure to detect changes in structural dynamic characteristics on the basis of measurements. This procedure is verified using mathematical models simulated on the computer. The experiments involve two eight-storey steel structures with and without energy devices, and a 47-storey building at San Francisco during the Loma Prieta earthquake. The recursive instrumental variable method and extended Kalman filter algorithm are used as identification algorithms. An exploratory investigation is made of the usefulness of various indices, such as mode shape and storey drift, that can be extracted accurately from identification to quantify changes in the characteristics of the physical system. It is concluded that the change of storey drift is the key information to the detection of changes in structural parameters, from which the proposed system identification algorithm can be applied with an appropriate inelastic model to simulate the dynamic behaviour of real structures undergoing strong ground motion excitations.     

Research Note: Frequency domain orthogonal projection filtration of surface microseismic monitoring data

We address the problem of increasing the signal-to-noise ratio during surface microseismic monitoring data processing. Interference from different seismic waves causes misleading results of microseismic event locations. Ground-roll suppression is particularly necessary. The standard noise suppression techniques assume regular and dense acquisition geometries. Many pre-processing noise suppression algorithms are designed for special types of noise or interference. To overcome these problems, we propose a novel general-purpose filtration method. The goal of this method is to amplify only the seismic waves that are excited in the selected target area and suppress all other signals. We construct a linear projector onto a frequency domain data subspace, which corresponds to the seismic emission of the target area. The novel filtration method can be considered an extension of the standard frequency–wavenumber flat wave filtration method for non-flat waves and arbitrary irregular receiver-position geometries. To reduce the effect of the uncertainty of the velocity model, we suggest using additional active shot data (typically the perforation shots), which provide static travel time corrections for the target area. The promising prospects of the proposed method are confirmed by synthetic and semi-synthetic data processing.     

基于机器学习算法的银川台阵资料自动化震相分析和定位

近年来快速发展的机器学习算法显著提高了震相拾取的精度和效率.采用卷积神经网络和递归神经网络的震相识别方法对银川台阵2019年6～7月的连续波形数据进行事件检测和P、S震相拾取,并通过快速震相关联和事件定位得到了银川地区较全的地震目录.结果表明,当震相数小于10时,虽然可以检测出较多事件,但分布呈弥散状,与区域地震活动特...     

Exponential data fitting applied to infiltration, hydrograph separation, and variogram fitting

Most lumped rainfall-runoff models separate the interflow and groundwater components from the measured runoff hydrograph in an attempt to model these as hydrologic reservoir units. Similarly, rainfall losses due to infiltration as well as other abstractions are separated from the measured rainfall hyetograph, which are then used as inputs to the various hydrologic reservoir units. This data pre-processing is necessary in order to use the linear unit hydrograph theory, as well as for maintaining a hydrologic budget between the surface and subsurface flow processes. Since infiltration determines the shape of the runoff hydrograph, it must be estimated as accurately as possible. When measured infiltration data is available, Horton’s exponential infiltration model is preferable due to its simplicity. However, estimating the parameters from Horton’s model constitutes a nonlinear least squares fitting problem. Hence, an iterative procedure that requires initialization is subject to convergence. In a similar context, the separation of direct runoff, interflow, and baseflow from the total hydrograph is typically done in an ad hoc manner. However, many practitioners use exponential models in a rather “layer peeling” fashion to perform this separation. In essence, this also constitutes an exponential data fitting problem. Likewise, certain variogram functions can be fitted using exponential data fitting techniques. In this paper we show that fitting a Hortonian model to experimental data, as well as performing hydrograph separation, and total hydrograph and variogram fitting can all be formulated as a system identification problem using Hankel-based realization algorithms. The main advantage is that the parameters can be estimated in a noniterative fashion, using robust numerical linear algebra techniques. As such, the system identification algorithms overcome the problem of convergence inherent in iterative techniques. In addition, the algorithms are robust to noise in the data since they optimally separate the signal and noise subspaces from the observed noisy data. The algorithms are tested with real data from field experiments performed in Surinam, as well as with real hydrograph data from a watershed in Louisiana. The system identification techniques presented herein can also be used with any other type of exponential data such as exponential decays from nuclear experiments, tracer studies, and compartmental analysis studies.     

Reproduction of Pechora runoff hydrographs with the help of a model of heat and water exchange between the land surface and the atmosphere (SWAP)

The potentialities of a procedure for calculating the Pechora River runoff from the pan-Arctic river basin are studied. The procedure is based on the use of a model describing heat and water exchange between the land surface and the atmosphere and two variants of input data sets relying on global databases on meteorological characteristics and land surface parameters and data of standard measurements of meteorological characteristics in combination with parameters of the land surface of the basin, taken from global databases. In both cases, use was made of the method for optimizing part of the most important model parameters, including both land surface parameters and correction factors for some meteorological elements.     

METHODS FOR CONTOURING IRREGULARLY SPACED DATA*

The sampling theorem in two dimensions univocally defines a surface, provided that its values are known at points disposed on a regular lattice. If the data are irregularly spaced, the usual procedure is first to interpolate the surface on a regular grid and then to contour the interpolated data: however, the resulting surface will not necessarily assume the prescribed values on the irregular grid. One way to obtain this result is to introduce a transformation of the coordinates such that all the original data points are transferred into part of the nodes of a regular grid. The surface is then interpolated in the points correspondent to the other crosspoints of the regular grid; the contour lines are determined in the transformed plane and then, using the inverse coordinate transformation, are transferred back to the original plane where they will certainly be congruent with the original data points. Nonetheless, the resulting surface is very sensitive to the interpolation method used: two algorithms for that are analyzed. The first (harmonization) corresponds to the determination of the potential of an electrical field whose contour conditions are those defined by the data points. The second method consists in two dimensional statistical estimation (krigeing); in particular, the effects of different choices for the data auto-covariance function are discussed. The solutions are compared and some practical results are shown.     

固体潮观测数据的预处理

本文介绍了固体潮观测数据的平滑检验和NAKAI法检验之方法原理和具体应用。平滑检验可以有效地监测並改正观测数据中的随机错误,突跳或坏读数;经平滑改正后,可使观测数据的读数误差小于1毫米。用NAKAI(1975)提出的方法检验观测数据可以监测仪器灵敏度变化情况和数据质量好坏,根据该检验计算获得的有关参数(主要是漂移系数和方差)剔除质量不好的48小时数据组或修正某些质量不好的数据可提高调和分析结果的精度。