基于随机森林算法检出hs-cTnT项目小偏移的实时质控方法

目的利用机器学习随机森林(random forest,RF)算法,建立一种准确识别检测小偏移的实时质控方法,并以浮动异常值之和的方法(moving sum of outlier,MovSO)作为参比方法,评价新算法效能。方法收集来自航空总医院实验室信息系统导出的2016年1月至2021年8月在罗氏化学发光E601设备检测的hs-cTnT项目检测结果,按照规定的数据清洗规则筛选出54243个结果作为无偏数据,人为引入10个不同大小的偏移,生成相应的有偏数据,每种偏移下用RF与MovSO两种算法进行实验。采用分类模型标准及临床指标对算法进行评价。结果RF算法在浮动窗口大小为10时,对10个小偏移均能检出,假阳性率(FPR)为4.0%~4.7%,受影响的患者样本数中位数(MNPed)在12以下。除了在±1 ng/L偏移时准确度为85%,其余8个偏移检出准确度均在90%以上;MovSO算法的最优浮动窗口大小为200,除了在1 ng/L时偏移无法检出,对其他9个偏移均可检出,FPR在3.5%~4.6%之间,MNPed均在100以上,仅在5 ng/L偏移时识别准确度方可达89%。RF算法总体显著优于MovSO,RF可准确识别hs-cTnT的测量小偏移。结论基于机器学习RF算法建立的质控方法可以改进类似hs-cTnT等临床对检测质量要求较高的项目的测量准确度,为实验室质控方案提供了新思路。

小偏移锥齿轮传动的评价方法

针对弧齿锥齿轮传动敏感性评价,一般文献把齿轮当作绝对刚体。着重将齿轮当作弹性体进行研究,分析了敏感性产生的原因及对传动性能的影响,提出了用传动质量向量及传动敏感性矩阵的评价方法。通过实验研究证实了矩阵法评价比较优越,不仅简单,而且计算量小。

利用偏移距衰减属性裂缝储层检测方法

变质岩潜山是辽东湾JZ区块重要的油气勘探开发层位。通过对JZ区块潜山裂缝储层的研究,提出一种通过裂缝地层在大、小偏移距地震资料中的衰减梯度差异(DALLO分析)检测裂缝储层的方法。DALLO分析方法实现了在窄方位角采集条件下三维区块的裂缝储层发育程度的预测。根据预测结果,JZ区块潜山裂缝储层分布在距离潜山顶面一定距离的"带"内,且具有显著的非均质特征。

洞内极小偏移距地震法在断层探测中的应用研究

在深埋、长距离隧洞工程中,由于施工前期的地表地质调查与地表物探调查无法准确查明断层、溶蚀带等中大型不良地质构造的规模与位置,隧洞施工过程中,如何对这些不良地质构造,特别是与隧洞斜交的断层进行准确的定位与识别,对工程安全至关重要.本文中,笔者创新地提出一种垂直反射法与TGS法组合的洞内极小偏移距地震法隧洞侧向断层探测方案,这两种方法不需要足够大的偏移距,适用于隧洞、巷道等空间较狭小的地下工程,并结合实际工程开展了现场应用研究工作,最终归纳出一套适用于隧洞与断层斜交的洞内断层探测技术方案与流程,对保障隧洞施工安全具有一定的指导意义.

陆地声纳法及其在岩溶探测中的应用

介绍了陆地声纳法(陆上极小偏移距高频弹性波反射连续剖面法)的基本原理和方法,通过对某高速公路基岩地震波探测剖面的实例分析,表明该方法与常规物探方法相比,可避免许多干扰波,可单道连续采集,并具有分辨率高、偏移距极小、反射波能量高等特点。在地下岩溶等浅层地震地质调查中效果良好,可满足狭窄、复杂环境下的高分辨率浅层地震勘探的需要。

基于主成分修整和线性判别分析的重叠故障识别

针对在二维空间中,由变量均值偏移量较小但协方差不变所导致地故障类重叠,并因此降低故障识别率的问题,提出一种基于主成分分析(PCA)修整和线性判别分析(LDA)的新方法(PLDA).该方法通过减弱不同故障类的重叠主成分对分类的影响,达到提升LDA故障识别率的目的.对24种具有代表性故障组合的模拟样本运用10次10倍交叉验证,试验结果表明PLDA算法的平均故障识别率为94.6%,远高于传统的核化LDA算法和LDA算法的60.0%和61.9%.

基于马尔可夫链的EWMA控制图参数优化及其实现

EWMA控制图的性能取决于其参数的选取,学术界和工业界常采用平均运行链长作为控制图性能评价指标,本文对基于马尔可夫链的EWMA控制图的参数(平滑系数、控制线参数)优化问题进行了深入的研究:给出了一种EWMA控制图参数优化方法及一般步骤;采用matlab平台对该算法进行研究并基于J2EE平台实现EWMA控制图参数优化系统界面,给出示例计算,并与传统的控制图进行了比较研究。

蛟岭隧道洞顶塌方区地震探测

介绍了在岩溶地区复杂地质条件下,对隧道塌方区进行地震勘探的技术和方法.对于蛟岭隧道塌方区应用弹性波方法,采取一次性布置检波器、多炮激震的观测方式,并运用地震波动力学和运动学特征对地震资料进行解释的方法,确定隧道上方地质灾害体的赋存位置和形态,取得了良好效果.

Multisource least-squares reverse-time migration with structure-oriented filtering

The technology of simultaneous-source acquisition of seismic data excited by several sources can significantly improve the data collection efficiency. However, direct imaging of simultaneous-source data or blended data may introduce crosstalk noise and affect the imaging quality. To address this problem, we introduce a structure-oriented filtering operator as preconditioner into the multisource least-squares reverse-time migration （LSRTM）. The structure-oriented filtering operator is a nonstationary filter along structural trends that suppresses crosstalk noise while maintaining structural information. The proposed method uses the conjugate-gradient method to minimize the mismatch between predicted and observed data, while effectively attenuating the interference noise caused by exciting several sources simultaneously. Numerical experiments using synthetic data suggest that the proposed method can suppress the crosstalk noise and produce highly accurate images.

Preconditioned prestack plane-wave least squares reverse time migration with singular spectrum constraint

Least squares migration can eliminate the artifacts introduced by the direct imaging of irregular seismic data but is computationally costly and of slow convergence. In order to suppress the migration noise, we propose the preconditioned prestack plane-wave least squares reverse time migration （PLSRTM） method with singular spectrum constraint. Singular spectrum analysis （SSA） is used in the preconditioning of the take-off angle-domain common-image gathers （TADCIGs）. In addition, we adopt randomized singular value decomposition （RSVD） to calculate the singular values. RSVD reduces the computational cost of SSA by replacing the singular value decomposition （SVD） of one large matrix with the SVD of two small matrices. We incorporate a regularization term into the preconditioned PLSRTM method that penalizes misfits between the migration images from the plane waves with adjacent angles to reduce the migration noise because the stacking of the migration results cannot effectively suppress the migration noise when the migration velocity contains errors. The regularization imposes smoothness constraints on the TADCIGs that favor differential semblance optimization constraints. Numerical analysis of synthetic data using the Marmousi model suggests that the proposed method can efficiently suppress the artifacts introduced by plane-wave gathers or irregular seismic data and improve the imaging quality of PLSRTM. Furthermore, it produces better images with less noise and more continuous structures even for inaccurate migration velocities.

Mean shift based log-Gabor wavelet image coding

In this paper, we propose a sparse overcomplete image approximation method based on the ideas of overcomplete log-Gabor wavelet, mean shift and energy concentration. The proposed approximation method selects the necessary wavelet coefficients with a mean shift based algorithm, and concentrates energy on the selected coefficients. It can sparsely approximate the original image, and converges faster than the existing local competition based method. Then, we propose a new compression scheme based on the above approximation method. The scheme has compression performance similar to JPEG 2000. The images decoded with the proposed compression scheme appear more pleasant to the human eyes than those with JPEG 2000.

Fractional Frequency Offset Estimation for OFDM Systems in Non-Cooperative Communication

The traditional fractional frequency offset(FFO) estimation schemes for orthogonal frequency division multiplexing(OFDM) in non-cooperative communication have the problems of susceptible performance with the frequency offset values and the number of OFDM symbols,a novel fractional frequency offset blind estimation scheme based on EKF for OFDM systems is conceived.The nonlinear function of the frequency offset is calculated by employing the correlation.And then the frequency offset is estimated by means of the iterative algorithm of EKF.The finally fractional frequency offset is estimated by adopting repeated the above process.Simulation results demonstrate that the proposed scheme is robust to the frequency offset values without any requirements of a prior knowledge.

Multiparameter least-squares reverse time migration for acoustic–elastic coupling media based on ocean bottom cable data

In marine seismic exploration,ocean bottom cable technology can record multicomponent seismic data for multiparameter inversion and imaging.This study proposes an elastic multiparameter lease-squares reverse time migration based on the ocean bottom cable technology.Herein,the wavefield continuation operators are mixed equations:the acoustic wave equations are used to calculate seismic wave propagation in the seawater medium,whereas in the solid media below the seabed,the wavefields are obtained by P-and S-wave separated vector elastic wave equations.At the seabed interface,acoustic–elastic coupling control equations are used to combine the two types of equations.P-and S-wave separated elastic migration operators,demigration operators,and gradient equations are derived to realize the elastic least-squares reverse time migration based on the P-and S-wave mode separation.The model tests verify that the proposed method can obtain high-quality images in both the P-and S-velocity components.In comparison with the traditional elastic least-squares reverse time migration method,the proposed method can readily suppress imaging crosstalk noise from multiparameter coupling.

Least-squares reverse time migration method using the factorization of the Hessian matrix

Least-squares reverse time migration(LSRTM)can eliminate imaging artifacts in an iterative way based on the concept of inversion,and it can restore imaging amplitude step by step.LSRTM can provide a high-resolution migration section and can be applied to irregular and poor-quality seismic data and achieve good results.Steeply dipping refl ectors and complex faults are imaged by using wavefi eld extrapolation based on a two-way wave equation.However,the high computational cost limits the method’s application in practice.A fast approach to realize LSRTM in the imaging domain is provided in this paper to reduce the computational cost signifi cantly and enhance its computational effi ciency.The method uses the Kronecker decomposition algorithm to estimate the Hessian matrix.A low-rank matrix can be used to calculate the Kronecker factor,which involves the calculation of Green’s function at the source and receiver point.The approach also avoids the direct construction of the whole Hessian matrix.Factorization-based LSRTM calculates the production of low-rank matrices instead of repeatedly calculating migration and demigration.Unlike traditional LSRTM,factorization-based LSRTM can reduce calculation costs considerably while maintaining comparable imaging quality.While having the same imaging eff ect,factorization-based LSRTM consumes half the running time of conventional LSRTM.In this regard,the application of factorization-based LSRTM has a promising advantage in reducing the computational cost.Ambient noise caused by this method can be removed by applying a commonly used fi ltering method without signifi cantly degrading the imaging quality.

Steeply dipping structural target-oriented viscoacoustic least-squares reverse time migration and its application

Steeply dipping structural imaging is a significant challenge because surface geophones cannot obtain seismic primary reflection wave information from steeply dipping structures.Prismatic waves with a significant amount of steeply dipping information can be used to improve the imaging eff ect on steeply dipping structures.Subsurface attenuation leads to amplitude loss and phase distortion of seismic waves,and ignoring this attenuation during imaging can cause blurring of migration amplitudes.In this study,we proposed a steeply dipping structural target-oriented viscoacoustic least-squares reverse time migration(LSRTM)method with prismatic and primary waves as an objective function based on the viscous wave equation,while deriving Q-compensated wavefield propagation and joint operators of prismatic and primary waves and the Q-compensated demigration operator.Numerical examples on synthetic and field data verified the advantages of the proposed viscoacoustic LSRTM method of joint primary and prismatic waves over conventional viscoacoustic LSRTM and non-compensated LSRTM when using attenuating observed data.

A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high frequency of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept unchanged.As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor.The performance of the proposed model is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.

Pure q P-wave least-squares reverse time migration invertically transverse isotropic media and its application to field data

The anisotropic properties of subsurface media cause waveform distortions in seismic wave propagation,resulting in a negative infl uence on seismic imaging.In addition,wavefields simulated by the conventional coupled pseudo-acoustic equation are not only aff ected by SV-wave artifacts but are also limited by anisotropic parameters.We propose a least-squares reverse time migration(LSRTM)method based on the pure q P-wave equation in vertically transverse isotropic media.A fi nite diff erence and fast Fourier transform method,which can improve the effi ciency of the numerical simulation compared to a pseudo-spectral method,is used to solve the pure q P-wave equation.We derive the corresponding demigration operator,migration operator,and gradient updating formula to implement the LSRTM.Numerical tests on the Hess model and field data confirm that the proposed method has a good correction eff ect for the travel time deviation caused by underground anisotropic media.Further,it signifi cantly suppresses the migration noise,balances the imaging amplitude,and improves the imaging resolution.

Blind cancellation for frequency offset in OFDM system based on MCMA-RLS algorithm

Modified constant modulus and recursive least squares （MCMA-RLS） algorithm is proposed to cancel interference caused by the variable frequency offset （FO） in the orthogonal frequency division multiplexing （OFDM） system. The MCMA-RLS algorithm is composed of two stages including MCMA scheme and RLS scheme. MCMA is selected to pre-cancel the variable frequency offset firstly, and then the residual interference has been canceled by the RLS scheme. BR error rate is simulated to demonstrate that the proposed method is robust for canceling the variable frequency offset.

Least-squares reverse-time migration for reflectivity imaging

A least-squares reverse-time migration scheme is presented for reflectivity imaging. Based on an accurate reflection modeling formula, this scheme produces amplitude-preserved stacked reflectivity images with zero phase. Spatial preconditioning, weighting and the Barzilai-Borwein method are applied to speed up the convergence of the least-squares inversion. In addition, this scheme compensates the effect of ghost waves to broaden the bandwidth of the reflectivity images. Furthermore, roughness penalty constraint is used to regularize the inversion, which in turn stabilizes inversion and removes high-wavenumber artifacts and mitigates spatial aliasing. The examples of synthetic and field datasets demonstrate the scheme can generate zerophase reflectivity images with broader bandwidth, higher resolution, fewer artifacts and more reliable amplitudes than conventional reverse-time migration.

The improved element-free Galerkin method for three-dimensional transient heat conduction problems

With the improved moving least-squares (IMLS) approximation, an orthogonal function system with a weight function is used as the basis function. The combination of the element-free Galerkin (EFG) method and the IMLS approximation leads to the development of the improved element-free Galerkin (IEFG) method. In this paper, the IEFG method is applied to study the partial differential equations that control the heat flow in three-dimensional space. With the IEFG technique, the Galerkin weak form is employed to develop the discretized system equations, and the penalty method is applied to impose the essential boundary conditions. The traditional difference method for two-point boundary value problems is selected for the time discretization. As the transient heat conduction equations and the boundary and initial conditions are time dependent, the scaling parameter, number of nodes and time step length are considered in a convergence study.