Two-dimensional inversion of spectral induced polarization data using MPI parallel algorithm in data space

Traditional two-dimensional（2D） complex resistivity forward modeling is based on Poisson＇s equation but spectral induced polarization（SIP） data are the coproducts of the induced polarization（IP） and the electromagnetic induction（EMI） effects.This is especially true under high frequencies,where the EMI effect can exceed the IP effect.2D inversion that only considers the IP effect reduces the reliability of the inversion data.In this paper,we derive differential equations using Maxwell＇s equations.With the introduction of the Cole-Cole model,we use the finite-element method to conduct2 D SIP forward modeling that considers the EMI and IP effects simultaneously.The data-space Occam method,in which different constraints to the model smoothness and parametric boundaries are introduced,is then used to simultaneously obtain the four parameters of the Cole-Cole model using multi-array electric field data.This approach not only improves the stability of the inversion but also significantly reduces the solution ambiguity.To improve the computational efficiency,message passing interface programming was used to accelerate the 2D SIP forward modeling and inversion.Synthetic datasets were tested using both serial and parallel algorithms,and the tests suggest that the proposed parallel algorithm is robust and efficient.

Research and application of spectral inversion technique in frequency domain to improve resolution of converted PS-wave

Multi-wave exploration is an effective means for improving precision in the exploration and development of complex oil and gas reservoirs that are dense and have low permeability. However, convened wave data is characterized by a low signal-to-noise ratio and low resolution, because the conventional deconvolution technology is easily affected by the frequency range limits, and there is limited scope for improving its resolution. The spectral inversion techniques is used to identify λ/8 thin layers and its breakthrough regarding band range limits has greatly improved the seismic resolution. The difficulty associated with this technology is how to use the stable inversion algorithm to obtain a high-precision reflection coefficient, and then to use this reflection coefficient to reconstruct broadband data for processing. In this paper, we focus on how to improve the vertical resolution of the converted PS-wave for multi-wave data processing. Based on previous research, we propose a least squares inversion algorithm with a total variation constraint, in which we uses the total variance as a priori information to solve under-determined problems, thereby improving the accuracy and stability of the inversion. Here, we simulate the Gaussian fitting amplitude spectrum to obtain broadband wavelet data, which we then process to obtain a higher resolution converted wave. We successfully apply the proposed inversion technology in the processing of high-resolution data from the Penglai region to obtain higher resolution convened wave data, which we then verify in a theoretical test. Improving the resolution of converted PS-wave data will provide more accurate data for subsequent velocity inversion and the extraction of reservoir reflection information.

Chlorophyll Content Retrieval of Rice Canopy with Multi-spectral Inversion Based on LS-SVR Algorithm

To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.

Optimization of Spectral Inverters in Optical Fiber Communication Systems with Mid-Span Spectrum Inversion

In this paper, signal distortions caused by spectral inverters which are used in 10Gb/s optical fiber communication systems with mid span spectrum inversion in terms of dispersion shift fibers are analyed numerically. It is shown that there exist the optimal input signal power, fiber length and fiblter bandwidth for the spectral in verters. System transmission penalty can be minimized by optimizing these parameters.

155 Mb/s-10 Gb/s combined FSK-IM/optical label-packet modulation signals 100 km transmission over standard single mode fiber using mid-span spectral inversion by four-wave mixing in an SOA

This paper introduces the mid-span spectral inversion by four-wave mixing in a commercially available semiconductor optical amplifier （SOA） with a length of about 1.5 mm to optical label switching network based on combined frequency shift keying （FSK）-intensiy modulation （IM）/optical label-packet modulation to overcome the dispersion limitation of fiber. The 155 Mb/s-10 Gb/s combined FSK/IM signal is experimentally transmitted over a 100 km standard single mode fiber. 10^-10 and 10^-9 BER （bit error ratio）, or even better, is achieved for the FSK label and IM packet, respectively. The -19 dB power conversion efficiency is obtained for -1 nm wavelength detuning.

Research of inverse mathematical model to high-speed trains

Operation safety and stability of the train mainly depend on the interaction between the wheel and rail.Knowledge of wheel/rail contact force is important for vehicle control systems that aim to enhance vehicle stability and passenger safety.Since wheel/rail contact forces of high-speed train are very difficult to measure directly,a new estimation process for wheel/rail contact forces was introduced in this work.Based on the state space equation,dynamic programming methods and the Bellman principle of optimality,the main theoretical derivation of the inversion mathematical model was given.The new method overcomes the weakness of large fluctuations which exist in current inverse techniques.High-speed vehicle was chosen as the research object,accelerations of axle box as input conditions,10 degrees of freedom vertical vibration model and 17 degrees of freedom lateral vibration model were established,respectively.Under 250 km/h,the vertical and lateral wheel/rail forces were identified.From the time domain and frequency domain,the comparison of the results between inverse and SIMPACK models were given.The results show that the inverse mathematical model has high precision for inversing the wheel/rail contact forces of an operation high-speed vehicle.

Full waveform inversion with spectral conjugategradient method

Spectral conjugate gradient method is an algorithm obtained by combination of spectral gradient method and conjugate gradient method,which is characterized with global convergence and simplicity of spectral gradient method,and small storage of conjugate gradient method.Besides,the spectral conjugate gradient method was proved that the search direction at each iteration is a descent direction of objective function even without relying on any line search method.Spectral conjugate gradient method is applied to full waveform inversion for numerical tests on Marmousi model.The authors give a comparison on numerical results obtained by steepest descent method,conjugate gradient method and spectral conjugate gradient method,which shows that the spectral conjugate gradient method is superior to the other two methods.

Tomographic inversion of near-surface Q factor by combining surface and cross-hole seismic surveys

The estimation of the quality factor Q plays a fundamental role in enhancing seismic resolution via absorption compensation in the near-surface layer.We present a new geometry that can be used to acquire field data by combining surface and cross-hole surveys to decrease the effect of geophone coupling on Q estimation.In this study,we drilled number of receiver holes around the source hole,each hole has different depth and each geophone is placed geophones into the bottom of each receiver hole to avoid the effect of geophone coupling with the borehole wall on Q estimation in conventional cross-hole seismic surveys.We also propose a novel tomographic inversion of the Q factor without the effect of the source signature,and examine its stability and reliability using synthetic data.We estimate the Q factors of the near-surface layer in two different frequency bands using field data acquired in the Dagang Oilfield.The results show that seismic absorption in the nearsurface layer is much greater than that in the subsurface strata.Thus,it is of critical practical importance to enhance the seismic solution by compensating for near-surface absorption.In addition,we derive different Q factors from two frequency bands,which can be treated,to some extent,as evidence of a frequency-dependent Q.

Interval Q inversion based on zero-offset VSP data and applications

In order to obtain stable interval Q factor, by analyzing the spectrum of monitoring wavelet and down-going wavelet of zero-offset VSP data and referring the spectrum expression of Ricker wavelet, we propose a new expression of source wavelet spectrum. Basing on the new expression, we present improved amplitude spectral fitting and spectral ratio methods for interval Q inversion based on zero-offset VSP data, and the sequence for processing the zero-offset VSP data. Subsequently, we apply the proposed methods to real zero-offset VSP data, and carry out prestack inverse Q filtering to zero-offset VSP data and surface seismic data for amplitude compensation with the estimated Q value.

Dynamic Analysis of High-Speed Boat Motion Simulator by a Novel 3-DoF Parallel Mechanism with Prismatic Actuators Based on Seakeeping Trial

In this study,we focused on a novel parallel mechanism for utilizing the motion simulator of a high-speed boat(HSB).First,we expressed the real behavior of the HSB based on a seakeeping trial.For this purpose,we recorded the motion parameters of the HSB by gyroscope and accelerometer sensors,while using a special data acquisition technique.Additionally,a Chebychev highpass filter was applied as a noise filter to the accelerometer sensor.Then,a novel 3 degrees of freedom(DoF)parallel mechanism(1T2R)with prismatic actuators is proposed and analyses were performed on its inverse kinematics,velocity,and acceleration.Finally,the inverse dynamic analysis is presented by the principle of virtual work,and the validation of the analytical equations was compared by the ADAMS simulation software package.Additionally,according to the recorded experimental data of the HSB,the feasibility of the proposed novel parallel mechanism motion simulator of the HSB,as well as the necessity of using of the washout filters,was explored.

Inverse spectral decomposition using an I_p-norm constraint for the detection of close geological anomalies

An important application of spectral decomposition(SD)is to identify subsurface geological anomalies such as channels and karst caves,which may be buried in full-band seismic data.However,the classical SD methods including the wavelet transform(WT)are often limited by relatively low time-frequency resolution,which is responsible for false high horizonassociated space resolution probably indicating more geological structures,especially when close geological anomalies exist.To address this issue,we impose a constraint of minimizing an lp(0<p<1)norm of time-frequency spectral coefficients on the misfit derived by using the inverse WT and apply the generalized iterated shrinkage algorithm to invert for the optimal coefficients.Compared with the WT and inverse SD(ISD)using a typical l1-norm constraint,the modified ISD(MISD)using an lp-norm constraint can yield a more compact spectrum contributing to detect the distributions of close geological features.We design a 3 D synthetic dataset involving frequency-close thin geological anomalies and the other3 D non-stationary dataset involving time-close anomalies to demonstrate the effectiveness of MISD.The application of 4 D spectrum on a 3 D real dataset with an area of approximately 230 km2 illustrates its potential for detecting deep channels and the karst slope fracture zone.

Experimental Research on Quantitative Inversion Models of Suspended Sediment Concentration Using Remote Sensing Technology

Research on quantitative models of suspended sediment concentration （SSC） using remote sensing technology is very important to understand the scouting and siltation variation in harbors and water channels. Based onlaboratory study of the relationship between different suspended sediment concentrations and reflectance spectra measured synchronously, quantitative inversion models of SSC based on single factor, band ratio and sediment parameter were developed, which provides an effective method to retrieve the SSC from satellite images. Results show that the bl （430-500nm） and b3 （670-735nm） are the optimal wavelengths for the estimation of lower SSC and the b4 （780-835nm） is the optimal wavelength to estimate the higher SSC. Furthermore the band ratio B2/B3 can be used to simulate the variation of lower SSC better and the B4/B1 to estimate the higher SSC accurately. Also the inversion models developed by sediment parameters of higher and lower SSCs can get a relatively higher accuracy than the single factor and band ratio models.

隧道不良地质识别:方法、现状及智能化发展方向

随着隧道施工对于不良地质识别精度要求的不断提高以及人工智能技术的发展,融合多源信息的不良地质智能化识别已成为发展趋势。本文首先阐述了常见的6种隧道不良地质类型及其地质成因,回顾分析了隧道主要的不良地质识别方法及现状,详细介绍了笔者在不良地质智能化识别方面的探索性研究:基于机器学习利用图像识别技术对隧道围岩岩性与裂隙特征进行智能识别;融合图像和光谱特征进行不良地质识别;将地化分析融入到传统的超前钻探中,融合随钻参数和地化信息进行不良地质随钻识别,既可以发挥超前钻探在感知岩体质量和地层信息变化方面的优势,又可以发挥地化分析在岩性和不良地质异常识别方面的优势;基于地质与物探联合反演进行不良地质识别,旨在实现掌子面前方不良地质体“形”(位置、形态、规模)和“性”(性质和类型)的精确识别。最后,对隧道不良地质智能化识别的发展趋势进行了展望。

Generalized Invertibility of Operators through Spectral Sets

If an operator is not invertible, we are interested if there is a subspace such that the reduction of the operator to that subspace is invertible. In this paper we give a spectral approach to generalized inverses considering the subspace determined by the range of the spectral projection associated with an operator and a spectral set containing the point 0. We compare the cases, 0 is a simple pole of the resolvent function, 0 is a pole of order n of the resolvent function, 0 is an isolated point of the spectrum, and 0 is contained in a circularly isolated spectral set.

基于Sentinel-2多光谱遥感影像的小浪底水质反演

多光谱遥感技术可根据遥感波段信息反演水质参数,降低监测成本,提高监测速度和质量,为大范围水环境监测提供了一种新的方法。通过分析小浪底水库的Sentinel-2多光谱影像以及采样点实测水质数据,建立了最佳光谱波段的水质参数反演模型,对小浪底水库的化学需氧量(COD)、总磷(TP)、总氮(TN)和氨氮(NH_3-N)进行了遥感反演,验证了反演模型的精确度和稳定性,并反演了各水质参数的空间分布规律。结果表明:在4种水质参数反演模型中,COD模型精确度和稳定性最高,其次是TP、TN,最低的是NH_3-N,水库出水口和部分边缘COD质量浓度较高,水库中心TN、TP和NH_3-N质量浓度高于边缘处。

塔里木盆地星火井区深层薄储层地震预测

星火井区巴什基奇克组顶部储层埋深超过5 000 m,为超深层强非均质性碎屑岩储层,是塔里木盆地新和地区重要生产层段。储层埋藏深度大、厚度薄、研究区完钻井数量少,勘探开发难度很大。由于传统的地震预测技术精度不够,薄储层预测一直是制约星火井区超深层油气藏勘探开发的瓶颈技术。为落实星火井区薄储层分布特征,利用模型正演明确了星火井区超深薄储层在不同分辨率模型下的地震响应特征、基于谱反演方法的拓频技术提高地震分辨率,识别出超深层6 m以内的薄储层,进而使用波形指示反演技术刻画出薄储层的展布特征。薄储层预测结果与实钻井结果的吻合率超过87%,证明正演-拓频-反演技术在星火井区具有很高的准确性。研究结果可以为与星火井区地质背景相同地区的油藏开发提供借鉴。

具有延迟参数的Sturm-Liouville算子的谱特征及其反问题

具有延迟参数的Sturm-Liouville算子的逆谱问题是Sturm-Liouville理论的一个重要分支.延迟参数的Sturm-Liouville微分方程可用于对声波信号的传输以及液压冲击或其他波过程的模拟.本文主要研究有限区间上具有延迟变量的二阶Sturm-Liouville算子的特征值及其逆谱问题,利用势函数的Fourier展开式的方法得出了相应的逆谱问题的唯一性定理,即在某些假设条件下由边值问题的两组谱可以唯一确定延迟变量τ和势函数q(x).最后本文给出势函数的重构算法.

基于GF-1数据的耕地土壤镉(Cd)含量遥感估算方法

本文采用多种光谱变换和回归分析方法探索了使用GF-1卫星影像监测耕地土壤镉(Cd)含量的可行性。首先针对获取的GF-1原始影像数据,在完成预处理及剔除植被信息后进行倒对数、平方根和反正弦平方根变换,生成4套光谱影像;然后分别用采样点5 m缓冲区内各套影像光谱统计值与Cd含量进行相关性分析和多种回归分析。选择模型决定系数最高(>95%)的反正弦平方根变换后的自适应重加权回归方法构建的线性回归模型作为遥感估算模型。遥感估算结果在稻田积水、边缘地带等出现了异常估算值;笔者分析原因后应用线性插值的方法得到最终估算结果。相关性分析和建模精度表明该方法是可行的,有望应用于实际土壤质量监测和土地管理中。

基于波谱响应特征的雄安新区农田土壤重金属含量反演

分析并监测雄安新区农田土壤污染状况,对于保障粮食安全、建设绿色雄安具有重要意义。该研究以雄安新区为研究区,基于多源遥感数据珠海一号(Zhuhai-1)OHS数据、哨兵二号(Sentinel-2)L2A级数据的波谱响应特征及实地测得的农田土壤重金属含量数据,在对土壤重金属含量单因子与多因子污染评价的基础上,筛选出3种超标的农田重金属元素铅(Pb)、铜(Cu)、锌(Zn),采用偏最小二乘回归方法(partial least squares regression,PLSR)建立农田土壤重金属含量反演模型。利用Zhuhai-1提取土壤样本点的原始光谱反射率以及4种变换后的光谱反射率,Sentinel-2提取7种对重金属胁迫敏感的植被指数,将其与3种土壤重金属含量作相关性分析,筛选出敏感波段与植被指数,即波谱响应特征,构建土壤重金属含量反演模型。结果表明,3种模型整体反演精度较为优良,Pb含量反演模型决定系数(determination coefficient,R^(2))为0.490,均方根误差(root mean squared error,RMSE)为4.66 mg/kg,平均绝对值误差(mean absolute error,MAE)为1.92 mg/kg;Cu含量反演模型R^(2)为0.491,RMSE为16.85 mg/kg,MAE为3.69 mg/kg;Zn含量反演模型R^(2)为0.664,RMSE为20.63 mg/kg,MAE为9.36 mg/kg。将该模型应用于雄安新区农田区域,得到大部分农田土壤中Pb含量均未超过风险筛选标准,在研究区西南部、西部部分区域Cu含量超过土壤污染风险筛选值,同时在研究区西部、西南部Zn污染较严重,雄安新区东南部部分农田有Zn零星分布,其他区域Cu和Zn含量未超过国家土壤污染风险管控值。因此,利用多源遥感数据波谱响应特征反演土壤重金属Pb、Cu和Zn含量,能够快速准确地实现对雄安新区土壤重金属污染情况的调查,同时为大面积土壤重金属含量监测提供解决方案。

基于采样点光谱信息窗口尺度优化的土壤含水率无人机多光谱遥感反演

针对空间异质性导致的土壤含水率反演误差较大的问题,分别以玉米灌浆期和小麦苗期的土壤含水率反演为例,利用无人机多光谱遥感技术获取喷灌和畦灌灌溉方式下的正射影像。将34组光谱特征变量按照滑动窗口法提取不同空间尺度的光谱信息平均值,通过极端梯度提升(Extreme gradient boosting, XGBoost)、支持向量机回归(Support vector machine regression, SVR)以及偏最小二乘回归(Partial least squares regression, PLSR)3种机器学习模型确定采样点光谱信息最优窗口尺度;然后,采用皮尔逊相关系数特征变量筛选法(Pearson correlation coefficient feature variable screening method, R)结合XGBoost和SVR模型对提取的34组光谱特征变量进行筛选,选取与土壤含水率敏感的特征变量;最后,估算土壤含水率。结果表明:喷灌方式下所选择的采样点最优光谱信息窗口尺度比畦灌小,其最优窗口尺度范围分别为11×11~21×21和15×15~29×29;采用皮尔逊相关系数特征变量筛选方法结合机器学习模型可有效提高土壤含水率反演精度;5种机器学习模型(R_XGBoost、R_SVR、XGBoost、SVR、PLSR)中R_XGBoost模型估算土壤含水率精度最优,在喷灌和畦灌方式下玉米灌浆期R_XGBoost模型的测试集决定系数R2分别为0.80、0.83,均方根误差(Root mean square error, RMSE)分别为1.27%和0.98%,小麦苗期R2分别为0.76、0.79,RMSE分别为1.68%和0.85%;土壤含水率反演模型在畦灌条件下的精度优于喷灌条件下。该研究可为基于无人机多光谱影像分析的信息挖掘和土壤水分监测提供参考。