UAV-based transient electromagnetic 3D forward modeling and inversion and analysis of exploration capability

Unmanned aerial vehicle transient electromagnetic(UAV-TEM)is a novel airborne exploration method that offers advantages such as low cost,simple operation,high exploration efficiency and suitability for near-surface exploration in complex terrain areas.To improve the accuracy of data interpretation in this method,the authors conducted a systematic three-dimensional(3D)forward modeling and inversion of the UAV-TEM.This study utilized the finite element method based on unstructured tetrahedral elements and employed the second-order backward Euler method for time discretization.This allowed for accurate 3D modeling and accounted for the effects of complex terrain.Based on these,the influence characteristics of flight altitudes and the sizes,burial depths,and resistivities of anomalies are compared and analyzed to explore the UAV-TEM systems’exploration capability.Lastly,four typical geoelectrical models of landslides are designed,and the inversion method based on the Gauss-Newton optimization method is used to image the landslide models and analyze the imaging effect of the UAV-TEM method on landslide geohazards.Numerical results showed that UAV-TEM could have better exploration resolution and fine imaging of nearsurface structures,providing important technical support for monitoring,early warning,and preventing landslides and other geological hazards.

Three-dimensional forward modeling and inversion of borehole-to-surface electrical imaging with different power sources

Borehole-to-surface electrical imaging （BSEI） uses a line source and a point source to generate a stable electric field in the ground. In order to study the surface potential of anomalies, three-dimensional forward modeling of point and line sources was conducted by using the finite-difference method and the incomplete Cholesky conjugate gradient （ICCG） method. Then, the damping least square method was used in the 3D inversion of the formation resistivity data. Several geological models were considered in the forward modeling and inversion. The forward modeling results suggest that the potentials generated by the two sources have different surface signatures. The inversion data suggest that the low- resistivity anomaly is outlined better than the high-resistivity anomaly. Moreover, when the point source is under the anomaly, the resistivity anomaly boundaries are better outlined than when using a line source.

Real-time forward modeling and inversion of logging-while-drilling electromagnetic measurements in horizontal wells

Based on the pseudo-analytical equation of electromagnetic log for layered formation,an optimal boundary match method is proposed to adaptively truncate the encountered formation structures.An efficient integral method is put forward to significantly accelerate the convergence of Sommerfeld integral.By asymptotically approximating and subtracting the first reflection/transmission waves from the scattered field,the new Sommerfeld integral method has addressed difficulties encountered by the traditional digital filtering method,such as low computational precision and limited operating range,and realized the acceleration of the computation speed of logging-while-drilling electromagnetic measurements(LWD EM).By making use of the priori information from the offset/pilot wells and interactively adjusting the formation model,the optimum initial guesses of the inversion model is determined in order to predict the nearby formation boundaries.The gradient optimization algorithm is developed and an interactive inversion system for the LWD EM data from the horizontal wells is established.The inverted results of field data demonstrated that the real-time interactive inversion method is capable of providing the accurate boundaries of layers around the wellbore from the LWD EM,and it will benefit the wellbore trajectory optimization and reservoir interpretation.

Multi-component joint inversion of gravity gradient based on fast forward calculation

With the development of gravity gradient full tensor measurement technique,three-dimensional( 3D) inversion based on gravity gradient tensor can provide more accurate information. But the forward calculation of 3D full tensor sensitivity matrix is very time-consuming,which restricts its development and application.According to the symmetry of the kernel function,the authors reconstruct the underground source of geological body to avoid repeat computation of the same value,and work out the corresponding relationship between the response of geological body to the observation point and the response of reconstructed geological body to the observation point. According to the relationship,rapid calculation of full tensor gravity sensitivity matrix can be achieved. The model calculation shows that this method can increase the speed of 30-45 times compared with the traditional calculation method. The sensitivity matrix is applied to the multi-component inversion of gravity gradient. The application of this method on the measured data provides the basis for the promotion of the method.

Contrast between 2D inversion and 3D inversion based on 2D high-density resistivity data

The 2D data processing adopted by the high-density resistivity method regards the geological structures as two degrees, which makes the results of the 2D data inversion only an approximate interpretation;the accuracy and effect can not meet the precise requirement of the inversion. Two typical models of the geological bodies were designed, and forward calculation was carried out using finite element method. The forward-modeled profiles were obtained. 1% Gaussian random error was added in the forward models and then 2D and 3D inversions using a high-density resistivity method were undertaken to realistically simulate field data and analyze the sensitivity of the 2D and 3D inversion algorithms to noise. Contrast between the 2D and 3D inversion results of least squares inversion shows that two inversion results of high-density resistivity method all can basically reflect the spatial position of an anomalous body. However, the 3D inversion can more effectively eliminate the influence of interference from Gaussian random error and better reflect the distribution of resistivity in the anomalous bodies. Overall, the 3D inversion was better than 2D inversion in terms of embodying anomalous body positions, morphology and resistivity properties.

Three-dimensional frequency-domain full waveform inversion based on the nearly-analytic discrete method

The nearly analytic discrete(NAD)method is a kind of finite difference method with advantages of high accuracy and stability.Previous studies have investigated the NAD method for simulating wave propagation in the time-domain.This study applies the NAD method to solving three-dimensional(3D)acoustic wave equations in the frequency-domain.This forward modeling approach is then used as the“engine”for implementing 3D frequency-domain full waveform inversion(FWI).In the numerical modeling experiments,synthetic examples are first given to show the superiority of the NAD method in forward modeling compared with traditional finite difference methods.Synthetic 3D frequency-domain FWI experiments are then carried out to examine the effectiveness of the proposed methods.The inversion results show that the NAD method is more suitable than traditional methods,in terms of computational cost and stability,for 3D frequency-domain FWI,and represents an effective approach for inversion of subsurface model structures.

Physics-informed deep learning for one-dimensional consolidation

Neural networks with physical governing equations as constraints have recently created a new trend in machine learning research.In this context,a review of related research is first presented and discussed.The potential offered by such physics-informed deep learning models for computations in geomechanics is demonstrated by application to one-dimensional(1D)consolidation.The governing equation for 1D problems is applied as a constraint in the deep learning model.The deep learning model relies on automatic differentiation for applying the governing equation as a constraint,based on the mathematical approximations established by the neural network.The total loss is measured as a combination of the training loss(based on analytical and model predicted solutions)and the constraint loss(a requirement to satisfy the governing equation).Two classes of problems are considered:forward and inverse problems.The forward problems demonstrate the performance of a physically constrained neural network model in predicting solutions for 1D consolidation problems.Inverse problems show prediction of the coefficient of consolidation.Terzaghi’s problem,with varying boundary conditions,is used as a numerical example and the deep learning model shows a remarkable performance in both the forward and inverse problems.While the application demonstrated here is a simple 1D consolidation problem,such a deep learning model integrated with a physical law has significant implications for use in,such as,faster realtime numerical prediction for digital twins,numerical model reproducibility and constitutive model parameter optimization.

Inversion of time-domain airborne EM data with IP effect based on Pearson correlation constraints

Due to the induced polarization(IP)eff ect,the sign reversal often occurs in timedomain airborne electromagnetic(AEM)data.The inversions that do not consider IP eff ect cannot recover the true umderground electrical structures.In view of the fact that there are many parameters of airborne induced polarization data in time domain,and the sensitivity diff erence between parameters is large,which brings challenges to the stability and accuracy of the inversion.In this paper,we propose an inversion mehtod for time-domain AEM data with IP effect based on the Pearson correlation constraints.This method uses the Pearson correlation coeffi cient in statistics to characterize the correlation between the resistivity and the chargeability and constructs the Pearson correlation constraints for inverting the objective function to reduce the non uniqueness of inversion.To verify the eff ectiveness of this method,we perform both Occam’s inversion and Pearson correlation constrained inversion on the synthetic data.The experiments show that the Pearson correlation constrained inverison is more accurate and stable than the Occam’s inversion.Finally,we carried out the inversion to a survey dataset with and without IP eff ect.The results show that the data misfit and the continuity of the inverted section are greatly improved when the IP eff ect is considered.

A three-dimensional transient electromagnetic data inversion method based on a time–frequency transformation

Herein,a three-dimensional(3D)inversion method in the frequency domain based on a time–frequency transformation was developed to improve the efficiency of the 3D inversion of transient electromagnetic(TEM)data.The Fourier transform related to the electromagnetic response in the frequency and time domains becomes a sine or cosine transform under the excitation of downward-step current.We established a transformation matrix based on the digital fi ltering calculation for the sine transform,and then the frequency domain projection of the TEM data was determined from the linear transformation system using the smoothing constrained least squares inversion method,in which only the imaginary part was used to maintain the TEM data transformation equivalence in the bidirectional projection.Thus,the time-domain TEM inversion problem was indirectly and effectively solved in the frequency domain.In the 3D inversion of the transformed frequency-domain data,the limited-memory Broyden–Fletcher–Goldfarb–Shannoquasi–Newton(L-BFGS)method was used and modifi ed with a restart strategy to adjust the regularization parameter when the algorithm tended to a local minimum.Synthetic data tests showed that our domain transformation method can stably project the TEM data into the frequency domain with very high accuracy;furthe rmore,the 3D inversion of the transformed frequency-domain data is stable,can be used to recover the real resistivity model with an acceptable effi ciency.

The Forward and Inverse Problem Based on Magneto-Acoustic Tomography with Current Injection

The Magneto-acoustic Tomography with Current Injection (MAT-CI) is a new biological electrical impedance imaging technique that combines Electrical Impedance Tomography (EIT) with Ultrasonic Imaging (UI), which possesses the non-invasive and high-contrast of the EIT and the high-resolution of the UI. The MAT-CI is expected to acquire high quality image and embraces a wide application. Its principle is to put the conductive sample in the Static Magnetic Field(SMF) and inject a time-varying current, during which the SMF and the current interact and generate the Lorentz Force that inspire ultrasonic signal received by the ultrasonic transducers positioned around the sample. And then according to related reconstruction algorithm and ultrasonic signal, electrical conductivity image is obtained. In this paper, a forward problem mathematical model of the MAT-CI has been set up to deduce the theoretical equation of the electromagnetic field and solve the sound source distribution by Green’s function. Secondly, a sound field restoration by Wiener filtering and reconstruction of current density by time-rotating method have deduced the Laplace’s equation that caters to the current density to further acquire the electrical conductivity distribution image of the sample through iteration method. In the end, double-loop coils experiments have been conducted to verify its feasibility.

Conductivity Inversion of Unidirectional CFRP Laminates Using Eddy Current Testing

Due to the electrical anisotropy of carbon fiber reinforced polymer(CFRP),this paper presents a method to inverse the anisotropic conductivity of unidirectional CFRP laminate using eddy current testing(ECT). The relationship between the conductivity and probe signal of ECT is studied by means of numerical simulation. Finally,the accuracy of inversion result is improved by optimizing the initial conductivity by use of experimental data.

基于2.5D有限元的高密度电法不同装置勘探效果研究

高密度电法是一种适用性广泛的电阻率勘探方法,拥有多种电极排列装置选择。不同装置由于电极分布方式的差异,在针对不同地质目标和测区环境时往往表现出明显不同的探测能力。为探究各排列装置的特点及其适用情况,推导了2.5D电位的变分问题,采用Delaunay三角化算法实现了非结构化网格剖分,进而实现了有限元正演模拟。结合实践,对常见地质情况进行建模,分别使用温纳α、温纳β、施伦贝谢尔、偶极-偶极4种装置开展正反演对比研究。研究发现:(1)在探测未知区域单个异常体时,温纳β、施伦贝谢尔2种排列的效果更好;(2)在探测相邻较近的多个异常体时,应优先采用具有更高水平分辨率的施伦贝谢尔和偶极-偶极排列;(3)对于低阻破碎带的勘探,温纳β和偶极-偶极排列更为适用;(4)当地层具有较好分层界限时,4种排列均可体现良好的分层效果。因此,高密度电法勘探实践中应综合考虑不同排列装置的特点,根据具体情况选用合适的多种排列方式进行测量,并对采集数据进行综合对比分析,以获得更为准确的解释结果。

TriMule机器人并联机构运动学分析与仿真

针对并联机构运动学模型求解困难的问题,以TriMule并联机构为研究对象对其正向运动学与逆向运动学进行了分析与仿真,并通过样机平台的加工工艺参数评价了运动学算法的正确性与稳定性。根据TriMule机器人机构特点,基于矢量法与连杆长度差迭代法建立机器人并联机构的运动学逆解及正解模型,推导机器人驱动支链长度与动定平台位姿关系;在机器人运动空间中选取样本位姿,应用MATLAB对所建立TriMule机器人并联机构运动学模型进行仿真验证,结果表明运动学模型求解算法正确稳定。最后搭建TriMule机器人搅拌摩擦焊实验平台验证运动学算法控制稳定性。

面向学科竞赛的发球机器人设计与控制

以发球机器人的设计和控制为案例,涵盖机械结构设计、电子电路设计、单片机控制、机器视觉、运动学建模和控制调试等多个领域的知识综合。该机器人的机械结构主要包括捡球机构、传球机构、发球机构和底盘机构。底盘机构为机器人的移动提供动力,捡球机构用于捡取放置的待发球,结合视觉定位单元可实现待发球的准确定位和自动抓取,传球机构负责将待发传送到发球机构,由发球机构将待发球发射到目标区域。电气控制系统的主控芯片为STM32单片机,实现对各执行机构的协调控制。经制作样机测试,该发球机器人的发球方向和速度可控。

基于U⁃Rnet的重力全张量梯度数据反演

重力反演是通过地表信息获取地下地质体空间结构与物理性质的重要手段之一。每个重力梯度分量反映不同的地质体信息,联合重力梯度分量进行重力反演能够更好地研究地下密度异常体的形态和分布。为此,提出基于神经网络的重力全张量梯度数据反演算法,将U⁃Rnet网络应用于重力全张量数据的三维反演问题。为了检验该算法的有效性,采用六种典型模型进行模拟实验,获得了具有清晰边界和稀疏的反演结果。首先,对比L2和Tversky两种损失函数的反演结果,后者的反演结果能更清晰地反映模型的边界位置;然后,对不同梯度张量组合进行反演,四组实验结果在三个方向(x、y、z)上具有不同的反演精度,组合四的误差最低;最后,将该方法应用于美国德克萨斯州文顿盐丘的FTG数据,反演结果与实际地质信息基本吻合。

蒙城野外站地电观测场地电性结构探测及解析

利用高密度电阻率层析成像方法,在安徽蒙城地球物理国家野外科学观测研究站地电观测场地及周边进行地下介质电性结构探测,采用温纳装置连续滚动测量方式测得高精度视电阻率数据及测量电极高程信息。通过对探测数据进行正反演计算,获得不同轴向、不同深度的电阻率分布特征及较为精细的研究区电性结构图像,同时结合历史探测资料和钻孔数据进行地质解释,为蒙城地电阻率观测资料的深入研究提供更为科学的背景依据。

一种基于重力正演理论的海底地形反演迭代算法

重力数据反演海底地形的方法是获取全球海底地形数据的重要途径。针对传统地形反演算法需要间接引入经验参数的问题,本研究以不采用经验公式为原则,从空间域方法入手,结合重力正演公式,建立重力异常与地形之间的解析观测方程,采用最小二乘方法进行求解。为了得到观测方程最优解,建立牛顿迭代关系,引入正则化参数,增强方程组的收敛性。顾及边缘效应对正演的影响,采用双线性插值算法对原始重力异常数据进行加密处理和无约束网格扩充以削弱误差。在太平洋海域(155°E~156°E,16°N~17°N)构建了1′×1′分辨率的海底地形模型,并通过船测水深数据验证。相比于传统空间域算法——重力地质法,本研究方法的均方根误差降低了12.7%,验证了其可行性与准确性。

聚酰胺/醋酸纤维素复合正渗透膜的制备及相转化工艺参数的优化

正渗透(forward osmosis,FO)分离技术具有低操作压力、低能耗、膜污染程度轻等优点,可能成为解决世界上水资源匮乏和能源短缺等极具挑战性问题的潜在方案。然而,正渗透技术缺乏性能优异且稳定的FO膜被认为是限制其发展及应用的主要障碍之一。本文从调控相转化工艺参数出发,通过改变铸膜液中的致孔剂种类以及操作参数(涂布厚度、溶剂蒸发时间和凝固浴温度)来实现对醋酸纤维素(cellulose acetate,CA)基底结构和性能的有效调控,并采用界面聚合(interfacial polymerization,IP)的方法在这种亲水的多孔基底上制备聚酰胺(polyamide,PA)分离层,得到薄膜复合正渗透(TFC-FO)膜。实验结果表明,用10%(质量分数)CA、致孔剂A等组成的铸膜液在150μm浇铸刀下涂布,立即浸入25℃凝固浴相转化制得CA基底,再经界面聚合所得的TFC-FO膜的渗透选择性最佳,以1mol/L NaCl溶液作为汲取液,去离子水作为原料液,膜的FO水通量达10.94L/(m^(2)·h),反向盐通量为0.0500mol/(m^(2)·h),对NaCl的截留率为95.0%,结构参数为1404μm。

基于高精度重力勘探对地下空洞的探测研究

地下空洞埋藏浅、规模小不易探测,随着重力感应技术的发展,给准确快速获取微重力变化为探测地下空洞带来新机会,对小规模地下空洞的探测能力具有广泛的研究和实用价值。本文从重力基础理论、重力探测技术、重力数据处理及反演3方面对地下空洞进行系统的分析研究。在给定形体大小、重力数据精度的情况下,通过二分法计算出了重力探测的最大埋藏深度;将高密度采集、高精度重力探测方法用于某区客运站地下人行通道实际探测,获得一套高精度重力格网数据,理论研究和实测结果表明,现有重力仪器具有探测地下空洞的能力;采用最小曲率位场分离方法、2.5D人机交互反演和靶区识别三维物性快速反演方法,得到地下人行通道近SN向的分布,埋藏深度大致2.5~5 m,这与实际情况相符。本次研究为地下空洞探测摸索出一套完整的重力勘探流程,具有一定的参考价值。

CSAMT在巴音戈壁盆地铀储层识别中的应用

在电磁法数值模拟中,正演模拟是认识各种地电条件下的大地电磁响应特征的良好途径,模型正演得到的结论,对于资料处理、反演、解释具有积极的指导作用。通过在巴音戈壁盆地开展可控源音频大地电磁测量(CSAMT),结合巴音戈壁盆地已知钻孔资料,建立了初始地电模型,进行模型正演计算得出正演数据,通过对正演数据进行反演计算,确定合理的反演模型和反演参数,提高资料反演的准确性,最后根据对实测数据的反演计算、资料解释、综合分析结果,推断研究区铀储层巴音戈壁组的空间展布特征,解释结果得到钻孔资料的验证,在砂岩型铀矿勘查中取得较好的效果。