基于交叉梯度交替结构约束的二维地震走时与全通道直流电阻率联合反演

在利用不同的地球物理勘探方法对地下复杂介质成像时,因观测系统的非完备性及数据本身对某些岩石物性的不敏感性,单独成像的结果存在较大的不确定性和不一致性.对于地震体波走时成像与直流电阻率成像,均面临着成像阴影区问题.对于地震走时成像,地震射线对低速区域覆盖较差形成阴影区,造成低速区域分辨率降低.对于电阻率成像,电场线在高阻区域分布较少,造成高阻区域分辨率较低.为了提高地下介质成像的精度,Gallado和Meju(2003)提出了基于交叉梯度结构约束的联合地球物理成像方法.在要求不同的物性模型拟合各自对应的数据同时,模型之间的结构要求一致,即交叉梯度趋于零.为了更有效地实现基于交叉梯度的结构约束,我们提出了一种新的交替结构约束的联合反演流程,即交替反演不同的数据而且在反演一种数据时要求对应的模型与另一个模型结构一致.新的算法能够更容易地把单独的反演系统耦合在一起,而且也更容易建立结构约束和数据拟合之间的平衡.基于新的联合反演流程,我们测试了基于交叉梯度结构约束的二维跨孔地震走时和直流电阻率联合成像.合成数据测试表明,我们提出的交替结构约束流程能够很好地实现基于交叉梯度结构约束的联合成像.与单独成像结果相比,地震走时和全通道电阻率联合成像更可靠地确定了速度和电阻率异常.     

Discrete cosine transform for parameter space reduction in Bayesian electrical resistivity tomography

Electrical resistivity tomography is a non-linear and ill-posed geophysical inverse problem that is usually solved through gradient-descent methods. This strategy is computationally fast and easy to implement but impedes accurate uncertainty appraisals. We present a probabilistic approach to two-dimensional electrical resistivity tomography in which a Markov chain Monte Carlo algorithm is used to numerically evaluate the posterior probability density function that fully quantifies the uncertainty affecting the recovered solution. The main drawback of Markov chain Monte Carlo approaches is related to the considerable number of sampled models needed to achieve accurate posterior assessments in high-dimensional parameter spaces. Therefore, to reduce the computational burden of the inversion process, we employ the differential evolution Markov chain, a hybrid method between non-linear optimization and Markov chain Monte Carlo sampling, which exploits multiple and interactive chains to speed up the probabilistic sampling. Moreover, the discrete cosine transform reparameterization is employed to reduce the dimensionality of the parameter space removing the high-frequency components of the resistivity model which are not sensitive to data. In this framework, the unknown parameters become the series of coefficients associated with the retained discrete cosine transform basis functions. First, synthetic data inversions are used to validate the proposed method and to demonstrate the benefits provided by the discrete cosine transform compression. To this end, we compare the outcomes of the implemented approach with those provided by a differential evolution Markov chain algorithm running in the full, un-reduced model space. Then, we apply the method to invert field data acquired along a river embankment. The results yielded by the implemented approach are also benchmarked against a standard local inversion algorithm. The proposed Bayesian inversion provides posterior mean models in agreement with the predictions achieved by the gradient-based inversion, but it also provides model uncertainties, which can be used for penetration depth and resolution limit identification.     

混合范数下的最优化反演方法

在求解地球物理反问题时,通常根据最小二乘准则构造目标函数进行反演,并在实践中得到了广泛的应用.为进一步增强反演的稳健性及减少多解性,不损失反演结果的分辨率,本文提出了混合范数下的最优化反演方法,它根据数据和模型可能服从不同的概率分布,对数据空间和模型空间采用不同的范数来构造目标函数.在给出目标函数的基础上,导出了混合范数下的线性反演方程.由于该线性反演方程的复杂性,我们采用混合范数下迭代再加权共轭梯度法进行求解.最后,通过对模拟的电阻率数据进行反演,验证了本文计算方法是可行的.     

Pareto Joint Inversion of 2D Magnetotelluric and Gravity Data — Towards Practical Applications

In this paper, a Pareto inversion based global optimization approach, to obtain results of joint inversion of two types of geophysical data sets, is formulated. 2D magnetotelluric and gravity data were used for tests, but presented solution is flexible enough to be used for combination of any kind of two or more target functions, as long as misfits can be calculated and forward problems solved. To minimize dimensionality of the solution, space and introduce straightforward regularization Sharp Boundary Interface (SBI) method was applied. As a main optimization engine, Particle Swarm Optimization (PSO) was used. Synthetic examples based on a real geological model were used to test proposed approach and show its usefulness in practical applications.     

Imaging a 3D geological structure from HEM, airborne magnetic and ground ERT data in Kalat-e-Reshm area, Iran

A set of geophysical data collected in an area in Iran are analyzed to check the validity of a geological map that was prepared in connection to a mineral prospecting project and also to image the spatial electrical resistivity distribution. The data set includes helicopter electromagnetic (HEM), airborne magnetic and ground electrical resistivity measurement. Occam approach was used to invert the HEM data to model the resistivity using a layered earth model with fixed thicknesses. The algorithm is based on a nonlinear inverse problem in a least-squares sense.The algorithm was tested on a part of an HEM dataset acquired with a DIGHEM helicopter EM system at Kalat-e-Reshm, Semnan in Iran. The area contains a resistive porphyry andesite that is covered by Eocene sedimentary units. The results are shown as resistivity sections and maps confirming the existence of an arc like resistive structure in the survey area. The resistive andesite seems to be thicker than it is indicated in the geological maps. The results are compared with the reduced to the pole (RTP) airborne magnetic anomaly field data as well as with two ground resistivity profiles. We found reasonable correlations between the HEM 1D resistivity models and 2D models from electrical resistivity tomography (ERT) inversions. A 3D visualization of the 1D models along all flight lines provided a useful tool for the study of spatial variations of the resistivity structure in the investigation area.     

基于非结构双网格的2D RMT双参数同步反演研究

Radio-magnetotelluric(RMT)是以无线电发射机为信号源的一种地球物理勘探方法,近年来被广泛应用于数米至数十米内的近地表工程和环境地球物理勘探.目前,各类电磁资料的反演均是以寻求满足目标拟合差的地下介质电阻率分布为目的.然而,对于勘探频率为10~300kHz的RMT数据,由介电常数所引起的波动场在总场中的比例可达20%以上,在这种情况下,忽略介电常数,仅通过电阻率参数的反演来进行数据拟合势必降低反演资料解释的准确性.为解决这一问题,本文研究了基于电阻率-介电常数的双参数同步反演算法.构建了一个全新的双参数目标函数,并推导了双参数反演迭代方程组;通过灵敏度分析,研究了电阻率和介电常数对正演响应的影响,并据此提出相对电导率的概念,统一了反演参数的灵敏度;通过理论模型分析了参考频率、双参数正则化因子对反演结果的影响,并给出了一般性的参数优选方案.此外,为了能够灵活处理复杂地形,本文采用非结构的正反演双网格进行模型离散,并通过局部加密技术保证反演的速度和精度.最后,对一带地形的理论模型分别进行了单参数和双参数反演,结果表明单参数反演无法正确反映出地电信息,而双参数反演能够准确得到异常的分布,验证了本文所开发的双参数反演程序的有效性.     

基于交叉梯度约束的重力、磁法和大地电磁三维联合反演

为了降低单一地球物理方法反演的多解性及受噪声的影响程度,本文围绕重力、磁法和大地电磁法开展了三维联合反演的研究.重、磁采用基于对数障碍法的正则化反演算法,大地电磁使用limited-memory BroydenFletcher-Goldfarb-Shanno(L-BFGS)反演算法,引入交叉梯度函数实现了三种物性结构的相互耦合,最终开发出一套重磁电三维联合反演算法,并实现MPI并行加速计算.通过理论模型算例验证了算法的准确性,结果表明:不论是单棱柱体模型还是组合棱柱体模型,联合反演结果相较单独反演对于异常体的空间形态刻画以及物性数值恢复具有较好的提升;单棱柱体模型算例使得异常体的物性参数(密度、磁化率和电阻率)更加接近于真实的物性参数;组合棱柱体模型的联合反演结果不仅仅消除了围岩物性参数的假异常,而且还增强了异常体边界结构的恢复程度.     

Out‐of‐plane effects in 2D borehole‐to‐surface resistivity tomography and applications in mineral exploration

In this paper, we discuss the effects of anomalous out‐of‐plane bodies in two‐dimensional (2D) borehole‐to‐surface electrical resistivity tomography with numerical resistivity modelling and synthetic inversion tests. The results of the two groups of synthetic resistivity model tests illustrate that anomalous bodies out of the plane of interest have an effect on two‐dimensional inversion and that the degree of influence of out‐of‐plane body on inverted images varies. The different influences are derived from two cases. One case is different resistivity models with the same electrode array, and the other case is the same resistivity model with different electrode arrays. Qualitative interpretation based on the inversion tests shows that we cannot find a reasonable electrode array to determine the best inverse solution and reveal the subsurface resistivity distribution for all types of geoelectrical models. Because of the three‐dimensional effect arising from neighbouring anomalous bodies, the qualitative interpretation of inverted images from the two‐dimensional inversion of electrical resistivity tomography data without prior information can be misleading. Two‐dimensional inversion with drilling data can decrease the three‐dimensional effect. We employed two‐ and three‐dimensional borehole‐to‐surface electrical resistivity tomography methods with a pole–pole array and a bipole–bipole array for mineral exploration at Abag Banner and Hexigten Banner in Inner Mongolia, China. Different inverse schemes were carried out for different cases. The subsurface resistivity distribution obtained from the two‐dimensional inversion of the field electrical resistivity tomography data with sufficient prior information, such as drilling data and other non‐electrical data, can better describe the actual geological situation. When there is not enough prior information to carry out constrained two‐dimensional inversion, the three‐dimensional electrical resistivity tomography survey is the better choice.     

Exploring seismic inversion methodologies for non‐stationary geological environments: a benchmark study between deterministic and geostatistical seismic inversion

This paper presents a comparison between subsurface impedance models derived from different deterministic and geostatistical seismic inversion methodologies applied to a challenging synthetic dataset. Geostatistical seismic inversion methodologies nowadays are common place in both industry and academia, contrasting with traditional deterministic seismic inversion methodologies that are becoming less used as part of the geo‐modelling workflow. While the first set of techniques allows the simultaneous inference of the best‐fit inverse model along with the spatial uncertainty of the subsurface elastic property of interest, the second family of inverse methodology has proven results in correctly predicting the subsurface elastic properties of interest with comparatively less computational cost. We present herein the results of a benchmark study performed over a realistic three‐dimensional non‐stationary synthetic dataset in order to assess the performance and convergence of different deterministic and geostatistical seismic inverse methodologies. We also compare and discuss the impact of the inversion parameterisation over the exploration of the model parameter space. The results show that the chosen seismic inversion methodology should always be dependent on the type and quantity of the available data, both seismic and well‐log, and the complexity of the geological environment versus the assumptions behind each inversion technique. The assessment of the model parameter space shows that the initial guess of traditional deterministic seismic inversion methodologies is of high importance since it will determine the location of the best‐fit inverse solution.     

一种高效的基于交叉梯度结构约束的三维地震走时与直流电阻率联合反演策略

基于交叉梯度结构约束的不同数据的联合反演可以提高地球物理成像的可靠度,但是由于不同观测数据对地下介质的灵敏度不同以及不同反演系统灵敏度矩阵元素的数值可能存在较大的差异,导致构建和求解联合反演系统存在很大的挑战.针对以上问题,本文提出一种新的基于单独反演模型更新量与交叉梯度结构约束相结合的联合反演策略.该策略利用单独反演系统分别确定出各个模型的更新量,然后利用它们约束交叉梯度系统的反演,得出新的模型更新量.通过这样的方式,有效实现了数据拟合与结构约束的平衡,实现了基于交叉梯度联合反演的目标.新的联合反演策略不需要对原来单独反演程序修改即可实现联合反演,减小了联合反演实现的难度,极大地提高了联合反演的易实现性,而且避免了联合反演矩阵存储及结构过于复杂难以求解的问题.基于新的策略,本文首次实现了基于交叉梯度结构约束的三维地震走时与直流电阻率联合反演.合成模型测试表明,与单一成像相比,联合成像减少了地震走时反演中出现的干扰异常并提高了电阻率反演的分辨率.     

A Bayesian trans-dimensional approach for the fusion of multiple geophysical datasets

We propose a Bayesian fusion approach to integrate multiple geophysical datasets with different coverage and sensitivity. The fusion strategy is based on the capability of various geophysical methods to provide enough resolution to identify either subsurface material parameters or subsurface structure, or both. We focus on electrical resistivity as the target material parameter and electrical resistivity tomography (ERT), electromagnetic induction (EMI), and ground penetrating radar (GPR) as the set of geophysical methods. However, extending the approach to different sets of geophysical parameters and methods is straightforward. Different geophysical datasets are entered into a trans-dimensional Markov chain Monte Carlo (McMC) search-based joint inversion algorithm. The trans-dimensional property of the McMC algorithm allows dynamic parameterisation of the model space, which in turn helps to avoid bias of the post-inversion results towards a particular model. Given that we are attempting to develop an approach that has practical potential, we discretize the subsurface into an array of one-dimensional earth-models. Accordingly, the ERT data that are collected by using two-dimensional acquisition geometry are re-casted to a set of equivalent vertical electric soundings. Different data are inverted either individually or jointly to estimate one-dimensional subsurface models at discrete locations. We use Shannon's information measure to quantify the information obtained from the inversion of different combinations of geophysical datasets. Information from multiple methods is brought together via introducing joint likelihood function and/or constraining the prior information. A Bayesian maximum entropy approach is used for spatial fusion of spatially dispersed estimated one-dimensional models and mapping of the target parameter. We illustrate the approach with a synthetic dataset and then apply it to a field dataset. We show that the proposed fusion strategy is successful not only in enhancing the subsurface information but also as a survey design tool to identify the appropriate combination of the geophysical tools and show whether application of an individual method for further investigation of a specific site is beneficial.     

基于非结构网格的电阻率三维带地形反演

地表起伏地形在野外矿产资源勘察中不可避免,其对直流电阻率法勘探影响巨大.近年来,电阻率三维正演取得诸多进展,特别是应用非结构网格我们能够进行任意复杂地形和几何模型的电阻率三维数值模拟,但面向实际应用的起伏地形下电阻率三维反演依然困难.本文基于非结构化四面体网格,并考虑到应用GPS/GNSS时,区域地球物理调查中可非规则布设测网的实际特点,实现了任意地形(平坦或起伏)条件下、任意布设的偶极-偶极视电阻率数据的不完全Gauss-Newton三维反演.合成数据的反演结果表明了方法的有效性,可应用于复杂野外环境下的三维电法勘探.     

电阻率层析成像非线性反演

本文通过概率成像法、α中心法、遗传算法的有机结合, 实现了2-D电阻率层析成像非线性反演.首先,根据概率成像结果指示出的2-D地质异常体的相对电性和大致分布范围,合理地确定出α中心的位置与强度系数的变化范围.其次,以α中心法作为2-D地电结构的正演手段,采用遗传算法反演出各α中心的强度系数.在此基础上, 应用α中心法重新构建出地下介质电阻率的分布.通过对均匀半空间中赋存2-D地质异常体模型的反演试验, 获得了令人满意的结果.     

部分区域约束下的交叉梯度多重地球物理数据联合反演

交叉梯度联合反演方法通过对多种地球物理模型实现结构耦合,在岩石物性关系不确定的情况下,既能提高反演结果的可靠性,又能减少反演的多解性,还能减少不同方法解释结果之间的矛盾.当不同的模型观测数据覆盖范围不一致时,交叉梯度联合反演通常需要取出重叠区域数据进行联合反演,并且建模时还要扩展一些模型范围.本文首先提出并实现了部分区域约束下的交叉梯度多重地球物理数据联合反演算法;接着进行了算法的模型试算;最后,我们将该反演算法用于本溪—集安深部地质调查重磁电综合地质地球物理解释中.结果表明:该算法不但能在重叠区域内很好地恢复结构相似的模型,而且在非重叠区域与重叠区域的边界处仍然可以得到平滑变化的模型;在本溪—集安10号剖面所获得的结构上相似的电阻率、密度及磁化率模型较好地反映了该区的深部地质结构,对于确定深部地质体的性质提供了有力的证据.     

电阻率二维神经网络反演

由于非线性特性地球物理反演一直以来都是一个比较困难的问题. 近十年来,非线性反演方法如人工神经网络、遗传算法在地球物理数据解释中得到越来越多的应用,但目前基本仍限于一维反演问题. 对于二维反问题,反演参数较多,神经网络反演运用较少. 本文利用BP神经网络优化方法,实现了电阻率二维非线性反演. 与传统线性化的迭代反演比较,神经网络反演能够克服传统方法的不足、获得更好的反演结果.     

Minimizing Interpretation Ambiguities Through Joint Inversion of Surface Electrical Data

Joint inversion is an extension of standard least-squares inversion techniques. With this approach, it is possible to construct a model that is simultaneously consistent with surface observations made using more than one geophysical method. Specifically, discussion focuses on applications to the Schlumberger and audiomagnetotelluric sounding methods, although the technique is not limited to these.
The authors have found that the non-uniqueness problems that often plague the interpretation of electrical sounding data can be substantially reduced if the model is required to be consistent with more than one set of surface measurements. If geological ground truth is available to further constrain the model, the chances of arriving at the true physical property distribution of the subsurface are far better than if a single data set is interpreted.
To demonstrate the power of joint inversion, the authors present a study using synthetic data obtained from a three-layer resistivity model. Finally, two case histories are presented to demonstrate that a much better representation of the true earth can be obtained with joint inversion than with standard interpretation methods.     

大地电磁的人工鱼群最优化约束反演

大地电磁的反演问题是非线性,如果采用线性反演方法容易陷入局部极小,使得反演结果非唯一性严重.本文将人工鱼群算法引入到地球物理反演之中,提出了非线性的大地电磁人工鱼群最优化反演.该方法不需要进行偏导数的求取,可以对反演的范围进行约束,以减小反演结果的非唯一性.同时我们对搜索步长进行了改进,给出适用于大地电磁反演的人工鱼群参数.大量的理论数据试算表明,人工鱼群反演算法能够较好地寻找到全局最优解.实测数据的处理结果表明,该方法可以用来处理实际资料,并且能够取得很好的应用效果.     

Combining discrete cosine transform and convolutional neural networks to speed up the Hamiltonian Monte Carlo inversion of pre-stack seismic data

Markov chain Monte Carlo algorithms are commonly employed for accurate uncertainty appraisals in non-linear inverse problems. The downside of these algorithms is the considerable number of samples needed to achieve reliable posterior estimations, especially in high-dimensional model spaces. To overcome this issue, the Hamiltonian Monte Carlo algorithm has recently been introduced to solve geophysical inversions. Different from classical Markov chain Monte Carlo algorithms, this approach exploits the derivative information of the target posterior probability density to guide the sampling of the model space. However, its main downside is the computational cost for the derivative computation (i.e. the computation of the Jacobian matrix around each sampled model). Possible strategies to mitigate this issue are the reduction of the dimensionality of the model space and/or the use of efficient methods to compute the gradient of the target density. Here we focus the attention to the estimation of elastic properties (P-, S-wave velocities and density) from pre-stack data through a non-linear amplitude versus angle inversion in which the Hamiltonian Monte Carlo algorithm is used to sample the posterior probability. To decrease the computational cost of the inversion procedure, we employ the discrete cosine transform to reparametrize the model space, and we train a convolutional neural network to predict the Jacobian matrix around each sampled model. The training data set for the network is also parametrized in the discrete cosine transform space, thus allowing for a reduction of the number of parameters to be optimized during the learning phase. Once trained the network can be used to compute the Jacobian matrix associated with each sampled model in real time. The outcomes of the proposed approach are compared and validated with the predictions of Hamiltonian Monte Carlo inversions in which a quite computationally expensive, but accurate finite-difference scheme is used to compute the Jacobian matrix and with those obtained by replacing the Jacobian with a matrix operator derived from a linear approximation of the Zoeppritz equations. Synthetic and field inversion experiments demonstrate that the proposed approach dramatically reduces the cost of the Hamiltonian Monte Carlo inversion while preserving an accurate and efficient sampling of the posterior probability.     

井间电阻率层析成像研究新进展

由于井间电阻率层析成像方法具有施工方便、成本低、对钻孔无破坏作用且穿透深度也较大等优点，因而得到越来越广泛的应用，在过去几年内对该方法研究的重点在于提高计算效率和改善成像质量，本文从井间电阻率层析成像的基本原理出发，对该方法近年的新进展进行了比较和综述，还介绍了概率层析成像算法的原理，认为今后几年内在此领域内的理论研究重点将是探索有效的反演方法和提高成像质量。     

Gravity-geoelectric joint inversion over the Potiguar rift basin, NE Brazil

A joint inversion of gravity and geoelectrical data has been performed along a 100 km long profile across the oil-bearing Potiguar rift basin in NE Brazil. The integrated approach is based on the connection between density and resistivity of a sedimentary sequence through the porosity. Seventy-one gravity stations and twenty-nine vertical electrical soundings were carried out across the central part of the Early Cretaceous basin in the Precambrian Borborema Province to apply the proposed joint inversion. Both the physical properties and geometry of the multilayer deep model were well constrained by a wide set of prior information obtained by surface mapping, geophysical logs and seismic sections. The results reveal a rift architecture formed by a 5000 m thick half-graben structure separated by an intervening basement high and an extensive adjacent platform with a sedimentary infill of about 300 m thick. The calculated model shows geometries for the sedimentary density/resistivity interfaces in agreement with the seismostratigraphic sequences revealed by seismic section, representing a substantial improvement in comparison with previously published gravity models.