An algorithm for fast 3D inversion of surface electrical resistivity tomography data: application on imaging buried antiquities

In this work a new algorithm for the fast and efficient 3D inversion of conventional 2D surface electrical resistivity tomography lines is presented. The proposed approach lies on the assumption that for every surface measurement there is a large number of 3D parameters with very small absolute Jacobian matrix values, which can be excluded in advance from the Jacobian matrix calculation, as they do not contribute significant information in the inversion procedure. A sensitivity analysis for both homogeneous and inhomogeneous earth models showed that each measurement has a specific region of influence, which can be limited to parameters in a critical rectangular prism volume. Application of the proposed algorithm accelerated almost three times the Jacobian (sensitivity) matrix calculation for the data sets tested in this work. Moreover, application of the least squares regression iterative inversion technique, resulted in a new 3D resistivity inversion algorithm more than 2.7 times faster and with computer memory requirements less than half compared to the original algorithm. The efficiency and accuracy of the algorithm was verified using synthetic models representing typical archaeological structures, as well as field data collected from two archaeological sites in Greece, employing different electrode configurations. The applicability of the presented approach is demonstrated for archaeological investigations and the basic idea of the proposed algorithm can be easily extended for the inversion of other geophysical data.     

A 3D gravity data interpretation of the Matagami mining camp, Abitibi Subprovince, Superior Province, Québec, Canada: Application to VMS deposit exploration

Two inversions, unconstrained and constrained, of a gravity survey of the Matagami mining camp (Abitibi Archaean Subprovince, Canada) have been performed in order to identify the downward extension of a rhyolitic horizon hosting VMS-type base metals deposit and the morphologies of the major felsic plutons. A comparative study exhibits the similarities between measured and calculated densities from chemical compositions of the Matagami lithologies. This allows building an initial 3D geodensity model which integrates densities and available structural and geological surface mapping data. This model is integrated during the iteration process of the constrained inversion in the objective function. The resulting true density model and two derived cross-sections upgrade the 3D imaging of this area. Also, the model gives new insight for regional geological interpretation exposing possible shapes of the main geological units at depth and suggests the potential existence of deep fertile geological bodies.     

1D elastic full‐waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm–Gibbs sampler approach

Stochastic optimization methods, such as genetic algorithms, search for the global minimum of the misfit function within a given parameter range and do not require any calculation of the gradients of the misfit surfaces. More importantly, these methods collect a series of models and associated likelihoods that can be used to estimate the posterior probability distribution. However, because genetic algorithms are not a Markov chain Monte Carlo method, the direct use of the genetic‐algorithm‐sampled models and their associated likelihoods produce a biased estimation of the posterior probability distribution. In contrast, Markov chain Monte Carlo methods, such as the Metropolis–Hastings and Gibbs sampler, provide accurate posterior probability distributions but at considerable computational cost. In this paper, we use a hybrid method that combines the speed of a genetic algorithm to find an optimal solution and the accuracy of a Gibbs sampler to obtain a reliable estimation of the posterior probability distributions. First, we test this method on an analytical function and show that the genetic algorithm method cannot recover the true probability distributions and that it tends to underestimate the true uncertainties. Conversely, combining the genetic algorithm optimization with a Gibbs sampler step enables us to recover the true posterior probability distributions. Then, we demonstrate the applicability of this hybrid method by performing one‐dimensional elastic full‐waveform inversions on synthetic and field data. We also discuss how an appropriate genetic algorithm implementation is essential to attenuate the “genetic drift” effect and to maximize the exploration of the model space. In fact, a wide and efficient exploration of the model space is important not only to avoid entrapment in local minima during the genetic algorithm optimization but also to ensure a reliable estimation of the posterior probability distributions in the subsequent Gibbs sampler step.     

Using constrained inversion of gravity and magnetic field to produce a 3D litho‐prediction model

Geologically constrained inversion of gravity and magnetic field data of the Victoria property (located in Sudbury, Canada) was undertaken in order to update the present three‐dimensional geological model. The initial and reference model was constructed based on geological information from over 950 drillholes to constrain the inversion. In addition, downhole density and magnetic susceptibility measured in six holes were statistically analysed to derive lower and upper bounds on the physical properties attributed to the lithological units in the reference model. Constrained inversion of the ground gravity and the airborne magnetic data collected at the Victoria property were performed using GRAV3D and MAG3D, respectively. A neural network was trained to predict lithological units from the physical properties measured in six holes. Then, the trained network was applied on the three‐dimensional distribution of physical properties derived from the inversion models to produce a three‐dimensional litho‐prediction model. Some of the features evident in the lithological model are remnants of the constraints, where the data did not demand a significant change in the model from the initial constraining model (e.g., the thin pair of diabase dykes). However, some important changes away from the initial model are evident; for example, a larger body was predicted for quartz diorite, which may be related to the prospective offset dykes; a new zone was predicted as sulfide, which may represent potential mineralisation; and a geophysical subcategory of metabasalt was identified with high magnetic susceptibility and high density. The litho‐prediction model agrees with the geological expectation for the three‐dimensional structure at Victoria and is consistent with the geophysical data, which results in a more holistic understanding of the subsurface lithology.     

2017年6月16日湖北秭归Ms4.3地震成因初探

北京时间2017年6月16日19时48分, 湖北省秭归县发生M_S4.3地震(震中位置31.06°N, 110.48°E)。 此次地震导致当地160多间建筑物出现不同程度损坏, 100多人生活受到影响, 震区中危岩体出现一定程度破坏。 分析此次地震的震源参数将有助于了解其震源特征和发震机制。 本文首先获取了震中距在3°以内的40个宽频带固定台站的三分量数据, 然后利用FK(Frequency wavenumber)方法计算了在不同深度下Crust2.0模型和改进的1-D模型的格林函数, 分别使用Cut And Paste(CAP)方法反演得到本次地震的震源机制解和深度; 并采用基阶Rayleigh波振幅谱进一步约束了质心深度。 结果表明此次地震的最佳震源机制解的节面Ⅰ: 68°/59°/163°; 节面Ⅱ: 166°/75°/32°, 最佳矩震级为M_W4.3, 最佳深度约为5 km。 对比2013年巴东地震、 2014年秭归地震以及2011年湖北阳新—江西瑞昌地震的震源参数和余震信息, 此次地震有可能是水库诱发地震, 但要判断成因还需进一步研究。     

CO2 saturation estimates at Sleipner (North Sea) from seismic tomography and rock physics inversion

CO₂ saturations are estimated at Sleipner using a two-step imaging workflow. The workflow combines seismic tomography (full-waveform inversion) and rock physics inversion and is applied to a two-dimensional seismic line located near the injection point at Sleipner. We use baseline data (1994 vintage, before CO₂ injection) and monitor data that was acquired after 12 years of CO₂ injection (2008 vintage). P-wave velocity models are generated using the Full waveform inversion technology and then, we invert selected rock physics parameters using an rock physics inversion methodology. Full waveform inversion provides high-resolution P-wave velocity models both for baseline and monitor data. The physical relations between rock physics properties and acoustic wave velocities in the Utsira unconsolidated sandstone (reservoir formation) are defined using a dynamic rock physics model based on well-known Biot–Gassmann theories. For data prior to injection, rock frame properties (porosity, bulk and shear dry moduli) are estimated using rock physics inversion that allows deriving physically consistent properties with related uncertainty. We show that the uncertainty related to limited input data (only P-wave velocity) is not an issue because the mean values of parameters are correct. These rock frame properties are then used as a priori constraint in the monitor case. For monitor data, the Full waveform inversion results show nicely resolved thin layers of CO₂–brine saturated sandstones under intra-reservoir shale layers. The CO₂ saturation estimation is carried out by plugging an effective fluid phase in the rock physics model. Calculating the effective fluid bulk modulus of the brine–CO₂ mixture (using Brie equation in our study) is shown to be the key factor to link P-wave velocity to CO₂ saturation. The inversion tests are done with several values of Brie/patchiness exponent and show that the CO₂ saturation estimates are varying between 0.30 and 0.90 depending on the rock physics model and the location in the reservoir. The uncertainty in CO₂ saturation estimation is usually lower than 0.20. When the patchiness exponent is considered as unknown, the inversion is less constrained and we end up with values of exponent varying between 5 and 20 and up to 33 in specific reservoir areas. These estimations tend to show that the CO₂–brine mixing is between uniform and patchy mixing and variable throughout the reservoir.     

Seismic amplitude inversion for the transversely isotropic media with vertical axis of symmetry

Transverse isotropy with a vertical axis of symmetry is a common form of anisotropy in sedimentary basins, and it has a significant influence on the seismic amplitude variation with offset. Although exact solutions and approximations of the PP-wave reflection coefficient for the transversely isotropic media with vertical axis of symmetry have been explicitly studied, it is difficult to apply these equations to amplitude inversion, because more than three parameters need to be estimated, and such an inverse problem is highly ill-posed. In this paper, we propose a seismic amplitude inversion method for the transversely isotropic media with a vertical axis of symmetry based on a modified approximation of the reflection coefficient. This new approximation consists of only three model parameters: attribute A, the impedance (vertical phase velocity multiplied by bulk density); attribute B, shear modulus proportional to an anellipticity parameter (Thomsen's parameter ε−δ); and attribute C, the approximate horizontal P-wave phase velocity, which can be well estimated by using a Bayesian-framework-based inversion method. Using numerical tests we show that the derived approximation has similar accuracy to the existing linear approximation and much higher accuracy than isotropic approximations, especially at large angles of incidence and for strong anisotropy. The new inversion method is validated by using both synthetic data and field seismic data. We show that the inverted attributes are robust for shale-gas reservoir characterization: the shale formation can be discriminated from surrounding formations by using the crossplot of the attributes A and C, and then the gas-bearing shale can be identified through the combination of the attributes A and B. We then propose a rock-physics-based method and a stepwise-inversion-based method to estimate the P-wave anisotropy parameter (Thomsen's parameter ε). The latter is more suitable when subsurface media are strongly heterogeneous. The stepwise inversion produces a stable and accurate Thomsen's parameter ε, which is proved by using both synthetic and field data.     

基于深度学习的2D-3D地下密度异常体反演方法

地下密度异常体反演需要由二维数据反演三维结果,为了高效、高精度地反演地下异常体的位置及密度信息,本文提出2D-3D InvNet深度学习反演方法.方法的编-解码器结构,在编码阶段利用二维卷积网络结构提取地表重力异常及重力梯度异常数据信息,解码阶段利用三维卷积网络结构恢复异常体地下形态,实现了二维和三维网络结构的结合.为了精确地反演异常体密度,提出利用加权均方误差（WtdMSE）作为损失函数,同时为了更好地评估反演结果,引入核密度函数作为评价手段.与均方误差（MSE）相比,利用WtdMSE作为损失函数对异常体密度的反演结果更为准确,异常体所在区域的密度误差减少50%以上.对理论样本的反演结果表明,2D-3D InvNet在准确反演异常体地下位置的同时,也能给出准确的密度信息.应用此方法对西澳大利亚Kauring地区实测数据进行反演,我们成功获得了此区域地下异常体的密度分布.理论与实际应用结果表明,2D-3D InvNet深度学习方法稳定且具有较强的泛化性能,能在无需规定限制条件的情况下快速获得准确的反演结果.

     

联合多点地质统计学与序贯高斯模拟的随机反演方法

岩相和储层物性参数是油藏表征的重要参数,地震反演是储层表征和油气藏勘探开发的重要手段.随机地震反演通常基于地质统计学理论,能够对不同类型的信息源进行综合,建立具有较高分辨率的储层模型,因而得到广泛关注.其中,概率扰动方法是一种高效的迭代随机反演策略,它能综合考虑多种约束信息,且只需要较少的迭代次数即可获得反演结果.在概率扰动的优化反演策略中,本文有效的联合多点地质统计学与序贯高斯模拟,并结合统计岩石物理理论实现随机反演.首先,通过多点地质统计学随机模拟,获得一系列等可能的岩相模型,扰动更新初始岩相模型后利用相控序贯高斯模拟建立多个储层物性参数模型;然后通过统计岩石物理理论,计算相应的弹性参数;最后,正演得到合成地震记录并与实际地震数据对比,通过概率扰动方法进行迭代,直到获得满足给定误差要求的反演结果.利用多点地质统计学,能够更好地表征储层空间特征.相控序贯高斯模拟的应用,能够有效反映不同岩相中储层物性参数的分布.提出的方法可在较少的迭代次数内同时获得具有较高分辨率的岩相和物性参数反演结果,模型测试和实际数据应用验证了方法的可行性和有效性.

     

基于广义模型约束的时间域航空电磁反演研究

由于航空电磁具有海量数据,因此快速有效的成像和反演手段至关重要.本文针对层状介质模型推导与实现了广义模型约束条件下时间域航空电磁一维反演.从正则化反演的目标函数出发,通过改变模型约束项构造Lp范数反演和聚焦反演,进而通过改变模型求解域构造出基于小波变换的稀疏约束反演.针对不同反演方法目标函数的构建方式,本文进一步从数学原理上分析不同反演方法的预期效果,并通过理论模型和实测数据进行验证.结果表明L0.8范数反演、聚焦反演和基于小波变换的稀疏约束反演可以得到更符合地下层状介质陡变界面的反演结果.