Characterization of a 4.1 Mb inversion harboring the stripe rust resistance gene YR86 on wheat chromosome 2AL

Wheat cultivar Zhongmai 895 was earlier found to carry YR86 in an 11.6 Mb recombination-suppressed region on chromosome 2AL when crossed with Yangmai 16.To fine-map the YR86 locus,we developed two large F2 populations from crosses Emai 580/Zhongmai 895 and Avocet S/Zhongmai 895.Remarkably,both populations exhibited suppressed recombination in the same 2AL region.Collinearity analysis across Chinese Spring,Aikang 58,and 10+wheat genomes revealed a 4.1 Mb chromosomal inversion spanning 708.5-712.6 Mb in the Chinese Spring reference genome.Molecular markers were developed in the breakpoint and were used to assess a wheat cultivar panel,revealing that Chinese Spring,Zhongmai 895,and Jimai 22 shared a common sequence named InvCS,whereas Aikang 58,Yangmai 16,Emai 580,and Avocet S shared the sequence named InvAK58.The inverted configuration explained the suppressed recombination observed in all three bi-parental populations.Normal recombination was observed in a Jimai 22/Zhongmai 895 F2 population,facilitating mapping of YR86 to a genetic interval of 0.15 cM corresponding to 710.27-712.56 Mb falling within the inverted region.Thirty-three high-confidence genes were annotated in the interval using the Chinese Spring reference genome,with six identified as potential candidates for YR86 based on genome and transcriptome analyses.These results will accelerate map-based cloning of YR86 and its deployment in wheat breeding.

3D electrical resistivity inversion using prior spatial shape constraints

To minimize the number of solutions in 3D resistivity inversion, an inherent problem in inversion, the amount of data considered have to be large and prior constraints need to be applied. Geological and geophysical data regarding the extent of a geological anomaly are important prior information. We propose the use of shape constraints in 3D electrical resistivity inversion, Three weighted orthogonal vectors （a normal and two tangent vectors） were used to control the resistivity differences at the boundaries of the anomaly. The spatial shape of the anomaly and the constraints on the boundaries of the anomaly are thus established. We incorporated the spatial shape constraints in the objective function of the 3D resistivity inversion and constructed the 3D resistivity inversion equation with spatial shape constraints. Subsequently, we used numerical modeling based on prior spatial shape data to constrain the direction vectors and weights of the 3D resistivity inversion. We established a reasonable range between the direction vectors and weights, and verified the feasibility and effectiveness of using spatial shape prior constraints in reducing excessive structures and the number of solutions. We applied the prior spatially shape-constrained inversion method to locate the aquifer at the Guangzhou subway. The spatial shape constraints were taken from ground penetrating radar data. The inversion results for the location and shape of the aquifer agree well with drilling data, and the number of inversion solutions is significantly reduced.

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.

Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks

Conventional artificial neural networks used to solve electrical resistivity imaging （ERI） inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network （PBNN） nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter αk, which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.

Three-dimensional gravity inversion based on sparse recovery iteration using approximate zero norm

This research proposes a novel three-dimensional gravity inversion based on sparse recovery in compress sensing. Zero norm is selected as the objective function, which is then iteratively solved by the approximate zero norm solution. The inversion approach mainly employs forward modeling; a depth weight function is introduced into the objective function of the zero norms. Sparse inversion results are obtained by the corresponding optimal mathematical method. To achieve the practical geophysical and geological significance of the results, penalty function is applied to constrain the density values. Results obtained by proposed provide clear boundary depth and density contrast distribution information. The method's accuracy, validity, and reliability are verified by comparing its results with those of synthetic models. To further explain its reliability, a practical gravity data is obtained for a region in Texas, USA is applied. Inversion results for this region are compared with those of previous studies, including a research of logging data in the same area. The depth of salt dome obtained by the inversion method is 4.2 km, which is in good agreement with the 4.4 km value from the logging data. From this, the practicality of the inversion method is also validated.

The sensitivity of the array resistivity log to mud filtrate invasion and its primary five-parameter inversion for improved oil water recognition

In order to improve reservoir fluid recognition, the sensitivity of array resistivity response to the difference of the invasion properties in both oil-bearing layers and water layers is analyzed. Then the primary inversion is carried out based on the array resistivity log. The mud invasion process is numerically simulated based on the oil-water flow equation and water convection diffusion equation. The results show that the radial resistivity of a fresh mud-invaded oil-bearing layer presents complex distribution characteristics, such as nonlinear increase, increasing to decreasing and low resistivity annulus, and the resistive invasion profile of a water layer is monotonic. Under specific conditions, array resistivity log can reflect these changes and the array induction log is more sensitive. Nevertheless, due to the effect of factors like large invasion depth, reservoir physical and oil-bearing properties, the measured apparent resistivity may differ greatly from the actual mud filtrate invasion profile in an oil-bearing layer. We proposed a five-parameter formation model to simulate the complex resistivity distribution of fresh mud-invaded formation. Then, based on the principle of non-linear least squares, the measured array resistivity log is used for inversion with the Marquardt method. It is demonstrated that the inverted resistivity is typically non-monotonic in oil-bearing layers and is monotonic in water layers. Processing of some field data shows that this is helpful in achieving efficient reservoir fluid recognition.

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.

Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements

We present systematic investigations on the physics,detection performance and inversion of logging-while-drilling extradeep azimuthal resistivity measurements(EDARM).First,the definitions of EDRAM measurements are discussed,followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals.Then,a new definition of detection depth,which considers the uncertainty of inversion results caused by the data noise,is proposed to quantify the detection capability of ED ARM.Finally,the B ayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data.Numerical results show that ED ARM is capable of detecting the azimuth and distance of remote bed boundaries,and the detection capability increases with increasing spacing and resistivity contrast.The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles.In addition,multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.

Fast anisotropic resistivities inversion of loggingwhile-drilling resistivity measurements in highangle and horizontal wells

Information about anisotropic resistivity is essential in real-time correlation,updating of formation model and making more confi dent geosteering decisions in logging-while-drilling(LWD)application.However,abnormal responses such as curve separations and apparent resistivity“horns”often exist in the LWD resistivity measurements due to the infl uences of complex downhole environments.Thus,accurate formation resistivity is not readily available.In this paper,we present an effi cient inversion scheme for the rapid estimation of anisotropic resistivity from LWD resistivity measurements acquired in high-angle and horizontal wells.Several strategies are adopted in the inversion:(1)a one-dimensional(1D)simulator with a simplifi ed three-layered model guarantees the forward speed and keeps the number of inverted parameters as few as possible;(2)combined with geological and petrophysical bounds,the tool constraints derived from a detection capability analysis of LWD resistivity measurements are applied to scale down the inverted parameters’searching scope,which avoids meaningless solutions and accelerates the inversion signifi cantly;(3)multiple-initial guesses are used in the inversion to ensure a global solution.Inversion results over synthetic examples demonstrate that the proposed 1D inversion algorithm is well suited for complex formation structures.It is also robust and fast in extracting anisotropic resistivities from LWD resistivity measurements.

Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm

Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was presented,which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion.In the proposed method,Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum.An implementation of proposed DE-BPNN was given,the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets.Two abnormity models were used to verify the feasibility and effectiveness of the proposed method,the results show that the proposed DE-BP algorithm has better performance than BP,conventional DE-BP and other chaotic DE-BP methods in stability and accuracy,and higher imaging quality than least square inversion.

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.

Electrical Structure of Wulingshan and Middle Jiangnan Orogen by Three-Dimensional Magnetotelluric Data Inversion

Funded by The National Key Research and Development Program of China,China Deep Exploration(Sinoprobe)and The China Geological Suvery Project on 2009–2019,a large scale magnetotelluric sounding(MT)survey grid(Fig.1)has covered whole south China.

Three-dimensional gravity inversion based on 3D U-Net++

The gravity inversion is to restore genetic density distribution of the underground target to be explored for explaining the internal structure and distribution of the Earth.In this paper,we propose a new 3D gravity inversion method based on 3D U-Net++.Compared with two-dimensional gravity inversion,three-dimensional(3D)gravity inversion can more precisely describe the density distribution of underground space.However,conventional 3D gravity inversion method input is two-dimensional,the input and output of the network proposed in our method are three-dimensional.In the training stage,we design a large number of diversifi ed simulation model-data pairs by using the random walk method to improve the generalization ability of the network.In the test phase,we verify the network performance by using the model-data pairs generated by the simulation.To further illustrate the eff ectiveness of the algorithm,we apply the method to the inversion of the San Nicolas mining area,and the inversion results are basically consistent with the borehole measurement results.Moreover,the results of the 3D U-Net++inversion and the 3D U-Net inversion are compared.The density models of the 3D U-Net++inversion have higher resolution,more concentrated inversion results,and a clearer boundary of the density model.

Inversion of source mechanism of 1989 Loma Prieta earthquake by three-dimensional FEM Green's function

Based on three-dimensional joint finite element, this paper discusses the theory and methodology about inversionof geodetic data. The FEM and inversion formula is given in detail; also a related code is developed. By use of theGreen's function about 3-D FEM, we invert geodetic measurementS of coseismic deformation of the 1989 Ms=7. 1Loma Prieta earthquake to datermine itS source mechanism. The result indicates that the slip on the fault plane isvery heterogeneous. The maximum slip and shear stress are located about 10 kin to northwest of the eathquakesource, the stress drop is about more than 1 MPa.

An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging

To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.

Nonlinear Inversion for Complex Resistivity Method Based on QPSO-BP Algorithm

The significant advantage of the complex resistivity method is to reflect the abnormal body through multi-parameters, but its inversion parameters are more than the resistivity tomography method. Therefore, how to effectively invert these spectral parameters has become the focused area of the complex resistivity inversion. An optimized BP neural network (BPNN) approach based on Quantum Particle Swarm Optimization (QPSO) algorithm was presented, which was able to improve global search ability for complex resistivity multi-parameter nonlinear inversion. In the proposed method, the nonlinear weight adjustment strategy and mutation operator were used to enhance the optimization ability of QPSO algorithm. Implementation of proposed QPSO-BPNN was given, the network had 56 hidden neurons in two hidden layers (the first hidden layer has 46 neurons and the second hidden layer has 10 neurons) and it was trained on 48 datasets and tested on another 5 synthetic datasets. The training and test results show that BP neural network optimized by the QPSO algorithm performs better than the BP neural network without initial optimization on the inversion training and test models, and the mean square error distribution is better. At the same time, a double polarized anomalous bodies model was also used to verify the feasibility and effectiveness of the proposed method, the inversion results show that the QPSO-BP algorithm inversion clearly characterizes the anomalous boundaries and is closer to the values of the parameters.

Simulation of Rock Complex Resistivity Using an Inversion Method

The complex resistivity of coal and related rocks contains abundant physical property information,which can be indirectly used to study the lithology and microstructure of these materials.These aspects are closely related to the fluids inside the considered coal rocks,such as gas,water and coalbed methane.In the present analysis,considering different lithological structures,and using the Cole-Cole model,a forward simulation method is used to study different physical parameters such as the zero-frequency resistivity,the polarizability,the relaxation time,and the frequency correlation coefficient.Moreover,using a least square technique,a complex resistivity“inversion”algorithm is written.The comparison of the initial model parameters and those obtained after inversion is used to verify the stability and accuracy of such approach.The method is finally applied to primary-structure coal considered as the experimental sample for complex resistivity measurements.

Concealed porphyry delineation based on nonlinear three-dimensional densitydifference inversion: An example in the Beiya mine area, Western Yunnan, China

Intermediate acid-complex rock masses with low-density characteristics are the most important prospecting sign in the Beiya area, of western Yunnan province, and provide a physical basis for good gravity exploration. It is usually difficult to obtaining solutions in connection with actual geological situations due to the ambiguity of the conventional gravity-processing results and lack of deep constraints. Thus, the three-dimensional (3D) inversion technology is considered as the main channel for reducing the number of solutions and improving the vertical resolution at the current stage. The current study starts from a model test and performs nonlinear 3D density-difference inversion called “model likelihood exploration”, which performs 3D inversion imaging and inversion of the known model while considering the topographic effects. The inversion results are highly consistent with those of the known models. Simultaneously, we consider the Beiya gold mine in Yunnan as an example. The nonlinear 3D densitydifference inversion technology, which is restricted by geological information, is explored to obtain the 3D density body structure below 5 km in the mine area, and the 3D structure of the deep and concealed rock masses are obtained using the density constraints of the intermediate-acid-complex rock masses. The results are well consistent with the surface geological masses and drilling-controlled deep geological masses. The model test and examples both show that the 3D density-difference nonlinear inversion technology can reduce inversion ambiguity, improve resolution, optimize the inversion results, and realize “transparency” in deeply concealed rock masses in ore-concentrated areas,which is useful in guiding the deep ore prospecting.

Sea Water Intrusion Modeling in Rashid Area of Nile Delta (Egypt) via the Inversion of DC Resistivity Data

The current research focuses on the detection of sea water intrusion in Rashid area which is located about 75 km east to Alexandria, Egypt. For this purpose, geoelectrical survey was carried out using the Schlumberger Vertical Electric Sounding (VES) to identify freshwater thickness, sea water intrusion and estimate subsurface lithology. Seventeen VES stations were measured with current electrode separation (AB/2) ranging from 1.5 m to 100 m. Then, the VES data was interpreted using 1-D and 2-D inversion schemes of DC resistivity data based on least squares method with smoothness constrains. The inverted resistivity distribution at relatively shallow depth shows an important low resistivity zone that probably reflects salt water alteration zone (northern parts). Depth to the freshwater bearing layer reaches its maximum at the south and decreases towards the north. From quantitative interpretation, invasion of salt water started at depth about 10 m at north in the thickness of freshwater bearing layer ranging from 15 to 25 m, while at depth of about 120 m all the layers were saturated with salt water.

Inverse Spline Interpolation for All-time Resistivity of Central-Loop TEM

A convenient numerical calculation method (inverse spline interpolation) for all-time apparent resistivity intransient electromagnetic method (TEM) is proposed in this paper. Characteristic of early and late normalized inductiveelectromotive force was investigated. According to the turning point, the transient process is divided into the earlyphase, the turning point, and the late phase. Afterwards, apparent resistivity is obtained through inverse spline interpo-lation in the early and the late phases, respectively. Finally, the resistivities of the early-time and the late-time wereconnected together by the turning point. The result shows that the inverse spline method is feasible and the method alsolays a foundation for initial model construction in the TEM automatic inversion.