南海中部海盆海山磁性反演及初步解析

南海中部海盆分布着众多海山,从实测磁异常反演海山磁性是南海海盆古地磁研究新的重要课题,它有助于解决南海海盆的运动方向和运动形式等问题.对南海海盆16座海山作磁性反演取得了较高的计算精度.对南海中部海盆海山磁性反演表明,海山均为非均匀磁化体,海山形成经过多期火山喷溢叠加.海山磁性差异清晰地显示了海盆分区特征.南海海盆分东部海盆区和西南部海盐区,海盆地壳运动规律差异较大:东部海盆以逆时针旋转由南往北运移;西南海盆先经历了顺时针旋转,后改为逆时针旋转,由北往南运移.

The 3D magnetic structure beneath the continental margin of the northeastern South China Sea

Understanding the continental margin of the Northeastern South China Sea is critical to the study of deep structures, tectonic evolution, and dynamics of the region. One set of important data for this endeavor is the total-field magnetic data. Given the challenges associated with the magnetic data at low latitudes and with remanent magnetism in this area, we combine the equivalent-source technique and magnetic amplitude inversion to recover 3D subsurface magnetic structures. The inversion results show that this area is characterized by a north-south block division and east-west zonation. Magnetic regions strike in EW, NE and NW direction and are consistent with major tectonic trends in the region. The highly magnetic zone recovered from inversion in the continental margin differs visibly from that of the magnetically quiet zones to the south. The magnetic anomaly zone strikes in NE direction, covering an area of about 500 km × 60 km, and extending downward to a depth of 25 km or more. In combination with other geophysical data, we suggest that this strongly magnetic zone was produced by deep underplating of magma associated with plate subduction in Mesozoic period. The magnetically quiet zone in the south is an EW trending unit underlain by broad and gentle magnetic layers of lower crust. Its magnetic structure bears a clear resemblance to oceanic crust, assumed to be related to the presence of ancient oceanic crust there.

地面高精度磁测在西藏恰功铁矿勘查中的应用效果

恰功铁矿是近几年新发现的矿山,然而矿体的形态、大小、埋深、位置、产状、边界等几何特征还没有被认识清楚。为了弄清这些问题,本文通过欧拉反褶积方法对磁异常化极数据进行了反演,反演矿体深度为0-120m;在C-6异常中心选择了两条剖面进行了2.5维拟合反演,反演矿体厚度为20~30m,欧拉反褶积反演结果和2.5维拟合反演结果与ZK32钻孔验证结果相吻合。最后,通过建立恰功矽卡岩型铁矿地质—地球物理找矿模型,为该地区寻找隐伏夕卡岩型铁矿提供了思路。

Nonlinear inversion for magnetotelluric sounding based on deep belief network

To improve magnetotelluric(MT)nonlinear inversion accuracy and stability,this work introduces the deep belief network(DBN)algorithm.Firstly,a network frame is set up for training in different 2D MT models.The network inputs are the apparent resistivities of known models,and the outputs are the model parameters.The optimal network structure is achieved by determining the numbers of hidden layers and network nodes.Secondly,the learning process of the DBN is implemented to obtain the optimal solution of network connection weights for known geoelectric models.Finally,the trained DBN is verified through inversion tests,in which the network inputs are the apparent resistivities of unknown models,and the outputs are the corresponding model parameters.The experiment results show that the DBN can make full use of the global searching capability of the restricted Boltzmann machine(RBM)unsupervised learning and the local optimization of the back propagation(BP)neural network supervised learning.Comparing to the traditional neural network inversion,the calculation accuracy and stability of the DBN for MT data inversion are improved significantly.And the tests on synthetic data reveal that this method can be applied to MT data inversion and achieve good results compared with the least-square regularization inversion.

Detection performance of azimuthal electromagnetic logging while drilling tool in anisotropic media

Azimuthal electromagnetic(EM)logging while drilling(LWD)has been extensively used in high-angle and horizontal(HA/HZ)wells.However,due to the effects of formation anisotropy,accurate geosteering decision and formation evaluations have become increasingly difficult.To quantitatively analyze the effect of anisotropy on tool responses and data processing,this paper investigates the sensitivity of EM LWD measurements to electric anisotropy and inversion accuracy via forward modeling and inversion.First,a sensitivity factor is defined to quantitatively analyze the sensitivity of the magnetic field components and synthetic signals to electric anisotropy.Then,azimuthal EM LWD responses in anisotropic layered formations are simulated,and the sensitivities to formation parameters for compensated and uncompensated tool configurations are comparatively analyzed.Finally,we discuss the effects of the inversion model on bed boundary inversion in anisotropic formations.Numerical simulation and inversion results show that azimuthal EM LWD can be significantly affected by electric anisotropy.Fortunately,by using a symmetrical compensation configuration,the sensitivity of the geosignals to electric anisotropy can be suppressed,and the boundary detection capability can be further enhanced.Anisotropy normally gives rise to separated resistivity curves and abnormal"horns";moreover,complicated nonlinear distortion can also arise in geosignals as the tool approaches a bed boundary.If anisotropy effects are ignored in the inversion process,the estimated bed boundary and formation resistivity are usually unreliable,which may mislead geosteering decisions.

寿光磁异常区找矿方向探讨

寿光磁异常是山东仅次于济宁的第二大磁异常。由山东省第四地质矿产勘查院实施的ZK401验证孔,在孔深856.22～1 600.87m处见中生代白垩纪青山群八亩地组地层,是一套玄武安山安山岩石组合,认为寿光磁异常是具有磁性的中生代火山岩系引起的。该文依据寿光磁异常区的重、磁异常特征,结合所处的地质构造环境,通过反演磁性体的空间分布形态,推断寿光磁性体是夹持在前古生代地层(寒武系)间的类似山顶到山谷的一个楔型火山岩盆地。套用火山机构的划分模式,把磁性体所处的潜火山岩细分为火山喷发沉积岩相、火山通道相和重熔侵入岩相等3个岩相。依据火山岩相与矿产的关系,认为寿光火山岩系的深部可能有规模较大的岩浆岩型铁矿存在。寿光磁性体的中、深部的火山通道相是金、铜、铅、锌、钼等矿产的富集场所,是今后勘查找矿的重点靶区。

3D inversion modeling of joint gravity and magnetic data based on a sinusoidal correlation constraint

Joint inversion based on a correlation constraint utilizes a linear correlation function as a structural constraint.The linear correlation function contains a denominator,which may result in a singularity as the objective function is optimized,leading to an unstable inversion calculation.To improve the robustness of this calculation,this paper proposes a new method in which a sinusoidal correlation function is employed as the structural constraint for joint inversion instead of the conventional linear correlation function.This structural constraint does not contain a denominator,thereby preventing a singularity.Compared with the joint inversion method based on a cross-gradient constraint,the joint inversion method based on a sinusoidal correlation constraint exhibits good performance.An application to actual data demonstrates that this method can process real data.

A two-stage CO-PSO minimum structure inversion using CUDA for extracting IP information from MT data

The study of induced polarization （IP） information extraction from magnetotelluric （MT） sounding data is of great and practical significance to the exploitation of deep mineral, oil and gas resources. The linear inversion method, which has been given priority in previous research on the IP information extraction method, has three main problems as follows： 1） dependency on the initial model, 2） easily falling into the local minimum, and 3） serious non-uniqueness of solutions. Taking the nonlinearity and nonconvexity of IP information extraction into consideration, a two-stage CO-PSO minimum structure inversion method using compute unified distributed architecture （CUDA） is proposed. On one hand, a novel Cauchy oscillation particle swarm optimization （CO-PSO） algorithm is applied to extract nonlinear IP information from MT sounding data, which is implemented as a parallel algorithm within CUDA computing architecture; on the other hand, the impact of the polarizability on the observation data is strengthened by introducing a second stage inversion process, and the regularization parameter is applied in the fitness function of PSO algorithm to solve the problem of multi-solution in inversion. The inversion simulation results of polarization layers in different strata of various geoelectric models show that the smooth models of resistivity and IP parameters can be obtained by the proposed algorithm, the results of which are relatively stable and accurate. The experiment results added with noise indicate that this method is robust to Gaussian white noise. Compared with the traditional PSO and GA algorithm, the proposed algorithm has more efficiency and better inversion results.

Magnetic interface forward and inversion method based on Padé approximation

The magnetic interface forward and inversion method is realized using the Taylor series expansion to linearize the Fourier transform of the exponential function. With a large expansion step and unbounded neighborhood, the Taylor series is not convergent, and therefore, this paper presents the magnetic interface forward and inversion method based on Pade approximation instead of the Taylor series expansion. Compared with the Taylor series, Pade＇s expansion＇s convergence is more stable and its approximation more accurate. Model tests show the validity of the magnetic forward modeling and inversion of Pade approximation proposed in the paper, and when this inversion method is applied to the measured data of the Matagami area in Canada, a stable and reasonable distribution of underground interface is obtained.