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 i...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.展开更多
广义有效介质极化理论(Generalized Effective Medium Theory for Induced Polarization,GEMTIP)提供了岩石物理参数与复电阻率(Complex Resistivity,CR)的频散关系,据此可基于观测到的激发极化(Induced Polarization,IP)数据反演岩矿...广义有效介质极化理论(Generalized Effective Medium Theory for Induced Polarization,GEMTIP)提供了岩石物理参数与复电阻率(Complex Resistivity,CR)的频散关系,据此可基于观测到的激发极化(Induced Polarization,IP)数据反演岩矿石的激电参数。然而,传统的反演方法在非线性优化问题上存在局部最小值、计算量大和对初始模型依赖度高等问题,且含噪数据反演结果不稳定。此外,当前的激电参数反演研究主要集中在微观岩石孔隙表征和电化学机制领域,基于宏观地球物理观测数据直接进行反演和解释的相关研究不足。为此,提出了一种基于U-Net深度学习网络的方法,利用该方法可基于GEMTIP三维地电模型的地面IP差分数据直接提取激电参数。该方法将多个频率下的差分观测磁场振幅和相位作为网络输入,将异常区域的零频电阻率、体积分数、充电率、时间常数及弛豫常数作为输出标签。通过合成GEMTIP三维地电模型的可控源电磁样本数据训练深度神经网络,得到能够准确预测地下异常区域激电参数分布的网络模型。对包含GEMTIP激电参数的综合模型测试了该网络模型,并将测试结果与传统的正则化共轭梯度(Regularized Conjugate Gradients,RCG)反演结果进行比较,表明U-Net网络反演在耗时、求解精度和抗噪声能力方面均更具优势,能够从地面观测到的IP数据中直接、准确地预测GEMTIP激电参数。最后,利用深度学习方法对亚利桑那州南部North Sliver Bell地区的辉铜矿实际勘测数据进行训练,成功预测了该地区地下辉铜矿富集层分布,并与传统反演方法获得的地质解释成果进行对比,进一步证明了本文方法在实际应用中的可靠性和有效性。该方法可用于矿物组成和储层孔隙空间分布的预测,有望在宏观地球物理反演解释中得到广泛应用。展开更多
基金Projects(41604117,41204054)supported by the National Natural Science Foundation of ChinaProjects(20110490149,2015M580700)supported by the Research Fund for the Doctoral Program of Higher Education,China+1 种基金Project(2015zzts064)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(16B147)supported by the Scientific Research Fund of Hunan Provincial Education Department,China
文摘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.
文摘广义有效介质极化理论(Generalized Effective Medium Theory for Induced Polarization,GEMTIP)提供了岩石物理参数与复电阻率(Complex Resistivity,CR)的频散关系,据此可基于观测到的激发极化(Induced Polarization,IP)数据反演岩矿石的激电参数。然而,传统的反演方法在非线性优化问题上存在局部最小值、计算量大和对初始模型依赖度高等问题,且含噪数据反演结果不稳定。此外,当前的激电参数反演研究主要集中在微观岩石孔隙表征和电化学机制领域,基于宏观地球物理观测数据直接进行反演和解释的相关研究不足。为此,提出了一种基于U-Net深度学习网络的方法,利用该方法可基于GEMTIP三维地电模型的地面IP差分数据直接提取激电参数。该方法将多个频率下的差分观测磁场振幅和相位作为网络输入,将异常区域的零频电阻率、体积分数、充电率、时间常数及弛豫常数作为输出标签。通过合成GEMTIP三维地电模型的可控源电磁样本数据训练深度神经网络,得到能够准确预测地下异常区域激电参数分布的网络模型。对包含GEMTIP激电参数的综合模型测试了该网络模型,并将测试结果与传统的正则化共轭梯度(Regularized Conjugate Gradients,RCG)反演结果进行比较,表明U-Net网络反演在耗时、求解精度和抗噪声能力方面均更具优势,能够从地面观测到的IP数据中直接、准确地预测GEMTIP激电参数。最后,利用深度学习方法对亚利桑那州南部North Sliver Bell地区的辉铜矿实际勘测数据进行训练,成功预测了该地区地下辉铜矿富集层分布,并与传统反演方法获得的地质解释成果进行对比,进一步证明了本文方法在实际应用中的可靠性和有效性。该方法可用于矿物组成和储层孔隙空间分布的预测,有望在宏观地球物理反演解释中得到广泛应用。
