中文摘要：

在CSAMT测深中，测深曲线往往受到电偶极源的影响而出现畸变，这给CSAMT数据的反演解释带来很大的困惑。文中依据CSAMT法在波区与MT理论相似的原理，提出用Bostick半定量结果作为CSAMT一维反演的初始模型，采用传统最小二乘算法，结合多种常识性物性变化特点，优化层参数并控制反演过程的迭代方向，使纯粹的数值迭代转化为按地球物理特征自动迭代，其结果可更好地满足物探解释需要。算例和实测数据反演表明，优化后的CSAMT一维反演方法，反演精度较高、计算速度较快，其反演结果的正演响应曲线和实测曲线拟合得很好。

英文摘要：

In the application of CSAMT,the sounding curve is often distorted with the impact of electric di- pole source,which produces confusions in data inversion and interpretation. Based on the 1-D layered earth conventional least-square inversion for CSAMT data, this paper introduces an optimized tech- nique to get better result by discussing the forward modeling calculation, mechanism of inversion, selection of initial model, and iteration model controlling. Formulas of electromagnetic field components, E, and Hy, given as integrations which contain Bessel functions of first kind of order 0 and 1, can be obtained using numerical method. During the calculation, the kernel functions of integrations are changed respectively in order to get better conver- gence properties. Taking the short transmitter-receiver distance into account, the relatively long grounded linear wire source should be assumed as an accumulation of dipoles, thus the theoretical ap- parent resistivity can be calculated by the field responses of dipole series. The forward calculation of CSAMT needs configuration parameters such as source location,measuring point position, current amplitude and so on,which are different from MT modeling. A primary objective of inversion is to recover a geologically interpretable model that can repro- duce the set of observations. Based on the least square inversion theory,the method iteratively updates the vector of model parameters to get the minimum of the vector of relative mean square errors, where the Jacobi matrix can be obtained by difference method. Singular value decomposition method is adopted in equations solving, and we replace small singular value with zero. To get rid of falling into local minimum point during the iteration, it is necessary to control the model parameter excess when the model becomes abnormal. Actually the action will destroy the convergence, but it also increases the chance to find out the global minimum point. Numerical experiments show that the inversion result is better than the c