In the paper we present a new method to invert the interior structure in the basement or ancient hidden hill by using magnetotelhiric (MT) data with seismic data constraint. We first obtain the thickness and resistivity of each layer above the basement or buried hill by the inversion of seismic and log data and create a geoelectrical model for the layers above the basement or hidden hill. Then with the reference to the inversion of 1D MT data, a geoelectrical model for the layers below the basement or hidden hill is created. On the basis of the above initial model, we present an effective and practical forward method, i.e., a model-matched approach to conduct forward inversion arithmetic. Finally, by the method of conjugate gradient iteration, a forward and backward iterative calculation is made. Taking No. 618 profile of Shengli Oil Field as an example, we have found out that the tectonic information that is unreflective in the seismic data below the basement is better reflected in the inversion result.

Based on the study on electromagnetic field migration by Zhdanov, we have proposed an improved mefihod for the weak points in the research. Firstly, the initial background resistivity should be determined by using 1-D inversion results. Then in the process of continuation, the results are corrected and calculated layer by layer by the iteration method, so that more exact resistivity can be obtained. Secondly, an improved algorithm for finite-difference equation is studied. According to the property of electromagnetic migration field, the algorithm is designed by means of grids varying with geometric progression in the longitudinal direction. Being improved by the techniques mentioned above, better results are obtained by the new method, which has been verified by both the theory model and practical data. 107The problem how to separate the observed field into upgoing and downgoing fields is not discussed in detail, only the method in seismic exploration is used. Then we rebuilt the underground distribution of electromagnetic field with the finite-difference method. According to the principle of imaging, we made a migration and imaging for the rebuilt field and finally transformed it into a resistivity imaging problem.After many years of systematic study and with the reference of inverse-time seismic migration, Zhdanov et al (1996) applied the principle of wave-field analyses, i.e., the seismic migration and imaging in an inverse extrapolation from the surface to the subsurface, to the interpretation of electromagnetic field. They proposed the concept of electromagnetic field migration according to the above principle and combined the migration with inversion in practical data processing. However, there are some problems in the method, such as how to select background conductivity, and how to make migration and imaging of complex interface. In addition, the accuracy is not sufficient for high-precision migration and imaging when the matrix equation is solved by the chase algorithm. To solve the problems in these respects, we propose a new and accurate algorithm to optimize the conductivity by the iteration method. The result is better than that obtained by the old method after the final validation is made from both theory and practice.

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In order to identify micro‐seismic events of single seismic phase and locate the identified microseismic events as well, we have researched the method of identifying the micro‐seismic events on the basis of the arrival time difference between any two traces in the case of single seismic phase and the characteristic rule of relationship of micro‐seismic events, spatial location of detector and the seismic phase velocity. First, by analyzing the internal variation of the arrival time difference and the above changes of variables, we establish the quantitative calculation relationship between the arrival time difference and every variable. Then, with regard to the arrival time differences of the adjacent trace and the first and the last trace of detector array, we research the specific expression of the quantitative calculation relationship. Given the spatial location of the event, we can calculate the distribution range of the above two kinds of arrival time differences using the relation expression above. Comparing the distribution range of two arrival time difference of the actual micro‐seismic events which we have calculated to the given event, we can identify the single seismic phase with the range which they fall to. Integrating the advances of the search method and the genetic algorithm, we put forward a micro‐seismic joint inversion method based on the solution domain constrained iterative forward model. From the analysis of the expectation probability distribution of the iterative error solution and the variations of different directions of the solutions, we establish the definition conditions of the domain of solution constraints. In the determinate scope of the solution domain, we carry out the inversion of micro‐seismic events using the genetic algorithm. Through the analysis of application of practical data, the method of identifying the micro‐seismic events reduces the error ratio of phase recognition greatly. According to the identified information, we do the inversion of actual data using the inversion method. Comparing with the actual situation, the result is consistent with the actual crack distribution, and is fairly good in convergence.