3-D Gravity And Magnetic Inversion For Physical Properties Based On The Correlation Constraint

With the gravity and magnetic (GM) exploration developing, 3D GM inversion has become a trend. Inversion for physical properties which outline source scope according to physical properties chages become an important inversion mode because it can simulate more complex source.However, ambiguous solutions is very serious in 3D GM inversion for physical properties. The huge calculation leads to some quantitative calculation methods which have good inversion results for models can not reach the level of utility. In this thesis, quantitative inversion method for physical properties with correlation constrains which are established based on the relationship between GM anomaly and field source physical parameters is propoesd in order to improve the speed of inversion computation and reduce ambiguous solutions. Severval keys such as constraint intruduction, inversion algorithm and the combination of constrains are studied.Correlation constraint is studied which aim is reducing dimensions of solution space. Virtual depth is proposed to reflect the depth of field sources and establish a quantitative relationship between virtual depth and actual depth of source. Based on the relationship between GM anomalies and field source physical parameters, automatically source searching method is proposed. If there are known information got from well data, information expansion mechanism based on the feature of virtual depth is posed.The nature of these constrains is reducing the number of units which are inversed.These constrains can be combined with any other inversion algorithm easily and have strong adaptability.Interval search algorithm is selected as inversion algorithm. The expansion of inverval algorithm from low dimension to high dimension is achieved.The huge computation of high dimensional searching is beyond now computing ability. The idea of probability tomography is introduced to reduce its computation. Tomography probability of unit is calculated and the order of interval searching is from large to small probability. Compared with other global optimization algorithms, this method involves fewer parameters, the selecting principles are easy to grasp.Quantitative GM inversion under the correlation constraint is studied based on above research results. Firstly, determine which units are inversed according to the correlation constraint mechanism. Then calculate tomography probability of units and search optimal solution in their physical parameter interval from the unit of largest probability firstly.A great deal of theoretical models is designed to summarize the principle to choose parameters and verify the effectiveness of method.Compared with non-constrained inversion results, not only greatly improved computing speed, ambiguous solutions also have been significantly reduced.Finally, the method was applied to inverse virtual density and magnetization of igneous rock in Linshang area of Huimin depression.From the imaging result, the space distribution of igneous rock of the area was deduced.