The Research On The Edge Recognition Methods And Techniques For Potential Field

The gravity and magnetic exploration is one of the earliest geophysical exploration methods. We can study the geometric or physical parameters of geological body by the measured gravity and magnetic potential field data, and the prerequisite for the use is there have certain differences in the density or magnetic properties in horizontal between the detected objects and the country rock. The research on the geometric parameters consists of three stages:firstly, to determine the edge plane position of the geological body, known as edge position recognition; secondly, to study the vertical position (depth) of geological body edge, known as edge parameters inversion; and the last is to research the geological interface fluctuation, known as geological interface inversion. The above three steps are mutually independent as well as mutually related. The edge position recognition is the basic problem, the edge parameter inversion is the transitional period and the geological interface inversion is the final aim. Therefore, the theoretical study on the edge recognition methods can not only solve the edge recognition problems of geological bodies, but also lay a foundation for the further theoretical study on the edge inversion and interface inversion. In addition, the research on the technology and measures of edge recognition can not only better solve the edge recognition problems, but also promote the progress of potential field theory. Therefore, the researches on the edge recogniton methods and recogniton techniques have important theoretical significance. In China, the regional gravity & magnetic exploration, mining and energy resource gravity & magnetic exploration, all need to identify the fractures distribution and the rock boundary in the measured zone. So the potential field research on edge detection has important actual significance and practical application value.The research relies on the 10th five years programs for science and technology development of China—Large Scarce Mineral Resource Base Comprehensive Exploration and Efficient Development Research Topics of "Research on middle-and large-scale high-resolution aeromagnetic methods and techniques in complex terrain area" (Item No. 2001BA609A-5-3). Relies on National major special science and technology project—Large Oil & Gas Fields and Coal-bed Methane Development of "Key technology of deepwater oil and gas exploration" (Item No.2008ZX05025). This work is also supported by the project of national oil and gas resources strategic constituency survey and evaluation of the Ministry of Land and Resources (Item No.XQ-2007-03 and XQ-2007-05) and the second-stage project (Item No.2009GYXQ03,2009GYXQ05,2009GYXQ06 and 2009GYXQ09). The main aim of this paper is to research the automatization and systematization of edge recognition technology for potential field data. By means of analytic analysis, a profound theoretical and practical application research on edge recognition methods and techniques for the potential field data has been carried out. The main research results and conclusions are as follows:(1) Expressed the computational formula of gravity & magnetic anomalies and their derivative in the derivative of Green function (potential function). And Derived the mixed derivative expressions of Green function of first-, second-, third-or forth-order for the vertical step, tilted step, vertical parallelogram, ladder-step, vertical hexahedron, horizontal cylinder and sphere. Finally, we obtained the gravity & magnetic anomalies and their derivative expressions of the above simple and regular geological bodies. These results provide theoretical basis for further research on the potential field edge recognition.(2) Studied the function properties of the potential field edge recognition methods belong to numerical compution object, classified the edge recognition methods, and at last, and pointed out the "analytic singularities" is the main reason causing poor numerical stability. The research results show that the edge recognition methods are classified into the first-order derivatives, second-and higher-order derivatives by the orders of derivative. The most basic edge recognition methods are vertical derivative, the total horizontal derivative and amplitude of analytic signal, and the other methods are developed on the above three methods. In all of the edge recognition methods, only the vertical derivative method remains the nature of the potential functions, the others can not. For the edge detection method with potential function as the original function, there hasn’t the "analytic singularities", and the results of edge recognition methods are stable. Otherwise, there has the "analytic singularities" and the results are unstable. Therefore, the research focus on the various edge detection methods with potential function as the original function, in other words, on the various edge recognition methods based on the gravity anomalies and the gravity anomalies vertical derivative. For the magnetic anomaly, the data should be converted into pseudo-gravity anomaly or the RTP magnetic anomalies. The edge recognition methods used for pseudo-gravity anomalies similar to that used for gravity anomaly, while the edge recognition methods used for the RTP magnetic anomalies similar to that used for gravity anomaly vertical derivative.(3) Studied the spatial variation rule of the zero value position of gravity anomalies vertical derivative, the maximum value position of total horizontal derivative and the maximum value position of analytic signal amplitude. Research results show that, For the models with a single boundary, all of the edge recognition methods can accurately recognize edge location of the vertical border by the means of the zero value or the maximum value position, but can not to the single-border tilted body. For the models with a tilted boundary, the maximum value position of analytic signal amplitude has the least deviation from the top border location, followed by the maximum value position of total horizontal derivative, while the zero value position of the vertical derivative has the maximum deviation. Either the zero value position or the maximum value position, move from the top border position to bottom with the geological body depth increasing, and the deviation is no more than half width of inclined interface. For the models with double boundaries, the spatial variation rule of zero value position and maximum value position takes a huge difference with that of the models with single boundary. When the depth of geological body is shallow, the zero value position of vertical derivative, the maximum value position of total horizontal derivative and the maximum value position of analytic signal amplitude behave the same spatial variation rule as that of the models with single boundary. When the depth of geological body increases to a certain depth, the maximum value position of analytic signal amplitude disappears, which is called " the blind zone of maximum value position " and lead to the failure of edge recognition. And with the depth increases, the zero value position of vertical derivative and the maximum value position of total horizontal derivative all move from the top border position of the body to the outside, and the deviation of the zero value position of vertical derivative is the average buried depth of the geological body, the deviation of the maximum value position of total horizontal derivative is of 1/√3 times of the average buried depth. And with the depth further increases, the maximum value position of analytic signal amplitude converges from the top border to the inside, and quickly converges to the center of the body, which also leads to the failure of edge recogniton. For the models with multi boundaries, the zero value position of vertical derivative, the maximum value position of total horizontal derivative and the maximum value position of analytic signal amplitude behaves the same spatial variation rule as that of the models with double boundaries. Contrasting from the deviation, total horizontal derivative has the least deviation, followed by the vertical derivative, while analytic signal amplitude has the most.(4) Studied the recognition ability of the above-mentioned three kinds of basic edge recognition methods by the spatial variation rule of the zero value position of gravity anomalies vertical derivative, the maximum value position of total horizontal derivative and the maximum value position of analytic signal amplitude for the combined-models. The research results show that, for the gravity anomalies of combined-models, the zero value position of vertical derivative, the maximum position of total horizontal derivative and the maximum position of analytic signal amplitude were influenced by the zero value position or the maximum value position of the single model. And the recognition ability is subject to the following factors, such as buried depth, the size in horizontal and boundary geometry, and so on. In a word, total horizontal derivative shows the best recognition ability, followed by vertical derivative, and analytic signal amplitude has the worst ability.(5) Studied the spatial variation rule and the recognition ability of the edge recognition methods based on the gravity anomaly vertical derivative, such as the zero value position of the second-order vertical derivative, the maximum value position of total horizontal derivative of the vertical derivative, the maximum value position of the vertical derivative of analytical signal amplitude. The theoretical study results show that the edge recognition based on the gravity anomaly vertical derivative has smaller derivation and more power resolution than that based on the gravity anomaly. And the results of total horizontal derivative of vertical derivative are affected by the "secondary maximum value". The "secondary maximum value" is small and can be neglected.(6) Put forward the extending edge and interpolation technology based on the minimum curvature method, the frequency domain dipole layer method for the processing and transformation of potential field on curved surface and the edge recognition technologies (the peak sharpening technology, the threshold technology and the normalized enhancement technology). The above technologies enable edge detection step by step to the systematic, automated and high-precision development. From the results of the models and field data, the extending edge and interpolation technology, presented in this paper, overcomes the Gibbs effect and improves the results accuracy of the processing and transformation in the frequency domain. The proposed frequency domain dipole layer method for the processing and transformation can not only reduce the potential field data to a horizontal plane, and at the same time, directly calculate variety kinds of derivatives used to recognite the edges of geological bodies, but also make the processing and transformation of pseudo-gravity anomaly or the RTP magnetic anomalies. The proposed peak sharpening technology and threshold technology can not only enhance the ability of distinguish the edge, but also highlight the effect of geological body boundaries, making the map more simple and clear, and easier to identify. The adopted normalized enhancement technology highlights the geological body with small-scale, delicate-difference in physical properties and deeper-depth.(7) Interpreted the fault distribution and divided the tectonic units of Hanjiang depression and it’s adjacent regions by use of the gravity anomaly from satellite altimetry and the aeromagnetic anomaly. The research results show that the accuracy of the gravity anomaly from satellite altimetry is higher than 1:100 million scale gravity exploration work requirements, and is close to 1:50. Through the fault identification and division of tectonic units, the results indicate that there is a clear NWW uplift in the middle of Hanjiang depression. And the faults, on both sides of the uplift, divided the Hanjiang depression into north and south parts. The NE orientation faults are in the southern, and the NNE orientation faults are in the northern. The Hanjiang depression, with the southern sediment dominated by Cenozoic and the northern dominated by Mesozoic, is an overlaid sedimentary depression of Mesozoic-Cenozoic. Our research results provide a new basis for the study of the Hanjiang depression structure, construction and the distribution of oil-gas resources.