For a variety of applications, magnetic data is collected from airborne platforms. Normally, this data is collected with sensors that measure the total field or amplitude of the magnetic vector data. New generations of optically pumped sensors are extremely sensitive with their sensitivity quoted often in picoteslas. At present, some new instrumentation is also attempting to measure high accuracy vector data. Despite the accuracy of modern sensors and data acquisition systems, the noise of the flying platform is still one of the limiting factors in obtaining highly accurate data.The aircraft or helicopter itself emanates magnetic signals. These signals are due to a number of factors including induced fields due to magnetically susceptible materials and permanent magnetic materials on the platforms as well as both induced electromagnetic signals and electromagnetic signals generated either by electrical systems or moving parts such as rotors.This subject of this paper are problems and techniques related to removing the effects of the moving platform as well as attempts to study the subject with the use of simulated data.
In our previous work (R.W. Groom, R. Jia And C. Alvarez 2003), we have developed algorithms to implement an Euler depth estimator as well as an inversion algorithm for detecting a simple dipole, which is often a suitable model for UXO applications. These algorithms worked independently with a single anomaly. We have combined these algorithms to determine the locations as well as the internal magnetization vectors of buried objects in a practical field survey. We start with the Euler deconvolution depth estimator that gives the locations of buried objects using the measured total field and its measured/calculated gradients. Based on these initial results, a subset of measured data is selected and a local search grid is set for each individual body. Then the magnetization inversion algorithm is utilized to find the locations and internal magnetization vectors of the buried bodies. The inversion process involves performing an automatic iterative grid volume modification according to a prescribed volume range of the buried objectives. Consequently we can determine the location and internal magnetization vector of each individual body by applying the inversion algorithm in an automatic way. In general, the locations identified in this way are more accurate due to the fact that only the measured total field data is used in the later stage. It is shown that Magnetization Vector Inversion is relatively insensitive to data density and thus works more stably. Furthermore, starting with good Euler solutions is essential to guarantee an appropriate selection of dataset that incorporates substantial variation of the field and field gradients of each object.
We have extended our three-dimensional magnetic modeling capabilities to simulate TMI, magnetic vector and gradient measurements for both permanent magnetization and strong induced effects. We have developed capabilities to model quite general 3D shapes including conical and cylindrical objects both solid and hollow. These general shapes can be combined to represent projectile shells with quite general shapes having varying internal magnetic properties. With these simulation capabilities, we investigated the use of inversion algorithms to determine the internal magnetization vector of buried objects. Our objectives are to understand the limitations of recovering the location of the magnetization vector as well as its magnitude and vector orientation. Determining the strength and orientation of the internal magnetization can help in the discrimination of material properties. Our experiments include examination of data sampling, data noise and combinations of TMI, vector and gradient measurements to resolve the magnetization. As an example, we determined that with adequate data sampling one could determine, extremely accurately, the location and orientation of the internal magnetization vector only if the volume of the object is known. This was accomplished by non-linear inversion combined with iterative grid volume modification. In addition, we have experimented with the use of a modified Euler deconvolution technique for depth estimation. At present, we are working with combining the two techniques.
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