摘要：Three-dimensional(3 D)gravitational and magnetic exploration is performed using aerial measurement tools,however,this has difficulties with measuring-height design and the construction of a joint-interpretation *** present,the height in such experiments is set according to the measurement scale,and the distribution characteristics of anomalies are not fully ***,we present the idea of using the attenuation characteristics of a singular-value spectrum to evaluate the contributions of various measurement heights and multi-height combinations for inversion to correctly and reasonably design appropriate measuring heights and the number of various measurement heights to be *** jointgradient Euler-deconvolution method can accurately obtain the distribution of geological bodies from 3 D gravitational and magnetic data at an improved resolution,and experimental tests confirm these ***,an actual 3 D aeromagnetic-data-acquisition and inversion test were carried out in the vicinity of the Zhurihe Iron Mine in Inner *** fl ight-height diff erence was set to 60 m,and the specifi c distribution of lodes was obtained by the joint-gradient Euler-deconvolution *** provides a reliable basis for future detailed exploration and proves that the methods presented in this paper have good practicalapplication eff ects and prospects.

摘要：Joint inversion based on a correlation constraint utilizes a linear correlation function as a structural *** linear correlation function contains a denominator,which may result in a singularity as the objective function is optimized,leading to an unstable inversion *** improve the robustness of this calculation,this paper proposes a new method in which a sinusoidal correlation function is employed as the structural constraint for joint inversion instead of the conventional linear correlation *** structural constraint does not contain a denominator,thereby preventing a *** with the joint inversion method based on a cross-gradient constraint,the joint inversion method based on a sinusoidal correlation constraint exhibits good *** application to actual data demonstrates that this method can process real data.