In recent years, the advancement of unmanned aerial vehicle (UAV) technology has led to the gradual application of UAV aeromagnetic technology in small-area production for precise measurements. The flexibility of unmanned platforms allows for efficient detection. Unlike pilot-controlled fixed-wing aircraft, UAV platforms have smaller payloads and shorter endurance. Hence, it is essential to simplify the field acquisition process as much as possible. This has led to the development of rapid solution algorithms for magnetic compensation coefficients and related software. This letter is based on the Tolles and Lawson aeromagnetic compensation model. It analyzes the relationship between attitude and magnetic total field data, proposes a small signal improvement (SSI) algorithm, and conducts magnetic compensation experiments on two different types of fixed-wing UAVs. The results show better compensation effects.

Lattice spring model (LSM) is a novel method to simulate seismic wave propagation from a micromechanical perspective, which describes the elastic dynamics in complex media comprehensively and delineates the dynamic characteristics of the wavefield. However, it remains a great challenge to simulate wavefields at irregular interfaces with high accuracy. This work presents an adaptive location LSM (ALLSM) method to substantially improve the accuracy of simulated wavefield in a solid model. First, we design an algorithm to pick up the intersection coordinates of the internal interfaces with regular mesh. We obtain the intersection coordinates by solving the defined interface functions with different kinds of spring functions in the LSM. Then, we calculate the weights of springs with different elastic coefficients in the whole grid by the intersection coordinates. Finally, we obtain the real spring elastic coefficients in the presence of irregular interfaces by the arithmetic weighted average method, and develop the corresponding interface position ALLSM to simulate the wavefield propagation mechanism, in order to substantially improve the accuracy of simulation. Numerical tests show that the improved ALLSM is more accurate than conventional LSM and second-order finite difference method (FDM). The accuracy of simulated wavefields is significantly improved about 20%. The proposed method offers a new tool for wavefields simulation in complex media.

Staircase approximation in the simulation of the arbitrarily irregular surface will cause artificial interference of wavefield. The body-fit coordinate transformation can accurately simulate the arbitrarily irregular surface and avoid the staircase approximation. The finite difference method (FDM) based on body-fit coordinate system (BFCS) can simulate the propagation process of wavefield in the media with an undulating surface. Currently, the collocated grid is usually used in BFCS. However, adopting this method to simulate waves based on the first-order velocity-stress wave equation in the BFCS will cause oscillation due to the odd-even decoupling; therefore, additional filtering operations are required. To solve the above problem, we propose collocate-staggered grid (CSG) to simulate the first-order velocity-stress wave equation in the 2-D BFCS. CSG uses staggered grid scheme to calculate the equation parameters and adopts collocated grid scheme to store these values. We use second-order accuracy spatial and temporal difference for numerical tests. The results show that the elastic wavefield modeling method in the BFCS using CSG difference scheme has the characteristics of high accuracy and stable. This method is suitable for the wavefield simulation in a medium with a large Poisson’s ratio.

The solid geometric objects in the educational geometric books are usually illustrated as 2D line drawings accompanied with description text. In this paper, we present a method to recover the geometric objects from 2D to 3D. Unlike the previous methods, we not only use the geometric information from the line drawing itself, but also the textual information extracted from its context. The essential of our method is a cost function to mix the two types of information, and we optimize the cost function to identify the geometric object and recover its 3D information. Our method can recover various types of solid geometric objects including straight-edge manifolds and curved objects such as cone, cylinder and sphere. We show that our method performs significantly better compared to the previous ones.