In automated guided vehicle (AGV) magnetic navigation systems, magnetic markers typically serve solely as spatial references, offering limited informational content. This paper presents a novel Magnetic Moment Encoding (MME) system, which enables magnetic markers to simultaneously function as both physical landmarks and information carriers. The system leverages three-dimensional magnetic moment parameters of permanent magnets to achieve high-capacity information encoding. Compared to conventional binary magnetic pole encoding methods, MME increases information density by approximately threefold. To address the challenge of decoding MME signals, we propose a neural network-based Magnetic Moment Decoder (MMD), which enables robust decoding of encoded information even in complex magnetic field environments. Integrated into AGV navigation, the MME system achieves a positioning accuracy of 11.88±9.12 mm and a path yaw error of 4.21 ± 2.23°, while also supporting real-time adaptive path planning in dynamic environments. This study introduces a new paradigm for high-density, low-power magnetic information interaction, with broad application potential in industrial automation and the Internet of Things.

Geophysical joint inversion is more frequently applied to deep crust probes. For the potential field data, it requires the introduction of large-scale observed data and sensitivity matrices. Massive matrix-vector multiplications occur during iterations, and the obtained data cannot be effectively interpreted by merely using desktop computers. To improve the resolution and the computing ability of inversion, we here propose the parallel joint nonlinear inversion of full tensor gravity gradiometry data. As the inversion is affected by linear searches, it is associated with certain classical computing performance issues. Hence, we addressed the memory and efficiency limitations, which are caused by very large calculation volumes. Also, we performed a quantitative and comprehensive feasibility analysis of parallel computing. Then, we identified the main factor influencing the inversion performance and clarified the correspondence between the cell number and the memory. A parallel inversion solution was proposed via graphics processing unit (GPU) based on the sensitivity matrix compression. The data tests demonstrated that inversion has antinoise property and that it can obtain accurate underground density distributions. Also, the parallel solution was found to be suitable for inverting cells at the million cell scale and greater because of its ability of acceleration and matrix compression. A design pattern was applied for gravity or magnetic anomaly inversion of 100$\times 100\times20$cells, and the run time was less than 1 min. Overall, we believe that the proposed solution can help implement massive potential field data inversions and promote the application of the parallel technique in other inversion research.

We develop a fast and stable inversion method to solve the underdetermined linear sparsity gravity inversion problem. A new sparsity constraint based on$L0$norm is studied to improve the accuracy of the recovered block density. A projected space algorithm avoids the update of forward matrix in the general reweighted iterative method, whose operations are complex and time consuming. Another advantage of the proposed projected algorithm is that it is compatible with other fast gravity methods. A general iterative algorithm is applied to solve the general gravity inversion. This space algorithm projects the recovered density on the sparsity constraint. With the application of space algorithm, the large forward matrix is calculated once. A test of synthetic example demonstrates the advantages of inversion efficiency and accuracy. The application of real gravity obtained from Mobrun ore, northeast of Noranda, QC, Canada shows the practical value of the proposed inversion method. This novel study can be used in other geophysical inversions.

Integrated gravity and gravity gradient 3-D inversion can effectively improve the resolution of inversion results. This study aims to find a better way to extract geological information from gravity and gravity gradient data and to obtain the recovered model with a higher resolution. We present three different methods for integrating gravity and gravity gradient data in inversion: directly integrating gravity and gravity gradient data (DIGG), the sequential inversion of gravity and gravity gradient data (SIGG), and integrating gravity and gravity gradient components using a weighting matrix (IGGW). In addition, we propose a new discrete method for smooth inversion. We discretize the stabilizing functional into two parts. One is the square of the 2-norm of the model parameter, and the other one is the square of the Frobenius norm of the gradient of the model parameter. We adopt a conjugate gradient algorithm (CGA) to solve the objective function. We used the DIGG, SIGG, and IGGW inversion methods in a dip-slab model with multiple anomalous bodies. The inversion results show that by integrating gravity and gravity gradient in inversion, more reasonable results are obtained than by inverting a single data type. Among the presented methods, the IGGW method achieved the best performance in using information contained in gravity and gravity gradient data, followed by the SIGG method, and finally, by the DIGG method. We used the methods on actual data from Vinton salt dome, southwestern Louisiana, USA. The obtained results support the abovementioned conclusions.

Airborne gravity gradiometry is an effective tool for its high efficiency and sensitivity to the interesting buried targets, which has been increasingly introduced in mineral and petroleum exploration. When compared to gravimetry, full tensor gradient (FTG) measurements provide higher resolution and signal-to-noise ratio. In this article, we build and test models with different sampling intervals and compare the performances of individual components or their combinations in minimum structure inversion to evaluate the benefits of FTG combinations. As part of a theoretical study, the singular value spectrum (SVS) and depth-resolution plot (DRP) are examined to determine how much information and resolution different datasets provide in the underdetermined inverse problem. The synthetic examples show that integrating more components in inversion improves the resolution of the recovered model, and the inversion result of gravity or individual component cannot accurately restore the distribution of anomalous bodies. With the increasing sampling interval, joint inversion of FTG data still provides enough information corresponding to the source density distribution, and the limit survey interval is tested through models. In addition, we use a real survey over the Vinton dome as a case study and discover that$V_{\mathrm {zz}}$component fails to reconstruct the caprock model at coarse line spacing, whereas FTG combinations recover the acceptable geometry flawlessly. It also reflects the benefit of FTG combinations for inversion processing, which reduces the interference of measured noise on inversion results and allows for longer sampling intervals during the survey, effectively lowering the cost of an airborne survey and improving exploration efficiency