The small-loop transient electromagnetic (TEM) method has great application prospects in shallow surface geophysical exploration. However, the theoretical research on the forward and inversion of this method is obviously lagging behind, which seriously affects the application effect in practical engineering. For the oscillation problem of the small-loop TEM response calculated by using the forward theory of the conventional TEM methods, the dipole integral superposition method was proposed to solve the problem in this article. The transmitter source is divided into an infinite number of electric dipoles, and the calculation expressions of the electromagnetic field components satisfied by the electric dipoles are deduced, and the forward calculation problem of the layered model of the small-loop TEM method is solved. For the small-loop TEM method inversion problem, the simulated annealing (SA) method was presented for inversion calculation in this article. First, the influence of the selection of the initial temperature on the inversion result of the SA algorithm was analyzed. Then, we demonstrate the algorithm’s flexibility by choosing different initial models and search ranges and concluded that the different initial models and search ranges do not affect the inversion results of the SA algorithm. Next, the typical layered models were established, and the SA algorithm was used to carry out calculations. The research results show that the SA algorithm is an excellent global optimization algorithm, which can easily jump out of the local minima and converge to the global minima. Finally, we processed the measured data through inversion calculations and compared it with the results of the other three methods to verify the effectiveness and accuracy of the SA algorithm for small-loop TEM data processing. The research content solves the theoretical problem of the small-loop TEM method forward and inversion, promotes the development of the small-loop TEM method technology, and objectively and effectively solves practical engineering application problems.

Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted by background. In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking. Furthermore, we introduce a double-head predictor to boost the accuracy of foreground-background classification and regression of target bounding boxes, which further improve the tracking performance. Extensive experiments show that, without bells and whistles, our method significantly outperforms the state-of-the-art approaches on LaSOT, GOT-10k, TrackingNet, and UAV123, while running at 40 FPS. Notably, the training time of our method is reduced by 75% compared to that of TransT. The source code and models are available at https://github.com/fzh0917/SparseTT.

Transient electromagnetic (TEM) method is widely used in regional mineral resources surveys, environmental engineering geological surveys and shallow surface geophysical exploration, and so on. However, interpretation and inversion of TEM data is a complicated process. The traditional algorithm of TEM inversion employs the “smoke ring” fast imaging method, which can only reflect the approximate morphology of the stratigraphic model, and the inversion accuracy is low. Therefore, this method cannot meet the requirements of high-precision inversion. In this article, we present the firefly algorithm (FA) technology for TEM inversion. First of all, the response of the rectangular loop source TEM based on electric dipole integration was calculated and compared with the analytical solution results of the rectangular loop source and the accuracy of the algorithm was verified. Then, a layered medium model was established. The FA technology and “smoke ring” fast imaging method were used to perform inversion calculation, and the influence of random noise on the accuracy of the FA algorithm was analyzed. The results show that the FA has a high degree of fitting to the model, good anti-noise property, and fast search speed. Next, to illustrate the application effect of the FA algorithm in pseudo 2-D inversion, the 2-D model was established. The results show that the FA algorithm can reflect the distribution of the anomalous body more accurately, especially for the low-resistance anomalous body. Finally, we examined the effectiveness of the FA for TEM data processing by inverting survey data and comparing the results with those from the “smoke ring” fast imaging and particle swarm optimization (PSO) algorithms. The research works provide new methods and techniques for TEM data processing.

Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes. Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance, hindering them from real-time tracking and practical applications. In this paper, we propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking. Specifically, a novel memory mechanism is introduced, which stores the historical information of the target to guide the tracker to focus on the most informative regions in the current frame. Furthermore, the pixel-level similarity computation of the memory network enables our tracker to generate much more accurate bounding boxes of the target. Extensive experiments and comparisons with many competitive trackers on challenging large-scale benchmarks, OTB-2015, TrackingNet, GOT-10k, LaSOT, UAV123, and VOT2018, show that, without bells and whistles, our tracker outperforms all previous state-of-the-art real-time methods while running at 37 FPS. The code is available at https: //github.com/fzh0917/STMTrack.

Transient electromagnetic (TEM) soundings are being increasingly used in engineering applications of environmental and regional surveys and shallow metal detection. Efficient and real time of the processing of the observed TEM data is the trend for the engineering geophysical prospecting and detection instrument in modern times. This letter presents a fast resistivity imaging method of TEM using artificial neural networks. The input-output mapping relations of neural networks are established based on the TEM response characteristics under different transmitter loop devices. The built network could map the recorded TEM data and quickly obtain the resistivity image. The proposed method offers accuracy and fast computation for resistivity imaging, and only 9.003 s costs for the calculation of 142 measured points' data. Feasibility and technical attractiveness of the proposed method in fast resistivity imaging of TEM mean that it is well suited for instantaneous of survey results to a client. The proposed TEM imaging method can be used in real time so that the recorded TEM data can be calculated without retraining, which avoids time-consuming iteration and inversion computation.