Search

Semi-airborne transient electromagnetic (SATEM) method is an effective exploration tool in complex environments due to its utilization of unmanned aerial platforms for data collection. With the escalating demands of detection accuracy, the conventional 1-D inversion is no longer sufficient. Over the last two decades, there has been rapid development in 3-D electromagnetic (EM) inversions. SATEM method usually has a substantial number of receiver stations. To obtain accurate 3-D solutions, one has to refine the grids near the receiver points, which causes a huge number of grids and reduces computational efficiency. Thus, computational complexity is one of the primary factors restricting the practicality of 3-D inversions. In this article, we have developed an approach using decoupled meshes. This method uses a series of meshes to parallelly calculate the forward modeling and Jacobian information, with one mesh containing only a subset of receiver points. This scheme aims to decrease the number of grids and improve the efficiency. After that, we map the results to an overarching inversion mesh using the node cloud technique and renew the model parameters. We check our algorithm via both synthetic and field data. Numerical experiments show that the decoupled mesh inversion method can effectively recover the location and resistivities of the anomalies under the complex topography. When compared with the conventional inversion method, the decoupled mesh inversion method can significantly reduce the calculation time.

In geophysical exploration methods, the semi-airborne transient electromagnetic (SATEM) technique offers substantial exploration depth and the capability to acquire precise underground characteristics. However, the SATEM signal received by the induction coil has a low amplitude in the middle and late stages and is susceptible to various noise effects. Although a variety of conventional denoising methods are available today, their performance is often unsatisfactory, necessitating manual adjustments of the denoising outcomes. With the emergence of deep learning in various fields, several SATEM denoising neural network models have been proposed. However, these models are discriminative models and focus on modeling conditional probabilities, with insufficient generalization capabilities when faced with small datasets. On the other hand, the variational diffusion model (VDM) is a generative model that captures the joint distribution and exhibits excellent generalization capabilities. Building upon this premise, we explored a VDM-based denoising approach for SATEM. Nevertheless, due to the uncontrollable nature of the results generated by VDM, it is not possible to use VDM directly for denoising SATEM signals. To address this issue, we introduce the VDM-based denoiser with constraints, which incorporates guiding conditions to constrain the model and generate the desired denoising results, and propose a new SATEM signal denoising network called TEMSGnet. To further enhance the practical applicability of our method in real-world scenarios, we incorporate a wavelet transform-based supervised fine-tuning strategy. The experimental results demonstrate that TEMSGnet achieves effective denoising performance on both synthetic and real datasets. Furthermore, through inversion, TEMSGnet can more accurately approximate the true subsurface characteristics, thereby effectively reflecting the denoised outcomes. The code is available at https://github.com/woldier/TEMSGnet.

The stark contrast in density between geological layers is a fundamental aspect in the examination of basic geological structures. The delineation between the crystalline basement and sedimentary layers, moreover, is pivotal in the pursuit of strategic energy resources, such as petroleum and natural gas. Traditional full space density inversion, however, is beleaguered by issues of stability and resolution, impeding the accurate characterization of the sharp density interface. To rectify these shortcomings, we introduce an innovative methodology for estimating 2-D depth-to-basement and overlying density distribution, employing a deep neural network with a leaky rectified linear unit as an activation function. Evaluation of the proposed method on simulated sedimentary basin models underscores its superior ability to discern complex geometries of basin boundaries and overlying density, despite the presence of various degrees of Gaussian noise. In practical application to the Poyang basin, the relief of the Cretaceous basement is proficiently recovered through vertical gravity field data, with validation provided by corresponding seismic sections and well-established stratigraphic markers.