Deep Neural Networks (DNNs) are gradually becoming indispensable in various technological domains. To cater to more deployment backends and increasingly complex model architectures, deep learning compiler-driven efficient compilation modes are becoming essential components of productivity. However, this deployment method exacerbates security risks. Recent studies have shown that attackers can reverse-engineer executable files to regenerate trainable deep learning models, leading to adversarial attacks and other security breaches. Previous research indicates that such attacks pose significant threats, yet progress in implementing cost-effective mitigation strategies remains limited. Existing defense mechanisms primarily focus on Trusted Execution Environments or partial encryption to protect critical model parameters, often at the expense of compiled execution efficiency. To address this gap, we propose a schedule search based operator obfuscation method (SOOM) to defend against model extraction attacks for models compiled and executed on standard CPU and GPU backends, where low latency on device inference is required. SOOM is built on TVM, a deep learning compiler, and constructs a comprehensive obfuscation space for deep learning operators. It leverages a security aware learned cost model based on XGBoost gradient boosted trees to balance security objectives and performance requirements, and ultimately generates obfuscated executable code for various deep learning operators. Extensive experiments covered over 105 operator configurations and more than 30,000 tensor computation test cases. Our method was tested against state-of-the-art model extraction attacks, raising the operator inference failure rate to as high as 89%. We also observe up to approximately 25.4% performance gains in selected cases, while the balanced setting keeps model-level latency overhead within a modest budget.

Deep learning compilers such as TVM lower neural networks through intermediate representations (IRs) into optimized, hardware-specific binaries. While enabling high-performance deployment via optimizations like operator fusion and loop tiling, they leave stable execution signatures exploitable by reverse engineering. Prior attacks often rely on a single modality, symbolic lifting, instruction classification, or side channels, each struggles under at least one realistic condition, such as deep fusion, schedule diversity, or OS noise. We present SOFAN, a side-channel oriented fingerprinting and neutralization framework for TVM-compiled DNNs. On the attack side, TCScaptures timing and cache traces to recover operator boundaries via smoothing, non-maximum suppression (NMS), and dynamic time warping (DTW). A multimodal fusion network (MFN) then integrates these side-channel signals with instruction embeddings to classify deeply fused operators. On the defense side, LASR (Leakage-Aware Schedule Rewriting) selectively perturbs critical leakage via schedule diversification, access equalization, and memory remapping, under a fixed runtime budget. Evaluated across CNNs and fusion schedules, SOFANimproves segmentation and recognition over prior baselines. LASR reduces Top-1 attack accuracy by up to 24 points (16 on average) under 10-20% runtime overhead and minimal memory cost. By aligning both attack and defense with compiler boundaries, SOFANenables practical, budget-aware protection for real-world deployments.

Facial image tasks present distinct challenges in self-supervised learning (SSL) that are not encountered in general image classification, with existing backdoor attacks and defenses often fail to handle these specific issues. This paper introduces SymND, the first defense framework specifically designed to counter backdoor attacks in facial image SSL scenarios. SymND innovatively assesses noise stability across images and dynamically adjusts noise placement, capitalizing on the symmetrical properties of facial triggers—a departure from traditional SSL defenses that presume static trigger locations. Our method’s efficacy is underscored by experiments conducted on RAF-DB and UTKFace datasets, which show a significant reduction in attack success rates, plummeting from 99.58% to 0.15%, across a variety of downstream tasks employing different encoders.

Image encoder pre-training has experienced a substantial evolution due to contrastive learning, facilitating the extraction of intricate feature representations from unlabeled datasets. Nevertheless, the precise mitigation of the influence of specific data points, particularly in scenarios without labels, remains an inadequately explored issue within this field. This paper proposes CU-Encoder, the first approach aimed at selectively eliminating the impact of a designated ‘class’ from pre-trained encoders in contrastive learning. We also introduce a new evaluation framework that evaluates the unlearning effect, revealing how effectively the influence of the ‘class’ is removed and the model’s generalization capability is maintained. Comprehensive experiments conducted across different models and datasets highlight the effectiveness of CU-Encoder, confirming its capacity to achieve efficient unlearning while maintaining the model’s performance.

The growing use of multimodal contrastive learning in critical applications demands robustness against adversarial attacks. Although universal adversarial patches can broadly impact downstream tasks, their fixed perturbations are easily detectable and can be mitigated by simple defenses. To address this, we propose the sample-specific transferable adversarial patch (SSTAP), which generates adversarial patches tailored to individual inputs. By exploiting the unique features of each sample, SSTAP creates imperceptible patches that disrupt feature representations across diverse downstream tasks. Experiments on Wikipedia and Pascal-Sentence datasets show significant performance drops, demonstrating SSTAP's effectiveness.

As the structure of the underground space becomes increasingly complex, traditional 2-D seismoelectric methods are no longer adequate for comprehensive exploration. To achieve precise imaging of the underground space, there is an urgent need to develop 3-D full-waveform modeling techniques. In this article, we propose a 3-D time-domain finite-element method to solve the seismoelectric wavefield in saturated porous media. Since the electroosmotic feedback is very small, we can ignore the mechanical disturbance caused by the electromagnetic (EM) fields induced by seismic waves and, thereby, can decouple the electrokinetic coupling equations and separately solve the seismic and EM waves. For the simulation of seismic wavefield, we employ the explicit finite-element method and utilize a lumped mass matrix instead of a consistent mass matrix to facilitate explicit recursion. In addition, we apply the complex frequency-shifted unsplit perfectly matched layer technique to effectively handle seismic boundary conditions. Then, the velocity fields obtained by solving the poroelastic equations serve as the source term of the EM equations, and the finite-element method is used to solve the EM wavefield. Considering that the huge velocity difference exists between the EM and seismic waves, we adopt an unconditionally stable implicit method for the solution of the EM wavefield. By combining explicit and implicit recursion, the computational efficiency can be improved significantly. The accuracy of our time-domain finite-element algorithm is validated by checking our results against the analytical solutions for a half-space model. Furthermore, we conduct numerical simulations and analyses on a typical block model and a modified SEG/EAEG salt dome model.

We present an efficient method for uncertainty quantification in 3D magnetotelluric (MT) inversions based on variational inference principles and the variational autoencoder (VAE) framework. In this approach, we replace the VAE’s encoder and decoder respectively with a forward modeling and a 3D optimization module, where the latent variables represent updates in the constrained model space. Utilizing reparameterization, the gradient of objective function can propagate back to the mean and variance of the latent variables, and thus restores the Gaussian distribution of resistivity models. By setting appropriate prior and initial distributions, our method explores the solutions with high log-variance to fit the data. To ensure a stable convergence, we apply a smooth constraint on the log-variance of latent variables and employ a modified adaptive moment estimation (Adam) optimizer. Numerical experiments confirm that our method can accurately estimate both the resistivity model and standard deviation at a time cost only 2-3 times that of conventional inversion, which provides a highly efficient solution for uncertainty analysis. The tests with varying regularization parameters reveal that the standard deviation estimates are sensitive to both the Kullback-Leibler (KL) divergence and log-variance smoothness terms, while the adaptive smoothing strategy can well balance these effects. The application to a dataset from USArray survey in the Northwestern US demonstrates the method can achieve robust inversion and uncertainty quantification.

Magnetotelluric (MT) and teleseismic tomography are critical techniques in deep Earth exploration for geodynamic studies, internal energy circulation, plate tectonics, volcanic systems and so on. However, due to the uneven data distribution, the inherent non-uniqueness of inversion, and differing sensitivities to subsurface media, the velocity and resistivity structures derived from independent inversions often exhibit substantial discrepancies and contradict conventional geological understanding. To address these issues, we propose a novel three-dimensional (3D) joint inversion method for MT and teleseismic traveltime data, applicable to different types of grid discretization. The method first establishes a parameter mapping for different grid types. Then, a joint inversion framework based on the local Pearson correlation constraints (LPCC) is developed using a virtual grid technique. The inversion alternately updates the resistivity and velocity models by ensuring structural similarity between them. Numerical experiments demonstrate that the proposed method can effectively integrate the advantages of both techniques, enhance resolution and stability. Additionally, the tests on hyperparameter selection provides guidance for optimizing joint inversion parameters. Finally, the developed joint inversion method is applied to MT and teleseismic traveltime data in southeastern Australia and yields a constrained 3D resistivity and velocity model of the region. The results offer significant theoretical and practical insights into the lithospheric structure, the magma transport pathways, and the geodynamic processes in this area.

In this article, to explore the efficiency and precision of the 3-D finite volume method (FVM) for the logging while drilling (LWD) ultradeep resistivity measurements, we compared four different schemes: field-based total-field FVM, coupled potentials total-field FVM, field-based secondary-field FVM, and coupled potentials secondary-field FVM. The fast and accurate discretization of scattered current density in the secondary-field method is another issue we focus on. On the one hand, we improve the discretization accuracy of the scattered current density near the source by extracting the direct waves in the background electric field. On the other hand, based on the dyadic Green’s functions (DGFs) of vector potentials, the number of Sommerfeld integrals in the background electric field is reduced as much as possible through the background field library and interpolation. The numerical results show that the accuracy and stability of the secondary-field method are better than those of the total-field method and the efficiency of the background electric field is greatly improved through the library and interpolation. Based on the premises of the LWD ultradeep resistivity measurements and the direct solver, the accuracy of the field-based and coupled potentials methods is almost the same, however, the field-based method is much more efficient. Overall, we believe that the field-based secondary-field FVM and the direct solver constitute a more efficient modeling scheme with high precision for LWD ultradeep resistivity measurements.

The conventional magnetotelluric (MT) inversions rely primarily on L2norm to quantify the misfit between the observed data and the predicted ones. However, it often suffers from issues of unreasonable weighting of inversion data, sensitivity to outliers, and poor resolution to weak anomalies. To address these limitations and achieve better data fitting across varying scales, we propose to replace the L2norm with the Wasserstein distance to measure the distributional discrepancy between the predicted and observed data. This method adopts the Wasserstein Generative Adversarial Network (WGAN) framework and utilizes the Kolmogorov-Arnold Network (KAN) as the discriminator to implement the Wasserstein metric. By substituting the neural generator in WGAN with a physics-based forward modeling, we achieve a purely physics-driven iterative process that is similar to the conventional MT inversion workflows to ensure an adequate data fitting. Numerical experiments on synthetic models demonstrate that MTGAN-KAN can better fit data distributions and yield higher-resolution inversion results than the conventional methods. Compared to WGAN that employs a fully connected neural network (FCNN) as the discriminator, the incorporation of KAN can significantly improve the convergence and stability of MTGAN. An inversion test using MT data from Newer Volcanic Province in Australia further highlights the superior characteristics of data fitting and resolution of MTGAN-KAN in the inversion of MT data.

Induced polarization (IP) effects can distort late-time transient electromagnetic (TEM) signals, sometimes even leading to their sign reversal. These distortions are closely linked to subsurface IP effects. However, the significantly different sensitivities of the IP parameters often result in strong non-uniqueness in TEM data inversion. Here we present a three-dimensional (3-D) TEM-IP inversion method that incorporates local Pearson correlation constraints (LPCC). By establishing LPCC between the resistivity and the IP parameters, we achieve a joint inversion framework that stabilizes the estimation of these parameters. Numerical experiments on synthetic models demonstrate that, compared with unconstrained inversion, our method yields results that can more accurately recover the true distributions of chargeability, time constant, and frequency-dependent coefficient. We further investigate the impact of the size of correlation window in LPCC domain, and achieve practical guidance for optimal constraint selection. Finally, we apply the proposed method to a TEM dataset collected from a bauxite exploration site in Guangxi Province, Southern China, to validate our method for real-world geological settings.

As a classic semi-supervised approach, the Transductive Support Vector Machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains a subject of investigation, prompting concerns about its reliability in security-critical applications. To unveil the vulnerability of TSVM, we introduce a finite-attack model specifically tailored to its characteristics, effectively manipulating its outputs. Additionally, we present Adversarial Defense-based TSVM (AD-TSVM), the first dedicated defense scheme designed for TSVM. AD-TSVM incorporates adversarial information into the optimization process, enhancing robustness by rebuilding a customized loss function and decision margin to counteract attacks. Rigorous experiments conducted on benchmark datasets demonstrate the effectiveness of AD-TSVM in significantly improving both the accuracy and stability of TSVM when confronted with adversarial attacks. This pioneering research assesses the weaknesses of TSVM and, more importantly, offers valuable insights and solutions for developing secure and trustworthy TSVM systems in the face of emerging threats.

Semi-airborne transient electromagnetic (EM) method is an effective exploration tool in complex environment due to its utilization of unmanned aerial platforms for data collection. With the escalating demands of detection accuracy, the conventional one-dimensional (1D) inversion is no longer sufficient. Over the last two decades, there has been rapid development in three-dimensional (3D) EM inversions. For semi-airborne transient EM method, it usually has a substantial number of receiver stations. To obtain accurate 3D solutions, one has to refine the grids near the receiver points, which causes huge number of grids and reduces computational efficiency. Thus, the computational complexity is one of primary factors restricting the practicality of 3D inversions. In this paper, we have developed an approach using decoupled meshes. This method uses a serial 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.

At present, the mainstream technology for leachate detection in landfills is electrical resistivity tomography (ERT), known for its efficiency and nondestructive nature. However, the conventional ERT data interpretation primarily uses inversion based on structured grids, which cannot accurately simulate the complex and thin impermeable layers of landfills, leading to unreliable results. To address this issue, we propose a novel ERT observation system and a new 3-D inversion technology. In our observation system, all measuring electrodes are placed around the landfill at once, and only a limited number of transmitting sources are needed to sequentially inject current, which effectively reduces the time for data acquisition. For 3-D inversions, we employ an unstructured tetrahedral grid for fine discretization of structures at various scales. The node-based finite-element method is used for high-precision forward and adjoint forward calculations, while the gradient filtering method in combination with limited-memory quasi-Newtonian (L-BFGS) algorithm is used to update the inversion model. Numerical experiments indicate that the proposed method can mitigate the influence of thin impermeable landfill layers and provide more accurate imaging results compared to the conventional methods. In addition, we also test the effects of water-bearing layers, faults, and near-surface interferences on the leakage inversion results. The results show that the proposed method can achieve reliable high-resolution imaging under various conditions, making it an effective technique for landfill leachate detection.

The choice of data misfit measure has a great impact on the convergence of electromagnetic inversion. The conventional measure based onl2-norm tends to excessively amplify the weights of a larger misfit, inadvertently neglecting data with a smaller misfit during the inversion process, thereby diminishing the resolution to a certain degree. To solve this problem, we propose a robust inversion strategy based onl1-norm data misfit and adaptive moment estimation (Adam). In this scheme, we use the Ekblom-typel1-norm to simplify the derivative computation of the absolute value function. The Adam algorithm is further applied to optimize this type of non-smooth objective function, which incorporates momentum terms and adaptive steps, allowing it to better adapt to the irregularities in gradient changes. The inversion results obtained from both synthetic models and field measurements demonstrate that the Adam method performs considerably better than the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method for optimizing thel1-l2norm of objective function. Compared with the conventionall2-norm data misfit, thel1-norm data misfit can effectively avoid excessive optimization of data with large misfits and achieve high-resolution inversion results.

Airborne electromagnetic (AEM) technology is an efficient geophysical exploration tool for investigating subsurface electrical structures. In recent years, 3-D inversion of AEM data has been developed rapidly, but it still faces challenges such as low resolution and computational efficiency. To solve these problems, we propose a multiscale shearlet-based regularization inversion algorithm by establishing the relationship between spatial resolution and shearlet coefficients in the inversion process. In the initial stage of inversion, the coarse grids and sparse measurement points data are used to recover the main subsurface structure. When the data misfit reaches a certain level, the previous results are used as the coarse scale model in the shearlet domain to recover the model with fine grids and dense measurements. By building this coarse-to-fine inversion scheme, we can well utilize the multiscale information in AEM data and effectively achieve high-resolution inversions. We demonstrate the effectiveness and practicality of our 3-D MS inversion algorithm using two synthetic examples and a field dataset from Norway. The numerical experiments show that our inversion method can effectively reduce the computational time and improve inversion resolution.

Satellite magnetic data contain significant information about the Earth’s interior electrical structure. However, the altitudes of satellites vary over time and latitude. Theoretically, the signals from the external magnetosphere and ionosphere, along with the induced magnetic field from the Earth, exhibit considerable variation at different altitudes. The specific effects of altitude variations on the global electromagnetic induction signal, and their subsequent impact on the transfer function, are not yet fully understood. Given that the orbital altitude of the Challenging Minisatellite Payload (CHAMP) satellite changes continuously, we have calculated the Q-response and the C-response at different altitudes through numerical simulations and CHAMP satellite data, respectively. The Q-response represents the ratio of coefficients of the internal and external fields, while the C-response, derived from the Q-response, is altitude-dependent. The findings indicate that the amplitudes of the induced (internal) magnetic field decrease with increasing altitude, with discrepancies exceeding 10 nT, particularly in mid-latitude regions. Conversely, the inducing (external) fields increase with altitude. The Q-responses exhibit distinct behaviors across different degrees of Gaussian spherical harmonics (SHs). Specifically, the real part of the orbital C-response increases with satellite altitude, although higher degrees tend to yield lower magnitudes. This study offers insights into the design of satellite orbits in magnetic field measurements.

The conventional geo-electromagnetic data inversions are mostly based on gradient optimization methods. However, this type of method can only provide a single “optimal” inverse model under specific prior conditions, which cannot effectively evaluate the reliability and uncertainty of the inversion results. The widely used uncertainty quantification (UQ) methods are based on the theory of Bayesian inference. Although they have achieved success in many applications, they suffer from the curse of dimensionality and low efficiency. To overcome these problems, we propose a novel UQ strategy for geo-electromagnetic inversions based on Bayesian processes and surrogate modeling with adaptive deep neural network (DNN). In this method, an embedded DNN is used for forward modeling in the Bayesian inference to improve computational efficiency. The training of the DNN is divided into two stages. First, a predesigned small training set is used and the resulting DNN only gives a low-accuracy result. Second, this DNN is fine-tuned dynamically during the Metropolis-Hastings (M-H) sampling process, in which the training set is adaptively supplemented according to the modeling errors. Compared to the conventional data-driven approach, this dynamically adaptive constructing method of the training set can greatly reduce the training set and constantly maintain high accuracy in forward modeling. We demonstrate the effectiveness and practicality of our surrogate modeling Bayesian and analyze the effects of different sampling numbers, noise levels, prior distributions, and sampling radius. Compared with Occam’s inversion and conventional Bayesian inversions, our method shows good robustness and high accuracy, making it an effective Bayesian inversion technique.

The article proposes an efficient global optimization of adaptive boundary detection from the logging while drilling (LWD) azimuthal electromagnetic (EM) measurements. The goal is to realize real-time geo-steering in 1-D layered transversely isotropic (TI) formation with an arbitrarily deviated borehole. The method includes apparent resistivity (APR) extraction and 0-D inversion, 1-D adaptive regularized iterative inversion, and global optimization. The APR extraction and 0-D inversion are used to quickly determine the initial horizontal and vertical resistivities of the bed where the tool is located. Subsequently, the 1-D regularized inversion is performed to achieve a local minimum solution near an arbitrarily given initial model. For solving the non-unique problem and acquiring the globally optimal solution, several different initial values are selected according to the possible range per model parameter to construct a serial of initial models. The OpenMP parallel technique is applied for simultaneous inversions at all initial models. Multiple inversion solutions may be obtained due to the non-uniqueness. The one with the minimal residual function in all inversion results will become the globally optimal solution. Furthermore, the tool response and its exact explicit Fréchet derivative with respect to each model parameter are analytically calculated, while an adaptive regularization factor ensures a gradual reduction of the objective function. The correction of field data is required to reduce the mandrel effect. The inversion results of synthetic and field data demonstrated that the proposed inversion algorithm efficiently provides the reliable bed boundaries and resistivities around the wellbore.

Previous works have demonstrated that inverting magnetotelluric (MT) data jointly with gravity data can synergize the high lateral resolution of gravity and the vertical resolution of MT. However, the existing joint stabilizers usually work for structured grids instead of unstructured ones that are more powerful for characterizing complex geology. Here, we utilize the local Pearson correlation coefficient (LPCC) for the joint inversion of gravity and MT data based on unstructured grids. We first establish a background mesh by discretizing the research area into virtual rectangular grids and then enhance the structured similarity between density and resistivity via the LPCC. Compared to existing joint constraints, our method is more flexible in solving multiscale joint inversions thanks to the adjustable subdomain size. The synthetic experiments show that the joint stabilizer can recover the subsurface targets at a higher resolution, especially for gravity data, than the standalone inversions. This method is further applied to the joint inversion of gravity and MT data from the Yellowstone area and the inverted density and resistivity models are structurally consistent. Then, based on the inverted subsurface structures and incorporating the existing research, we infer that the two inverted zones with low density and low resistivity correspond to the partially molten rhyolitic and basaltic, respectively. The proposed joint constraint can be further extended to the inversion of other geophysical data.

The Bayesian inversion of electromagnetic data can obtain key information on the uncertainty of subsurface resistivity. However, due to its high computational cost, Bayesian inversion is largely limited to 1-D resistivity models. In this study, a fast Bayesian inversion method is implemented by introducing the spatial correlation as prior information. The contributions of this article mainly include: 1) explicitly introduce the expression of spatial correlation prior information and provide a method to determine the parameters in the expression through the variogram theory. The influence of parameters in the spatial correlation prior information on the inversion results is systematically analyzed with the 1-D synthetic model. 2) The information entropy theory of continuous functions is introduced to quantify the degrees of freedom (DOF) of the parameters of the spatial correlation prior model. The analysis shows that the DOF of model parameters are significantly smaller than the number of model parameters when spatial correlation prior information is introduced, which is the main reason for the rapid Bayesian inversion. 3) Introducing the Sengpiel fast imaging algorithm, combined with the variogram theory, realized the direct acquisition of spatial correlation prior information from the observation data, minimizing the dependence on other information. The inversion results of 1-D and 2-D synthetic models and field datasets show that considering the spatial correlation prior information, hundreds of thousands of Markov chain Monte Carlo sampling steps are needed to enable the inversion of up to thousands of model parameters. This result provides a possible idea for future Bayesian inversion of complex 3-D models.

Improving the transferability of adversarial examples for the purpose of attacking unknown black-box models has been intensively studied. In particular, feature-level transfer-based attacks, which destroy the intermediate feature outputs of source models, are proven to generate more transferable adversarial examples. However, existing state-of-the-art feature-level attacks only destroy a single intermediate layer, this severely limits the transferability of adversarial examples. And all of these attacks have a vague distinction between positive and negative features. By contrast, we propose the Multi-layer Feature Division Attack (MFDA), which aggregates multi-layer feature information on the basis of feature division to attack. Extensive experimental evaluation demonstrates that MFDA can significantly boost the adversarial transferability and quantitatively distinguish the effects of positive and negative features on transferability. Compared to the state-of-the-art feature-level attacks, our improvement methods with MFDA increase the average success rate by 2.8% against normally trained models and 3.0% against adversarially trained models.

In a heterogeneous medium (usually called a rough medium) with fractured formations, the propagation of an electromagnetic (EM) field is a type of subdiffusion. Current mainstream geophysical EM data processing methods cannot be applied to data acquired on heterogeneous Earth, as they are not governed by the classic diffusion theory. To evaluate the influence of roughness on the transient EM (TEM) signal for a complex model and contribute to data inversion, we proposed a novel three-dimensional (3-D) forward modeling scheme for TEM in rough media. First, we derived the governing equation with a fractional-order time derivative for the subdiffusion of EM waves in rough media. Then, we proposed a novel time discretization using an unequal step length for the Caputo operator, which significantly reduces the total number of time steps. Finally, an implicit time-domain finite-element method using unstructured tetrahedron discretization was adopted to solve the 3-D forward problem. Furthermore, an efficient time segmentation strategy combined with parallel RHS construction was proposed to accelerate modeling. The numerical results prove that the proposed method is accurate and efficient, and will be a powerful numerical method for analyzing TEM wave propagation and processing TEM data in areas with multiscale fractures or porosity.

The conventional magnetotelluric (MT) data inversion methods, such as the nonlinear conjugate gradient method, quasi-Newton method, and Gauss–Newton method and so on, can converge robustly, but their results are easily affected by the initial model and regularization term. Although supervised learning can break through the resolution limitation by directly learning the nonlinear relationship between the model and the data, it cannot guarantee the data fitting without considering physical constraints. Here, we propose a novel prior model generation method using deep learning for conventional inversion to jointly take advantages of the two techniques. We first combine Gaussian random rough surface scheme and random polygon generation algorithm to construct practical 2-D geoelectric models, in which the prior information on the geoelectric structure can be flexibly integrated. Then, a fast 2-D MT forward modeling method is applied to calculate the forward responses and establish the training set. Finally, we use the training set to complete the parameter optimization of U-shaped network (U-NET) and run the conventional inversion with prior model generated by the trained U-NET. Numerical experiments with synthetic data show that the proposed method can effectively integrate the advantages of conventional inversion and supervised learning, and remarkably improve the resolution in the inversion if proper training sets are used. The inversions of the USArray data also prove that our method can retain high-resolution structures predicted by the U-NET in the final inversion results with a data fitting as good as the traditional Gauss–Newton method.

The flight altitude has a large effect on the airborne electromagnetic (AEM) responses. Due to the dynamic environment of the aircraft, the recorded sensor altitudes may contain errors. Research demonstrates that the AEM responses caused by a several meters altitude errors can be larger than caused by some anomalous body. Ignoring these errors will create erroneous results in AEM data interpretation. Considering that there is not yet a published 3D AEM inversion method that takes into account the flight altitude, we develop in this paper a 3D inversion algorithm for AEM with the flight height treated as an inversion parameter. For the forward modeling we use the finite element method, while for the inversion we use the Gauss-Newton optimization method. To make our inversion works for variable flight altitudes, we propose a scheme of 3D Jacobean matrix calculation for both the resistivities and flight altitudes without much increasing the computational cost. The numerical simulation result confirms that the flight altitude really has a large effect on the AEM responses. The inversions of synthetic data show that our 3D inversion method can both recover the resistivity distribution in the underground and decrease the altitude errors recorded, while the field data inversion demonstrates that our method can deliver a better inversion model with a smaller data misfit.

A new approximation algorithm is developed to compute the electromagnetic (EM) tensor Green’s function in the biaxial anisotropic media based on the Fourier series expansion. First, a rectangular region is chosen as the computation region, in which the EM field can be expressed as a 2-D Fourier series. The Fourier coefficients can be regarded as the EM field in discrete wavenumber domain. We further obtain the EM solution in the spatial domain in the form of Fourier series. Then, we use the finite terms of the Fourier series to approximate the EM field for enhancement of computation efficiency. Because the new method avoids the numerical integration in the infinite wavenumber domain, it is more convenient to implement than other methods based on integral transform. Finally, the spatial distribution of the tensor Green’s function for a transversely isotropic medium is presented to verify the proposed approach. The agreements between the results obtained by the present method and the analytic solution demonstrate the validity and the robustness of our algorithm.

We presented a semi-analytic solution for the sensitivity of the response tensor of the multi-component induction logging (MCIL) tool in a homogeneous biaxial anisotropic (BA) medium. By using the perturbation principle and the spectral representation of electromagnetic (EM) fields, the sensitivity was formulated as a seven-fold integral whose integrand involves two sets of spectral electric fields. Fortunately, such a complicated integral can be reduced to a three-fold integral, which consists of two parts: one is a definite integral over the spatial variablezthat can be analytically integrated; the remaining is a two-fold integral over the spectral variable k that is computed by numerical quadrature. We verified our formulation by comparing it with the exact solution and the finite-difference (FD) approximation.

$Z$-axis tipper electromagnetic (ZTEM) technique is an airborne electromagnetic method that detects anomalies in the deep earth that are induced by natural sources. Conventional ZTEM forward modeling is generally conducted using structured grids that have limited accuracy and cannot be used to invert complex underground structures and topography. However, unstructured grids can accurately model complex underground structures with fewer cells. Therefore, to effectively model and recover the complex underground structures, we developed a 3-D framework with unstructured tetrahedral grids for ZTEM forward modeling and inversions. The forward problem was formulated using the finite-element method with unstructured tetrahedral grids. To solve the inverse problem, we used a limited-memory quasi-Newton algorithm (L-BFGS) with a parallel direct solver for optimization in order to avoid the explicit calculation of the Hessian matrix and save the memory and computational time. To validate our forward and inversion algorithms, we conducted numerical experiments on three synthetic models and inverted a survey dataset acquired for a tunnel project in Tibet, China. The experimental results demonstrate the effectiveness of our unstructured finite-element and L-BFGS method for modeling and inverting ZTEM data.

The 3-D airborne electromagnetic (AEM) inversions have been restricted by the modeling efficiency resulting from the complex geology in exploration areas and massive amount of data collected by AEM systems. In order to improve the modeling efficiency, we develop an algorithm that combines the hexahedral vector finite element (FE) with octree meshes, in which the boundary conditions are imposed via an algebraic constraint to ensure the continuity of the FE solution. This makes the division with hexahedral meshes more flexible for complex geology such as rugged topography or underground structures so that we can reduce the number of elements while maintaining the accuracy. After formulating the forward problem, we check the accuracy of our algorithm by taking a homogeneous half-space model and comparing the results of our octree method with the semianalytical solutions. Furthermore, we demonstrate the efficiency of our octree method by comparing with the traditional FE method using tetrahedral meshes. Finally, we subdivide a complex topography constructed using the 2-D Gaussian rough surface and calculate the EM responses with and without anomaly embedded. The results show that the EM responses are overwhelmed by the Earth topography. We carry out the topographic correction by taking a method based on the ratio of EM responses with and without anomaly. The experiments show that after topographic correction to AEM data, the response of anomaly becomes more distinguishable so that the anomaly can be clearly identified. Furthermore, we also calculate the EM response for a realistic model—the Ovoid Zone ore body located at Voisey’s Bay, Labrador, Canada, to verify the flexibility and practicality of our algorithm.

Based on the spatial structure correlation in different geophysical parameters, we propose a new 3-D joint inversion method for frequency-domain airborne electromagnetic (AEM) and airborne magnetic (AirMag) data by incorporating a local Pearson correlation constraint (LPCC). For each iteration, the entire model is separated into multiple subdomains and the Pearson correlation coefficients of resistivity and magnetization in the subdomain are employed as the additional regularization term to do the joint constraint. This new regularization term is continuously updated in the inversion process to ensure that the resistivity and magnetization models in two separated inversions converge to a similar spatial structure. As a statistics technology, the LPCC-based joint inversion scheme not only has the advantages of the conventional joint inversions, but also can implement the structural constraints in different scales by selecting different sizes of the subdomain. This provides the flexibility for solving multiscale problems. Synthetic examples show that the joint inversion can improve the overall inversion resolution by combining the high vertical resolution of the EM method and large exploration depth and high horizontal resolution of the magnetic method. In the application to field survey datasets, the joint inversion delivers better results than those of separate inversions, which further verifies the effectiveness of our method.

The topography has serious effects on time-domain airborne electromagnetic (AEM) signal, and the EM responses resulted from the topography frequently overwhelm those from the underground abnormal bodies. This brings big challenges to the traditional AEM interpretations based on a flat ground model. In this article, we develop a 3-D AEM inversion algorithm for a topographic earth model. The time-domain finite-element algorithm based on unstructured mesh is used to model the AEM responses. The tetrahedral grids provide the flexibility to fit the rugged topography. Furthermore, we adopt the Gauss–Newton method for our inversion of time-domain AEM data. In the forward modeling and the calculation of Jacobian matrix, we introduce an unstructured local mesh and decouple the meshes for forward modeling and inversion to improve the computational efficiency. For that purpose, we first set up an unstructured inversion mesh and then extract those cells corresponding to the sensitive area of AEM system for each survey station from the inversion mesh and construct a local forward mesh with the extracted cells as the core. After that, we take advantage of the spatial relationship between the local forward meshes and the inversion ones to set up the global sensitivity matrix for Gauss–Newton inversion. We test the effectiveness of our algorithm by applying our 3-D inversion code to both synthetic and survey data. The numerical experiments show that the Earth topography can have big influence on AEM inversions, and ignoring the topography can create serious distortion to AEM inversion results.

The conventional, L2-norm-based, regularization term in electromagnetic (EM) inversions implements smooth constraints on model complexity in the space domain, which can smoothen the boundaries of complex underground structures. To improve the resolution of 3-D frequency-domain airborne EM (AEM) inversions, we propose a new algorithm for sparse-regularized inversion based on the shearlet transform. Unlike traditional methods that invert the model parameters in the space domain, we first transform the 3-D resistivity model into the frequency domain and then invert the sparse coefficients using an L1-norm measure to ensure the sparseness of the solution. Finally, we transform the shearlet coefficients back to the space domain to update the model. The shearlet transform has inherent multiscale and multidirectional properties, making it capable of effectively extracting complex geometries such as curved boundaries. We adopt the finite-difference method and the iteratively reweighted least-squares scheme for our 3-D AEM modeling and inversions and apply the “moving footprint” technique to speed up the inversion. Tests using synthetic data show that sparse-regularized inversion based on the shearlet transform can obtain more-focused inversion results than conventional smoothness-constrained inversions based on the L2-norm. Tests using field survey data also reveal that the new method can achieve more realistic underground structures.

As an efficient geophysical exploration method, the time-domain airborne electromagnetic (AEM) data often show sign reversal in late-time channels due to induced polarization (IP) effect. The traditional imaging and inversion methods without considering the IP effect cannot recover the true electrical structure of the earth, so it is necessary to develop 3D EM forward modeling and inversion techniques with IP effect. In this paper, we propose a 3D forward modeling method for time-domain AEM with IP effect based on unstructured finite-element (FE) method. To describe the IP effect of a medium, we introduce the Cole-Cole model and transform it into fractional derivative form using frequency-time conversion. Then, we discretize it using the difference discretization format of Caputo fractional derivative. Finally, we use the vector FE method based on unstructured tetrahedral mesh and unconditionally stable second-order backward Euler’s scheme to discretize Maxwell’s equations in space and time. In this way, we can solve the 3D forward modeling problem for AEM with IP effect in time domain. We verify the accuracy of our algorithm by comparing it with the 1D semi-analytical solution for a half-space model, and then calculate EM responses for typical abnormal models and analyze the characteristics of IP effect.

In this article, to solve the frequency-domain airborne electromagnetic (EM) modeling problem, we express the electric and magnetic field by the vector magnetic potential and the scalar electric potential and derive the governing equation using the Galerkin weighted residual method under the Coulomb gauge. To avoid the strong singularity of the solution near the transmitting source, we directly solve the relatively slow-changing secondary potential. By introducing the spectral element method (SEM) based on unstructured tetrahedral grids, in which the EM field in each element is characterized by high-order Proriol–Koornwinder–Dubiner (PKD) orthogonal polynomials, we can obtain stable and accurate numerical solutions. By establishing the mapping relationship between the physical domain, the right-angled tetrahedral reference domain, and the orthogonal hexahedral domain, we can easily accomplish the element matrix analysis and calculation. The numerical examples show that, when the SEM is used to simulate the airborne EM response, a high accuracy can be obtained even if a very coarse grid is used. Furthermore, since the tetrahedral grids can easily model the complex boundaries, the SEM based on unstructured grids has significant advantages for simulating complex underground structures.

Botmasters generate a large number of malicious algorithmically generated domains (mAGDs) through domain generation algorithms (DGAs) to infect a large number of hosts on a network, which creates inconvenience in people's network lives. The workload of detecting mAGDs by collecting the responses of the domain name system (DNS) is considerable. In this article, we propose a system named the automatic NXDomain classification system (ANCS) that can automatically identify and classify the nonexistent domain (NXD) as benign or malicious by studying the features extracted from benign NXDs (bNXDs) and mAGDs. The ANCS uses online, incremental, and fuzzy rough sets machine learning to improve the time, memory, false positive rate, false negative rate, and accuracy of the detection process. First, an online and incremental algorithm can reduce the training time. Second, the addition of fuzzy rough sets can dynamically adjust the degree of the membership function, optimizing the weight distribution of each feature, and further, improving the classification accuracy. The experimental evaluation shows that the ANCS can reach a very high classification accuracy at a low false positive rate and a low false negative rate, which has good practicability. Moreover, both time and memory are well guaranteed, and the ANCS also has good generalization performance, making up for sensitive points of noisy samples and the lack of nonincremental machine learning.

The application of the Internet of Things (IoT) in medical field has brought unprecedented convenience to human beings. However, attackers can use device configuration vulnerabilities to hijack devices, control services, steal medical data, or make devices operate illegally. These restrictions have led to huge security risks for IoT, and have challenged the management of critical infrastructure services. Based on these problems, this article proposes an anomaly detection system for detecting illegal behavior (DIB) in medical IoT environment.The DIB system can analyze data packets transmitted by medical IoT devices, learn operation rules by itself, and remind management personnel that the device is in an abnormal operation state to ensure the safety of control service. We further propose a model that is based on rough set theory and fuzzy core vector machine (FCVM) to improve the accuracy of DIB classification anomalies. Experimental results show that the R-FCVM is effective.

In the intelligent manufacturing process of solar photovoltaic (PV) cells, the automatic defect detection system using the Industrial Internet of Things (IIoT) smart cameras and sensors cooperated in IIoT has become a promising solution. Many works have been devoted to defect detection of PV cells in a data-driven way. However, because of the subjectivity and fuzziness of human annotation, the data contains a high quantity of noise and unpredictable uncertainties, which creates great difficulties in automatic defect detection. To address this problem, we propose a novel architecture named fuzzy convolution, which integrates fuzzy logic and convolution operations at microscopic level. Combining the proposed fuzzy convolution with the regular convolution, we build a network called Hybrid Fuzzy Convolutional Neural Network (HFCNN). Compared with convolutional neural networks (CNNs), HFCNN can address the uncertainties of PV cell data to improve the accuracy with fewer parameters, making it possible to apply our method in smart cameras. Experimental results on a public dataset show the superiority of our proposed method compared with CNNs.

Over the years, public health has faced a large number of challenges like COVID-19. Medical cloud computing is a promising method since it can make healthcare costs lower. The computation of health data is outsourced to the cloud server. If the encrypted medical data is not decrypted, it is difficult to search for those data. Many researchers have worked on searchable encryption schemes that allow executing searches on encrypted data. However, many existing works support single-keyword search. In this article, we propose a patient-centered fine-grained attribute-based encryption scheme with multi-keyword search (CP-ABEMKS) for medical cloud computing. First, we leverage the ciphertext-policy attribute-based technique to construct trapdoors. Then, we give a security analysis. Besides, we provide a performance evaluation, and the experiments demonstrate the efficiency and practicality of the proposed CP-ABEMKS.

In virtue of advances in smart networks and the cloud computing paradigm, smart healthcare is transforming. However, there are still challenges, such as storing sensitive data in untrusted and controlled infrastructure and ensuring the secure transmission of medical data, among others. The rapid development of watermarking provides opportunities for smart healthcare. In this article, we propose a new data-sharing framework and a data access control mechanism. The applications are submitted by the doctors, and the data is processed in the medical data center of the hospital, stored in semi-trusted servers to support the selective sharing of electronic medical records from different medical institutions between different doctors. Our approach ensures that privacy concerns are taken into account when processing requests for access to patients’ medical information. For accountability, after data is modified or leaked, both patients and doctors must add digital watermarks associated with their identification when uploading data. Extensive analytical and experimental results are presented that show the security and efficiency of our proposed scheme.