Abstract Apparent resistivity is a significant tool in the controlled-source electromagnetic method (CSEM). The Cagniard apparent resistivity, which is defined by the ratio of the magnetic and electric fields, has been widely used in CSEM interpretation. However, it is only suitable for the far zone, where the signals are weak. Although another apparent resistivity based on horizontal electric field Ex can work in transition zone, its measurable zone is narrow. To better take the advantage of strong signals in non-far zone, we present a novel apparent resistivity by sophisticatedly combining the horizontal electric field and vertical magnetic field. We analyze the asymptotic characteristic of our apparent resistivity in the near and far zones using a theoretical formula. Synthetic data from the layered model show that when in the far zone, our apparent resistivity obtains the same performance as the Cagniard resistivity and Ex resistivity. Besides, our apparent resistivity can still wok well when azimuth approaches 45° in the tranzition zone. Field data collected in Tongling, China further demonstrate the practicability and advantages of our apparent resistivity.
Radar backscattering from human objects is subject to micro-Doppler modulations because of their flexible body articulations and complicated movement patterns, which can help identify the interested targets and provide valuable information about their motion dynamics. In this paper, a novel theoretical method to extract target micro-Doppler trajectories from continuous-wave radar echo is proposed with a united application of a modified high-order ambiguity function and an adaptive denoising technology. Through this method, multiple components corresponding to different target scattering parts and their micro-Doppler trajectories can be accurately extracted and estimated even in a time-varying low signal-to-noise ratio environment. Finally, a series of simulations is conducted to illustrate the validity and performance of the proposed techniques.
Summary form only given. A space-time array difference magnetotelluric method is proposed in order to suppress the correlated noise and obtain reliable plane wave impedance data in strong interference region. First, we established a model of electromagnetic prospecting system with multi-input and multi-output. Both natural and man-made electromagnetic field is considered to incidence to earth simultaneously, constitute a space-time array input. Many stations are carried out to simultaneously observe the total responses of all the inputs on the earth's surface, constitute a space-time array output. A set of quadratic space-time equations is obtained by resolving the input-output relationship of the linear time invariant system. Second, we presented a four step scheme to solve the space-time equations within complex input environment. Step 1, three types of space-time array data matrixes are obtained by reasonable observation design in the field, including the target data matrix constructed by data from all the stations in the survey area, the natural field data matrixes constructed by data from the remote reference stations, and the man-made electromagnetic field data matrix constructed by data from the horizontal magnetic field differential signals of the survey stations. Step 2, by using the principal component analysis method, the polarization parameters of the natural sources and man-made noise sources are extracted from the natural field data matrix and man-made electromagnetic field data matrix respectively. Step 3, the system responses of all the observation channels corresponding to each sources are estimated from the target data matrix using linear regression method. Step4, impedance tensors, apparent resistivities and phases corresponding to different sources are estimated from the system responses, so as to achieve the goal of signal noise separation based on the input end. Last, numerical simulation and practical experiment are carried out to evaluate the proposed method. The results indicate that our method is better than the conventional magnetotelluric method for processing the data contaminated with correlated noise, and can effectively separate the responses of natural and man-made electromagnetic field. A larger size of the space-time array can obtain more reasonable results when the noise environment is complex in the survey area.
In order to optimize the express logistics distribution path and improve the distribution efficiency, the multi-objective optimization model of express logistics distribution path with mixed time windows is proposed. The model takes into account the requirements of express substations for on-time arrival, express enterprises' control of logistics costs, quantifying customer satisfaction with on-time arrival rate, and constituting logistics costs with fixed costs and transportation costs. Based on the multi-objective optimization genetic algorithm, the Pareto solution set is obtained, and the optimal solution is selected by combining four evaluation methods. The feasibility of the method is demonstrated by applying the method to the case of a region in North China.
Seepage is a common hydrogeologic hazard in engineering. Determining the seepage paths is vital for derisking the instability of embankment structures. With the improvement of the acquisition accuracy of magnetic sensors, the magnetometric resistivity method has become an emerging technology for detecting seepage paths through earth-filled dams. This technique is nondestructive and gives prominent signals. However, the resulting magnetic data have seen ambiguity in fully determining the targets. We develop an induced magnetic gradient surveying approach to monitor seepage paths in earth-filled dams. First, we briefly review the electromagnetic theory for the magnetic gradient tensor based on Maxwell’s equations. To match against the measurements, we present an accurate modeling framework using the third-order finite-element method and a novel compact difference scheme. We verify our approach on semianalytical 1D and 3D models. Systematic modeling studies are then carried out to investigate the spatial distribution characteristics and sensitivities of the induced magnetic gradient to the seepage in typical dam scenarios. In addition, we conduct two field experiments in the Zhongmou experimental base and Xixiayuan Reservoir in Henan Province, China, respectively. The induced magnetic field vector and its gradient components are both acquired. Cross-validation with a priori geologic information shows that the seepage path can be spatially identified by the induced magnetic gradient components [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], whereas the field components failed to locate the seepage pathways. This successful application indicates that our approach could be a promising solution for seepage path discrimination in earth-filled dams with high resolution.
SUMMARY Geophysicists today face the challenge of quickly and reliably interpreting extensive controlled-source electromagnetic (CSEM) data sets to map subsurface conductivity structures within realistic geological environments. An ideal 3-D CSEM inversion algorithm using tetrahedral grids should be capable of distinguishing different resolution requirements between forward modelling and inversion grids, have an optimal parallel strategy that fully exploits the inherent independence of CSEM data sets while also possessing the capability to handle large-scale geo-electrical models, and incorporate conductivity anisotropy which should be a common characteristic in realistic subsurface environments. However, existing tools in the geo-electromagnetic community often fall short of these three demands. Addressing this gap, our study introduces a scalable and parallel anisotropic inversion technique for CSEM data, capitalizing on the potential of unstructured tetrahedral grids. We first apply the tetrahedral longest-edge bisection method to create a refined dense, heterogeneous forward modelling grid from a coarse inversion grid. This refinement, focused on areas around transmitters and receivers, is seamlessly integrated within the coarser inversion grid’s topology, enabling precise conductivity mapping and preserving electromagnetic response accuracy during model updates. We further innovate with a source-mesh double-level parallel strategy, utilizing the message passing interface technique for parallel handling of independent CSEM data sets and large-scale geo-electrical models. Externally, we dedicate a processor for inversion model updates employing the Limited-memory Broyden–Fletcher–Goldfarb–Shanno optimization algorithm and divide other processors into groups, each associated with specific transmitting sources and frequencies. Internally, in each group, we employ a domain-decomposition-based scalable and robust iterative solvers using the Auxiliary-Space Maxwell pre-conditioner to parallel quickly calculate the electromagnetic responses from its assigned source-frequency set. Additionally, recognizing the potential for electrical conductivity anisotropy in field data, we incorporate the case of vertical transverse isotropy. We validate the effectiveness of our method through examples, including an isotropic land model with undulating topography, an anisotropic marine model and a real-field data case. Results from both synthetic and field data inversions underscore our method’s significant advancements in efficiency and practicality, particularly in addressing large-scale 3-D CSEM data sets inversion challenges in realistic geological environments.
When the controlled-source electromagnetic (CSEM) data are contaminated by intense cultural noise and the signal-to-noise ratio (S/N) is lower than 0 dB, the existing denoising methods can hardly achieve good results. To overcome the problem, a new strong-noise elimination method called inception-temporal convolutional network-shift-invariant sparse coding (IncepTCN-SISC) is developed based on deep learning and dictionary learning. First, a novel deep neural network model called IncepTCN is created based on the inception block and temporal convolutional network (TCN). Then, IncepTCN is used to recognize strong-noise segments in the observed signal, which are then discarded. Finally, a dictionary-learning method based on shift-invariant convolutional coding is used to denoise the remaining weak-noise segments. A series of simulated and field data experiments indicate that the new proposed IncepTCN network has obvious advantages in accuracy and efficiency compared with alternative methods. The average recognition accuracy of IncepTCN is 96.5%, which is 25.5%, 3.2%, 1.1%, and 2.0% higher than that of the fuzzy C-means clustering, convolutional neural network (CNN), residual network (ResNet), and the nonimproved TCN, respectively. In addition, the test results of unfamiliar data indicate that the generalization ability of IncepTCN is significantly better than the CNN, ResNet, and nonimproved TCN. This IncepTCN-SISC method can improve the S/N of CSEM data from −5.0 dB to 3.1 dB or from 5.0 dB to 31.9 dB and solve the denoising problem of noisy data below 0 dB to a certain extent. After IncepTCN-SISC processing, the initially distorted apparent resistivity curves become smooth, and the result is better than dictionary learning. This method is intelligent without any manual intervention and is suitable for batch processing of CSEM data.
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