Journal of Applied Geophysics最新文献

Many geological problems cannot be solved by electrical prospecting methods. Often the reason for this is a slight difference in the electrical resistivity of target objects from that of the host medium. At the same time, the use of the transverse magnetic (TM) type field makes it possible to identify low-contrast objects and delineate their boundaries with high accuracy. TM-type prospecting is more efficient since its signal is much more strongly affected by changes in resistivity and other electrodynamic parameters. The article examines one of the difficult cases – a search for kimberlite pipes in Yakutia. Such objects differ very little from the host medium in terms of the horizontal resistivity. Exploration works to search for kimberlite pipes in Yakutia, carried out by conventional electrical prospecting methods, have many problems. The article considers the results of successful TM-type prospecting field surveys where kimberlite pipes stood out very prominently. Also presented are the results of modeling an even more complex situation in which the pipes are located at depths of over 140 m and overlain by traps; there are many objects in the area that create additional anomalies.

Geophysical methods are widely used to gather information about the subsurface as they are non-intrusive and comparably cheap. However, the solution to the geophysical inverse problem is inherently non-unique, which introduces considerable uncertainties. As a partial remedy to this problem, independently acquired geophysical data sets can be jointly inverted to reduce ambiguities in the resulting multi-physical subsurface images. A novel cooperative inversion approach with joint minimum entropy constraints is used to create more consistent multi-physical images with sharper boundaries with respect to the single-method inversions. Here, this approach is implemented in an open-source software and its applicability on electrical resistivity tomography (ERT), seismic refraction tomography (SRT), and magnetic data is investigated. A synthetic 2D ERT and SRT data study is used to demonstrate the approach and to investigate the influence of the governing parameters. The findings showcase the advantage of the joint minimum entropy (JME) stabilizer over separate, conventional smoothness-constrained inversions. The method is then used to analyze field data from Rockeskyller Kopf, Germany. 3D ERT and magnetic data are combined and the results confirm the expected volcanic diatreme structure with improved details. The multi-physical images of both methods are consistent in some regions, as similar boundaries are produced in the resulting models. Because of its sensitivity to hydrologic conditions in the subsurface, observations suggest that the ERT method senses different structures than the magnetic method. These structures in the ERT result do not seem to be enforced on the magnetic susceptibility distribution, showcasing the flexibility of the approach. Both investigations outline the importance of a suitable parameter and reference model selection for the performance of the approach and suggest careful parameter tests prior to the joint inversion. With proper settings, the JME inversion is a promising tool for geophysical imaging, however, this work also identifies some objectives for future studies and additional research to explore and optimize the method.

The remote Southern region of Angola is covered by siliciclastic Kalahari Cenozoic formations that host underground aquifers of great importance to local populations affected by water scarcity problems. These aquifers are well developed where Kalahari sands reach appropriate thicknesses. On the other hand, at the eastern end of this area, regional aeromagnetic data recently acquired suggested the possibility of the continuity of the geological structures of the Lufilian Arc, sited in the nearby Zambia and Congo, southwestwards into Angola under the Kalahari formations. Once the Lufilian Arc is associated with the presence of the so-called Central African Copperbelt, this possibility increased the interest in determining the depth to Pan-African rocks under the Kalahari basin. To estimate the thickness of Kalahari formations in this area of difficult access and poor logistics, an expedited and non-invasive geophysical method was needed. Seismic noise and the single-station Nakamura technique were chosen, but due to the large distance of the study area from the ocean, one of the major sources of seismic noise, a test survey was acquired in the Cuvelai region to assess the signal quality, where the data was calibrated using available drill-holes. >170 points of seismic ambient noise were later acquired and the horizontal/vertical (HVSR) amplitude versus frequency curves were 1D inverted for the best velocity/density model for each station. The results were compared with 1D inverted legacy vertical electrical soundings reprocessed and validated in this work, showing similar depth-to-basement, while interpreted velocities/densities of geological formations were sampled and confirmed with measurements. A depth-to-basement map was produced using seismic information, mechanical soundings, and geological information. Despite the relatively reduced geographical area covered, the map presents valuable information for hydrogeology and mineral exploration purposes and agrees with a previously available coarser map of Kalahari thickness and with observations from geological surveys simultaneously conducted at the time of the seismic surveys.

Elastic least-squares reverse time migration (ELSRTM), as an imaging method, offers advantages over conventional elastic reverse time migration (ERTM), including higher resolution, better amplitude balancing, reduced crosstalk, and broader bandwidth. However, conventional ELSRTM involves iterative processes in the data domain, resulting in high computational costs. Moreover, since time is continuous during data domain extrapolation, it cannot solely focus on the target area within the subsurface medium. In contrast, image-domain ELSRTM (IDELSRTM) exhibits high computational efficiency and the ability to image target area. Currently, research on image-domain least-squares reverse time migration is predominantly focused on the acoustic wave assumption, despite elastic waves being closer to the actual subsurface medium and providing richer imaging information. In this study, within the framework of data domain ELSRTM, we derived the objective function for the IDELSRTM and introduced an L1 regularization term under the L2 norm to enhance inversion stability. We devised an inversion strategy employing the fast iterative shrinkage-thresholding algorithm (FISTA). Furthermore, drawing from the point spread functions theory in optics, we derived the mapping relationship between the elastic multi-parameter point spread functions (PSF) and the elastic multi-parameter Hessian matrix, and the relationship between the Hessian matrix and the ERTM images. We provided the computational method for the elastic multi-parameter Hessian matrix and utilized it as the linearized forward operator for IDELSRTM. Through numerical experiments, we further elucidated the relationship between the ERTM images and the Hessian matrix under the framework of IDELSRTM, along with the sources of crosstalk in ERTM. Applying our proposed target-oriented IDELSRTM method to layered models and the Marmousi2 model, we demonstrated its effectiveness in improving imaging resolution and quality with only a marginal increase in computational overhead compared to conventional ERTM.

We present an approach to calculate the complex dielectric permittivity of a microheterogeneous rock composed of non-conductive solid grains with surface conductivity and a conductive liquid.

We have calculated the effective electrical properties of a rock using the model that consider the complex structure of the conducting double layer between a solid grain and the electrolyte in the pores. The influence of two parts of double layer: the Stern (inner) layer on the solid surface and the diffuse (outer) layer was considered.

Previously, the Differential Effective Medium (DEM) scheme was used to calculate the effective conductivity and dielectric permittivity. In contrast, we have adopted the Effective Medium Approximation (EMA) method for calculation of the effective electromagnetic properties of a rock with high inclusion concentration. This method allows one to describe both elastic and electromagnetic properties of the rock based on the unified model of the pore space.

The calculations were performed both for the rock model with a fixed grain size and for the model with a fractal distribution of grain sizes.

Our calculations have shown that the value of the dielectric permittivity in the low frequency range depends on the concentration and dimension of solid grains. However, the frequency-dispersion behavior is a function of the inclusion size only and it does not relate to the inclusion concentration in the porosity range typical for sedimentary rocks. This effect confirms the feasibility of the determination of the inclusion concentration and dimension by using the dielectric permeability and electrical conductivity dispersion curves.

Marine controlled source electromagnetic (MCSEM) is profoundly used for undersea resources exploration. The effective signal is easily contaminated by kinds of noise when the transmitter-receiver offset is large. Suppressing the noise influence is vital to improve data quality and further interpretation accuracy. Denoising becomes a research focus with the widespread application of the MCSEM technique. Many denoising approaches are proposed by different researchers. However, most of them only target a single type of noise, which severely limits the application of these approaches. The fast-developing dictionary learning technique paves a new way for MCSEM data denoising. Currently, typical dictionary learning algorithms include k-means singular value decomposition (K-SVD), data-driven tight frame (DDTF), shift-invariant sparse coding (SISC) and so on. These three algorithms are different in principles and arithmetic processes. Their applications for MCSEM data denoising are explored for the first time in this article. Besides, a comparative analysis of these three noise reduction methods is carried out. The comparison proves the effectiveness and superiority of the K-SVD, followed by the DDTF method. Besides, all these denoising methods are applied to the field data. The results further corroborates the above conclusions.

The current existing researches were discussed to assess the advantages and limitations of existing empirical and theoretical models for direct conductivity (DC) prediction. Prior research has demonstrated that an effective medium model may not accurately reflect the actual situation due to the presence of two types of water (bound water and bulk water) in clay-rich materials. Furthermore, the existing models can not satisfy the prediction of electrical conductivity of metal ions adsorbed clay or unsaturated clay. To address this issue, a new Effective Medium Double Water (EMDW) model was proposed based on multiple scattering techniques, which encompasses soil particles, surface-bound water layers, and bulk water and was established by controlled soil types and degrees of saturation. The novel EMDW model includes the Coherent Potential Approximation (CPA), which has consistently demonstrated superior agreement with experimental data when compared to other approximation models. Moreover, the binomial expansion approximation was utilized to simplify the formula and facilitate its use. The developed conductivity model was validated with data from other researchers. In comparison to other well-established conductivity models, the proposed EMDW model has clear physical meaning and can accurately compute matrix conductivity utilizing modified coated particle conductivity and saturation conductivity. The findings suggest that matrix conductivity in clay materials is significantly correlated with electrical-physical parameters, such as porosity, degree of saturation, shape of each discontinuous phase, and conductivity of surface-bound water and bulk water. Consequently, the new EMDW model is a theoretically grounded, physically meaningful, and easy-to-use model for conductivity prediction in clay materials.

Formation overpressure seriously affects drilling and downhole operation. Accurate prediction on the formation pore pressure can not only reduce the probability of drilling accidents, but also quantitatively evaluate the original formation pressure of underground pore space, which provides an important reference for site selection of carbon sink projects using underground space resources such as CO₂ geological storage. It is therefore necessary to set up a widely applicable method that is based on rock physics theory and conforms to the characteristics of rock mechanics and fluid mechanic. This method is suitable for both logging prediction and seismic inversion of pore pressure. The traditional method of predicting pore pressure based on P-wave velocity has multiple solutions, and the prediction based on S-wave velocity which is not sensitive to fluid has new significance. Based on the Hertz-Mindlin petrophysical model that considering pressure variation and the Gassmann fluid substitution equation that addresses the change in fluid saturation, this paper firstly derived rock physical formulas for predicting pore pressure in logging, and then derived the intrinsic power function relationship between the effective pressure (P_e) and S-wave velocity (V_s) as well as S-wave impedance (I_s). Based on this, a set of geophysical methods integrating S-wave velocity prediction, pore pressure prediction in well and seismic inversion is finally established. The efficacy of this method has been well validated, with an average error of 2.35% in S-wave velocities prediction, 4.5% in single-well pore pressure prediction. The results of seismic inversion of pore pressure are consistent with the phenomenon of overpressure development in actual working area. This method can be further extended to other areas, providing invaluable reference for underground operation such as oil and gas exploration and CO₂ geological storage.

This study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.

Road underground defect detection plays a crucial role in assessing transportation infrastructure. Ground penetrating radar (GPR) serves as a widely used geophysical tool for this purpose. However, the traditional manual interpretation of GPR images heavily relies on the experience of the practitioner, leading to inefficiency and inaccuracies. To tackle these challenges, this paper proposes an automatic detection method for underground defects of roads based on an improved YOLOv5s model. First, the dense connection structure is integrated in the C3 module of the backbone to form the Dense-C3 module to enhance the capability of feature extraction. Subsequently, a convolutional block attention module (CBAM) is incorporated after each Dense-C3 module to refine features and enhance efficiency. Furthermore, the focal loss function is employed for the confidence loss to mitigate the impact of sample imbalance on detection performance. Experimental results demonstrate that the proposed model achieves a mean average precision (mAP) of 96.4% for synthetic data and 91.9% for real data, outperforming seven other models. The detection speed of the proposed model for real data reaches 51 frames per second, meeting the real-time detection requirements of road underground defects.