Electrical transport properties of saturated porous media, such as soils, rocks and fractured networks, typically composed of a non-conductive solid matrix and a conductive brine in the pore space, ...have numerous applications in reservoir engineering and petrophysics. One of the widely used electrical conductivity models is the empirical Archie's law that has a practical application in well-log interpretation of reservoir rocks. The Archie equation does not take into account the contributions of clay minerals, isolated porosity, heterogeneity in grains and pores and their distributions, as well as anisotropy. In the literature, either some modifications were presented to apply Archie's law to tight and clay-rich reservoirs or more modern models were developed to describe electrical conductivity in such reservoirs. In the former, a number of empirically derived parameters were proposed, which typically vary from one reservoir to another. In the latter, theoretical improvements by including detailed characteristics of pore space morphology led to developing more complex electrical conductivity models. Such models enabled us to address the electrical properties in a wider range of potential reservoir rocks through theoretical parameters related to key reservoir-defining petrophysical properties. This paper presents a review of the electrical conductivity models developed using fractal, percolation and effective medium theories. Key results obtained by comparing experiential and theoretical models with experiments/simulations, as well as advantages and drawbacks of each model are analyzed. Approaches to obtaining more reasonable electrical conductivity models are discussed. Experiments suggest more complex relationships between electrical conductivity and porosity than experiential models, particularly in low-porosity formations. However, the available theoretical models combined with simulations do provide insight to how microscale physics affects macroscale electrical conductivity in porous media.
Archie's equation is an empirical electrical conductivity‐porosity model that has been used to predict the formation factor of porous rock for more than 70 years. However, the physical interpretation ...of its parameters, e.g., the cementation exponent m, remains questionable. In this study, a theoretical electrical conductivity equation is derived based on the fractal characteristics of porous media. The proposed model is expressed in terms of the tortuosity fractal dimension (DT), the pore fractal dimension (Df), the electrical conductivity of the pore liquid, and the porosity. The empirical parameter m is then determined from physically based parameters, such as DT and Df. Furthermore, a distinct interrelationship between DT and Df is obtained. We find a reasonably good match between the predicted formation factor by our model and experimental data.
Key Points
An electrical conductivity model is derived based on fractal geometry
Physical definitions of empirical parameters in Archie's equation are presented
Predictions of proposed model show good correspondence with experimental data
Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground ...points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the scene is complex. This paper proposes a new method for ground point extraction based on deep learning using deep convolutional neural networks (CNN). For every point with spatial context, the neighboring points within a window are extracted and transformed into an image. Then, the classification of a point can be treated as the classification of an image; the point-to-image transformation is carefully crafted by considering the height information in the neighborhood area. After being trained on approximately 17 million labeled ALS points, the deep CNN model can learn how a human operator recognizes a point as a ground point or not. The model performs better than typical existing algorithms in terms of error rate, indicating the significant potential of deep-learning-based methods in feature extraction from a point cloud.
Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method ...with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales.
In recent years, building change detection has made remarkable progress through using deep learning. The core problems of this technique are the need for additional data (e.g., Lidar or semantic ...labels) and the difficulty in extracting sufficient features. In this paper, we propose an end-to-end network, called the pyramid feature-based attention-guided Siamese network (PGA-SiamNet), to solve these problems. The network is trained to capture possible changes using a convolutional neural network in a pyramid. It emphasizes the importance of correlation among the input feature pairs by introducing a global co-attention mechanism. Furthermore, we effectively improved the long-range dependencies of the features by utilizing various attention mechanisms and then aggregating the features of the low-level and co-attention level; this helps to obtain richer object information. Finally, we evaluated our method with a publicly available dataset (WHU) building dataset and a new dataset (EV-CD) building dataset. The experiments demonstrate that the proposed method is effective for building change detection and outperforms the existing state-of-the-art methods on high-resolution remote sensing orthoimages in various metrics.
In the Mesozoic, the age of Paleo‐Pacific plate that flatly subducted beneath the South China was ∼80 Ma, resulting in a cold and dense oceanic slab. The transition process from flat to steep ...subduction of this old slab and factors that affect it are still unclear. Using 2‐D thermomechanical numerical models, we investigate the dependence of the transition mode on the size of TOC (length and thickness) and the subduction rate. The results indicate that under low subduction rates (e.g., 1 cm/yr), even a 100‐km long eclogitized TOC can induce slab delamination beneath continental lithosphere. The maximum subduction rate for slab delamination to occur increases with an increasing length or thickness of TOC. However, when the subduction rate is over a certain critical value (e.g., ≥4 cm/yr), oceanic slab rolls back. The presence of the TOC delays the initial rollback time, as compared to cases with no TOC. In addition, with a TOC of small size (e.g., 100‐km length and 20‐km thickness) under a certain range of subduction rate, an 80‐Ma old slab can maintain a long‐lasting flat subduction. The comparisons between the numerical results and global plate motion models indicate that the northwestward motion of South China Block since ca. 195 ± 5 Ma induced the delamination of oceanic slab. The arrival of the TOC in the Paleo‐Pacific Plate to the continental arc should be later than the previously estimated ca. 250 Ma. Determination of the TOC size requires three‐dimensional simulations and more detailed geological records.
Key Points
The maximum subduction rate for slab delamination to occur increases with an increasing length or thickness of the thickened oceanic crust
The arrival time of the thickened oceanic crust to the continental arc of the South China Block is expected to be later than ca. 250 Ma
The size of the thickened oceanic crust can be estimated from the arrival time of the thickened oceanic crust and the subduction rate
The western segment of the suture zone between the Yangtze and Cathaysia blocks, which is the most important tectonic boundary related to the formation and rifting of south China, is enigmatic and ...not fully understood due to the sporadic exposure of Precambrian strata and ophiolites. Three‐dimensional electrical resistivity models derived from inversion of magnetotelluric data identified a lithospheric‐scale conductive zone extending northeastwards beneath the Youjiang basin, which was interpreted as the western segment of the suture zone. The high conductivity and coincident high magnetic anomalies closely match the location of Carlin‐type gold deposits, which can be explained by fluids and gold‐bearing sulfide minerals in a fossil suture zone. Inconsistent with the southeast‐dip resolved at the eastern segment of the suture zone (the Jiangshan‐Shaoxing fault) in the earlier study, the slightly north‐dipping geometry at the western suture zone implies the reactivation by northward subduction and closure of the Paleo‐Tethys Ocean.
Plain Language Summary
Suture zones are tectonic scars that mark the subduction, collision, and amalgamation of different tectonic units. Imaging suture zones is critical because they are first‐order tectonic boundaries and can play a guiding role in continental rifting and breakup. The suture zone between the Yangtze and Cathaysia blocks is the most important tectonic boundary in south China. While the eastern segment of this suture zone is agreed to be located along the Jiangshan‐Shaoxing fault, the western segment of this suture zone is under debate. This study used magnetotelluric method to image the deep earth structure beneath the western boundary of the Yangtze and Cathaysia blocks. We identified a high‐conductivity zone from the subsurface to a depth of 80 km and interpreted this zone as being caused by fluids and gold‐bearing sulfide minerals in a fossil suture zone. The conductivity pattern observed in this suture zone differs from that in the earlier study about the eastern segment of the suture zone. This implies that the western suture zone has been modified after the initial collision of the two continents, perhaps by northward subduction and closure of the Paleo‐Tethys Ocean.
Key Points
Magnetotelluric array data were inverted to obtain the lithospheric resistivity structure in southwestern China
Three‐dimensional resistivity model images a Precambrian suture beneath the Youjiang basin
The geometry resolved at the suture zone implies the reactivation by northward subduction and closure of the Paleo‐Tethys Ocean
We solve the 3D controlled-source electromagnetic (CSEM) problem using the edge-based finite element method. The modeling domain is discretized using unstructured tetrahedral mesh. We adopt the total ...field formulation for the quasi-static variant of Maxwell's equation and the computation cost to calculate the primary field can be saved. We adopt a new boundary condition which approximate the total field on the boundary by the primary field corresponding to the layered earth approximation of the complicated conductivity model. The primary field on the modeling boundary is calculated using fast Hankel transform. By using this new type of boundary condition, the computation cost can be reduced significantly and the modeling accuracy can be improved. We consider that the conductivity can be anisotropic. We solve the finite element system of equations using a parallelized multifrontal solver which works efficiently for multiple source and large scale electromagnetic modeling.
•This paper develops an edge-based finite element method for 3D CSEM modeling.•The algorithm is capable of dealing with anisotropic conductivity.•We adopt a total field formulation and propose a new advanced boundary condition.•We use a parallelized multifrontal method to solve the system of equations.•The developed method is effective in modeling complex geometry such as bathymetry.
SUMMARY
Central loop transient electromagnetic (TEM) data are often interpreted by conventional 1-D or quasi-2-D inversion techniques. For example, the lateral constrained inversion (LCI) is a ...powerful technique for quick interpretation of central loop TEM data, and can produce spatially consistent resistivity images for profile data by assuming spatial correlation between adjacent model parameters. Such quasi-2-D techniques are very powerful in cases multidimensional effects are small or negligible. However, the inverse solution of conventional LCI methods strongly depends on subjective interpreter choices such as the model regularization and the imposed lateral constraints. Due to inherent non-linearity and nonuniqueness of the TEM inverse problems, this can result in biased model parameters and their estimated model uncertainties. We present a transdimensional Markov chain Monte Carlo method for the quasi-2-D inversion of TEM data using a Bayesian inference framework. We term the approach quasi-2-D, since the model is parametrized in 2-D with unstructured Voronoi cells, whereas the TEM response at each station is predicted using a 1-D forward solution to make the problem computationally affordable. During the inversion, the number of Voronoi cells as well as their positions and resistivities are variable. Accordingly, the level of model complexity is automatically determined by the framework and adapted to the spatial resolution of the data, thus avoiding the need for subjective model regularization or spatial constraints. The approach is validated using synthetic data and compared to 1-D Bayesian and conventional Gauss Newton inversion techniques. The application to dense field data from a floating TEM survey leads to a consistent subsurface image with unbiased uncertainty estimates and a plausible depth of investigation. The quantitative uncertainty information provided by the Bayesian framework is beneficial in identifying resolution.
PDF
