For electromagnetic (EM) modeling based on the electric-field formulation at low frequencies, the quasi-static approximation (i.e., only the conduction current is considered and the displacement current is ignored) is commonly applied, and a small conductivity value for the air layer is chosen subjectively. Actually, in the air layer, due to the use of the small conductivity value, the quasi-static approximation is ubiquitously violated. However, the effect of the violation of the quasi-static approximation in the air on EM modeling is not well examined in the literature. In this paper, we investigate this issue by comparing the finite-difference modeling results from the calculation with the quasi-static approximation and those considering both the conduction and displacement currents. For the quasi-static approximation, the conductivity in the air is set to be different small values, and zero air conductivity is used for the modeling with both the conduction and displacement currents considered. Several simple models are designed to verify the numerical solution and study how the assigned conductivity for the air affects the modeling accuracy. One flat model and two models with topography are designed to examine the effect of the quasi-static approximation on the EM modeling results. For frequencies used in typical geophysical applications of EM diffusion, using the quasi-static approximation is able to produce accurate modeling results for models with typical earth conductivity. However, if the rough surface topography is considered, the use of the quasi-static approximation can reduce the EM modeling accuracy substantially at much lower frequencies (as low as several hundred Hz), which is probably due to the inaccurate description of EM waves in the air, and poses problems for applications based on direct EM field interpretation.
Environment protection is a core priority for many governments in this century. Most environmental problems have diverse causes: emission of greenhouse gases from fossil fuels, resource depletion, or intense mining activities such as the Huayuan manganese mine. The positioning of mining factories and water treatment stations impacts the surrounding groundwater reservoir. As the mine expands, the environmental impact also increases, and the previous plan based on monitoring wastewater leakage has become inappropriate. Therefore, to solve this issue, a new study is required to understand the lateral resistivity distribution underground and to define a new station location for water treatment and divert the sewage to that station. In this study, the audio-frequency magnetotelluric method is used. Surveys of two long lines that cross the mining area to its boundaries are carried out. Data are robustly processed and inverted. Based on the inverted models, in addition to the geologic information, drilling inspections, and solid waste distributions map, the integrated interpretation develops two sites on the top of impermeable layers, which constitute a buffer point between the unsafe (high concentration of pollutants) and the safe zones in the northwestern part of the mine. From the resistivity distribution combined with the water quality analysis, a relationship between the fault structures reveals an interconnected conductive zone (CZ) in the southeastern part. As the main channels for water circulating underground, these CZs delineate the main groundwater reservoir with a clastic aquifer layer. However, close to factories, the water from faults contains solid wastes, thereby making the groundwater in that zone nonpotable, unlike the safety zone located in the northwestern part. This workflow can become a field guide to improve the environment of mines and the deployment of hydrogeologic drilling in a safe area.
Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms' performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR.
Jingping, Yunnan, China, is an unfriendly area for ground survey due to the high altitude, rough terrain and dense vegetation. To detect the concealed magnetite ore bodies in this area, the aeromagnetic survey in a 7.8 km2 region was carried out. The field experiments were conducted to investigate the source and level of the noise. Then, the proton magnetometer was placed 3 m under the multirotor unmanned aerial vehicle (UAV) to decrease the static interference. The inverse distance weighting interpolation algorithm with a nonlinear filter was proposed to suppress the noise and dynamic interference caused by the strong wind. The results show that these methods can alleviate the interference of the UAV rotor and strong wind effectively. The patterns of the data from our aeromagnetic survey agree well with the horizontal distribution of the magnetic strata deduced from the geological background. Furthermore, the concealed mafic magmatic rocks and the titanium magnetite inferred from the aeromagnetic survey are confirmed by 4 drill logs in the study area, which supports the validity of the UAV aeromagnetic survey.
Abstract Two Cretaceous granitoid belts (i.e., the northwest and southeast belts) have been identified in Zhejiang, northeast South China Block. In this study, seven granitoid plutons from both the two belts were collected for zircon U-Pb dating, whole-rock geochemistry, Sr-Nd isotope, and zircon Hf isotope analyses. Chronologically, the Longyou (132 Ma), Sucun (136 Ma), Shanghekou (131 Ma), and Huangshitan (ca. 126 Ma) plutons from the northwest belt display older magma crystallization age than those of the Xiaoxiong (100 Ma), Zhujiajian (108 Ma), and Qingbang island (108 Ma) plutons from the southeast belt. The Sucun quartz monzonite and the Longyou, Shanghekou, Zhujiajian, and Qingbang island granites therein are fractionated I-type granites (i.e., partial melting of meta-igneous rocks) with relatively moderate-low Zr saturation temperature (723–823 °C) and pronouncedly evolved Nd and Hf isotopic compositions (εNd(t) = –8.17 to –5.67 and εHf(t) = –15.07 to –5.67), indicating that they are derivatives of ancient crustal melt-dominated magmas. The Huangshitan granite shows A-type granitic (i.e., granites that are alkaline and anhydrous and from anorogenic setting) features with high Ga/Al (3.47–5.58), rare earth element (REE) content (271–402 ppm), and Zr saturation temperature (781–889 °C). It holds less enriched Nd and Hf isotopic compositions (εNd(t) = –4.13 to –3.60 and εHf(t) = –5.90 to –2.16) and is attributed to partial melting of mature crustal materials with minor basaltic magma incorporation. The Xiaoxiong (quartz) syenitic porphyry is characterized by moderate SiO2 content (60.68–69.92 wt%), high alkali (9.03–11.66 wt%) and REE contents with fractionated REE pattern [(La/Yb)N = 13.8–26.1]. Its relatively depleted Nd and Hf isotopic compositions (εNd(t) = –3.67 to –3.42 and εHf(t) = –5.76 to –2.25) imply that it could be a derivative of basaltic magma from K-rich metasomatized mantle. Available geochronological data indicate that there were two episodic magmatic pulses at ca. 140–120 Ma and ca. 110–85 Ma associated with the Paleo-Pacific Plate underthrusting beneath the northeast South China Block. Here we put forward an episodic slab retreat and roll-back model to account for generation of these magmatic rocks. Firstly, the subducting Paleo-Pacific slab roll-back initiated at ca. 140 Ma and reached climax at ca. 130–120 Ma, which led to formation of the Longyou, Sucun, and Shanghekou I-type granites and the Huangshitan A-type granite, respectively. Subsequently, a flat slab subduction stage occurred with eastward trench retreat, causing a period of magmatic quiescence from ca. 120 to 110 Ma. The following second slab roll-back started at ca. 110 Ma and reached climax at ca. 100 Ma, giving rise to the earlier Zhujiajian and Qingbang island I-type granites and the later Xiaoxiong (quartz) syenitic porphyry.
ABSTRACT High-resolution micro-resistivity images are used to study the distribution of neoformed carbonate concretions in Upper Cenozoic shallowly buried fluvial sandstones in the Kuche Depression, northwest China. The carbonate concretions are high-resistivity white facies on borehole images. Most of them occur as patches 1-40 cm thick and in shapes ranging from equant, oblate, to elongate. These concretions contrast markedly with the poorly cemented or uncemented host sandstones, which appear as dark to immediate grayscale facies on the images. Three patterns of occurrence of carbonate concretions are identified: isolated concretions, discontinuously cemented layers, and continuously cemented layers. Most concretions occur in fine-grained, low-permeability laminae, suggesting that their growth was controlled more probably by diffusion transport rather than advective transport. Grain size, lamination, and distribution of intergranular clay all potentially influenced the selective distribution of carbonate cement in the sandstones. Shale interbeds and terrigenous carbonate rock fragments in the sandstones are potential sources of carbonate cement.
The airborne electromagnetic (AEM) method is an efficient tool for assessing conductivity structures near the earth's surface. The huge amounts of collected data over a survey area of tens to thousands of square kilometers result in an extremely high computational cost for rigorous modeling. Fortunately, for each transmitter and receiver (Tx-Rx) station, a volume of limited scale beneath the transmitter, called the footprint, contains the majority of the induced current and contributes most of the EM response at the receiver. In this letter, we develop a footprint-guided compact finite element method (CFEM), in which the inhomogeneous conductivity structure in the entire survey area is divided into small subareas based on the footprint so that the forward modeling for each subarea can be performed efficiently. The computational domain for every single Tx-Rx station consists of a small subarea and a surrounding layer. The accuracy of the algorithm is verified by comparing its solutions with semianalytical solutions on a layered earth model, and its applicability and efficiency are demonstrated with a more complex 3-D model consisting of a large inhomogeneous structure.
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