ABSTRACTThe spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns. 相似文献
Mineral potential prediction is a process of establishing a statistical model that describes the relationship between evidence variables and mineral occurrences. In this study, evidence variables were constructed from geological, remote sensing, and geochemical data collected from the Lalingzaohuo district, Qinghai Province, China. Based on these evidence variables, a conjugate gradient logistic regression (CG-LR) model was established to predict exploration targets in the study area. The receiver operating characteristic (ROC) and prediction–area (P-A) curves were used to evaluate the effectiveness of the CG-LR model in mineral potential mapping. The difference between the vertical and horizontal coordinates of each point on the ROC curve was used to determine the optimal threshold for classifying the exploration targets. The optimal threshold corresponds to the point on the ROC curve where the difference between the vertical coordinate and the horizontal coordinate is the largest. In exploration target prediction in the study area, the CG algorithm was used to optimize iteratively the LR coefficients, and the prediction effectiveness was tested for different epochs. With increasing iterations, the prediction performance of the model becomes increasingly better. After 60 iterations, the LR model becomes stable and has the best performance in exploration target prediction. At this point, the exploration targets predicted by the CG-LR model occupy 14.39% of the study area and contain 93% of the known mineral deposits. The exploration targets predicted by the model are consistent with the metallogenic geological characteristics of the study area. Therefore, the CG-LR model can effectively integrate geological, remote sensing, and geochemical data for the study area to predict targets for mineral exploration.
Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.
Natural Resources Research - Some industrial activities, such as underground mining, hydraulic fracturing (HF), can cause microearthquakes and even damaging earthquakes. In recent years, with the... 相似文献
Soil CO_2 efflux, the second largest flux in a forest carbon budget, plays an important role in global carbon cycling. Forest logging is expected to have large effects on soil CO_2 efflux and carbon sequestration in forest ecosystems. However, a comprehensive understanding of soil CO_2 efflux dynamics in response to forest logging remains elusive due to large variability in results obtained across individual studies. Here, we used a meta-analysis approach to synthesize the results of 77 individual field studies to determine the impacts of forest logging on soil CO_2 efflux. Our results reveal that forest logging significantly stimulated soil CO_2 efflux of the growing season by 5.02%. However, averaged across all studies, nonsignificant effect was detected following forest logging. The large variation among forest logging impacts was best explained by forest type, logging type, and time since logging. Soil CO_2 efflux in coniferous forests exhibited a significant increase(4.38%) due to forest logging, while mixed and hardwood forests showed no significant change. Logging type also had a significant effect on soil CO_2 efflux, with thinning increasing soil CO_2 efflux by 12.05%, while clear-cutting decreasing soil CO_2 efflux by 8.63%. The time since logging also had variable effects, with higher soil CO_2 efflux for 2 years after logging, and lower for 3-6 years after logging; when exceeded 6 years, soil CO_2 efflux increased. As significantly negative impacts of forest logging were detected on fine root biomass, the general positive effects on soil CO_2 efflux can be explained by the accelerated decomposition of organic matter as a result of elevated soil temperature and organic substrate quality. Our results demonstrate that forest logging had potentially negative effects on carbon sequestration in forest ecosystems. 相似文献
Water flow velocity is an important hydraulic variable in hydrological and soil erosion models, and is greatly affected by freezing and thawing of the surface soil layer in cold high-altitude regions. The accurate measurement of rill flow velocity when impacted by the thawing process is critical to simulate runoff and sediment transport processes. In this study, an electrolyte tracer modelling method was used to measure rill flow velocity along a meadow soil slope at different thaw depths under simulated rainfall. Rill flow velocity was measured using four thawed soil depths (0, 1, 2 and 10 cm), four slope gradients (5°, 10°, 15° and 20°) and four rainfall intensities (30, 60, 90 and 120 mm·h−1). The results showed that the increase in thawed soil depth caused a decrease in rill flow velocity, whereby the rate of this decrease was also diminishing. Whilst the rill flow velocity was positively correlated with slope gradient and rainfall intensity, the response of rill flow velocity to these influencing factors varied with thawed soil depth. The mechanism by which thawed soil depth influenced rill flow velocity was attributed to the consumption of runoff energy, slope surface roughness, and the headcut effect. Rill flow velocity was modelled by thawed soil depth, slope gradient and rainfall intensity using an empirical function. This function predicted values that were in good agreement with the measured data. These results provide the foundation for a better understanding of the effect of thawed soil depth on slope hydrology, erosion and the parameterization scheme for hydrological and soil erosion models. 相似文献
Redox hot spots occurring as metal-rich anoxic groundwater discharges through oxic wetland and river sediments commonly result in the formation of iron (Fe) oxide precipitates. These redox-sensitive precipitates influence the release of nutrients and metals to surface water and can act as ‘contaminant sponges’ by absorbing toxic compounds. We explore the feasibility of a non-invasive, high-resolution magnetic susceptibility (MS) technique to efficiently map the spatial variations of magnetic Fe oxide precipitates in the shallow bed of three rivers impacted by anoxic groundwater discharge. Laboratory analyses on Mashpee River (MA, USA) sediments demonstrate the sensitivity of MS to sediment Fe concentrations. Field surveys in the Mashpee and Quashnet rivers (MA, USA) reveal several discrete high MS zones, which are associated with likely anoxic groundwater discharge as evaluated by riverbed temperature, vertical head gradient, and groundwater chemistry measurements. In the East River (CO, USA), widespread cobbles/rocks exhibit high background MS from geological ferrimagnetic minerals, thereby obscuring the relatively small enhancement of MS from groundwater induced Fe oxide precipitates. Our study suggests that, in settings with low geological sources of magnetic minerals such as lowland rivers and wetlands, MS may serve as a complementary tool to temperature methods for efficiently mapping Fe oxide accumulation zones due to anoxic groundwater discharges that may function as biogeochemical hot spots and water quality control points in gaining systems. 相似文献
ABSTRACTThis study investigated the late Quaternary climate and environmental characteristics of two tributary valleys (Xingmu and Depu Valleys) in the Parlung Zangbo Valley, southeastern Tibetan Plateau. Optically stimulated luminescence (OSL) samples collected from moraines at the mouth of Xingmu Valley produce a wide age range from 13.9 ka to 76 ka. The ages measured from the lenticular sand are consistent with the relative geomorphic sequence of the landforms. Lenticular sand layers below the moraine were dated to 37.9 ka and 44.7 ka, indicating that fluvial processes were likely dominant in the valley during Marine Isotope Stage (MIS) 3. The outer moraine ridges at the valley mouth were formed during 13.9 ka and 26.5 ka, corresponding to MIS2. At Depu Valley, OSL samples from two sets of lateral and terminal moraines close to the modern glacier, provide ages from 1.4 ka to 29.2 ka. The paleosol layer widely developed during 2.6 cal ka BP and 8.7 cal ka BP in the study area, reflecting a relatively warm condition during the mid-Holocene. 相似文献
