ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
Methane content in coal seam is an essential parameter for the assessment of coalbed gas reserves and is a threat to underground coal mining activities. Compared with the adsorption-isotherm-based indirect method, the direct method by sampling methane-bearing coal seams is apparently more accurate for predicting coalbed methane content. However, the traditional sampling method by using an opened sample tube or collecting drill cuttings with air drilling operation would lead to serious loss of coalbed methane in the sampling process. The pressurized sampling method by employing mechanical-valve-based pressure corer is expected to reduce the loss of coalbed methane, whereas it usually results in failure due to the wear of the mechanical valve. Sampling of methane-bearing coal seams by freezing was proposed in this study, and the coalbed gas desorption characteristics under freezing temperature were studied to verify the feasibility of this method. Results show that low temperature does not only improve the adsorption velocity of the coalbed gas, but also extend the adsorption process and increase the total adsorbed gas. The total adsorbed methane gas increased linearly with decreasing temperature, which was considered to be attributed to the decreased Gibbs free energy and molecular average free path of the coalbed gas molecular caused by low temperature. In contrast, the desorption velocity and total desorbed gas are significantly deceased under lower temperatures. The process of desorption can be divided into three phases. Desorption velocity decreases linearly at the first phase, and then, it shows a slow decreases at the second phase. Finally, the velocity of desorption levels off to a constant value at the third phase. The desorbed coalbed gas shows a parabolic relation to temperature at each phase, and it increases with increasing temperature at the first phase, and then, it poses a declining trend with increasing temperature at the rest phases. The experimental results show that decreasing the system temperature can restrain desorption of coalbed methane effectively, and it is proven to be a feasible way of sampling methane-bearing coal seams.
Natural Resources Research - A large ore loss and dilution can be expected when using a pre-blast ore boundary for shovel guidance because of the movement and re-distribution of ore in the muck... 相似文献
A numerical modeling study of the influence of the lateral flow on the estuarine exchange flow was conducted in the north passage of the Changjiang estuary. The lateral flows show substantial variabilities within a flood-ebb tidal cycle. The strong lateral flow occurring during flood tide is caused primarily by the unique cross-shoal flow that induces a strong northward (looking upstream) barotropic force near the surface and advects saltier water toward the northern part of the channel, resulting in a southward baroclinic force caused by the lateral density gradient. Thus, a two-layer structure of lateral flows is produced during the flood tide. The lateral flows are vigorous near the flood slack and the magnitude can exceed that of the along-channel tidal flow during that period. The strong vertical shear of the lateral flows and the salinity gradient in lateral direction generate lateral tidal straining, which are out of phase with the along-channel tidal straining. Consequently, stratification is enhanced at the early stage of the ebb tide. In contrast, strong along-channel straining is apparent during the late ebb tide. The vertical mixing disrupts the vertical density gradient, thus suppressing stratification. The impact of lateral straining on stratification during spring tide is more pronounced than that of along-channel straining during late flood and early ebb tides. The momentum balance along the estuary suggests that lateral flow can augment the residual exchange flow. The advection of lateral flows brings low-energy water from the shoal to the deep channel during the flood tide, whereas the energetic water is moved to the shoal via lateral advection during the ebb tide. The impact of lateral flow on estuarine circulation of this multiple-channel estuary is different from single-channel estuary. A model simulation by blocking the cross-shoal flow shows that the magnitudes of lateral flows and tidal straining are reduced. Moreover, the reduced lateral tidal straining results in a decrease in vertical stratification from the late flood to early ebb tides during the spring tide. By contrast, the along-channel tidal straining becomes dominant. The model results illustrate the important dynamic linkage between lateral flows and estuarine dynamics in the Changjiang estuary. 相似文献
