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.
High-pressure (HP) granulites provide telling records of mineral reactions at upper mantle to lower crustal levels and key information on the fate of material in subduction systems. The latter especially applies when they abut eclogite and mantle dunite because such rock associations are crucial for understanding the incompletely known processes at the interface of converging plates. A continental arc, active c. 520–395 Ma ago, formed an enigmatic example of such a rock association in the Songshugou area, Qinling Orogen. To unravel the juxtaposition of the distinct rocks, this study combines petrography, phase equilibria modelling, conventional thermobarometry, and zircon U–Th–Pb–Ti–REE analysis. Two mafic HP granulites, which contain the mineral assemblages garnet–clinopyroxene–plagioclase–rutile–mesoperthite–quartz and garnet–clinopyroxene–plagioclase–rutile, experienced peak metamorphic conditions of ≤1.4 GPa, 860°C and ~1.3 GPa, ≥910°C, respectively. During decompression and cooling, at 489 ± 4 Ma, amphibole lamellae unmixed from a clinopyroxene solid solution and orthopyroxene in part replaced garnet. A felsic HP granulite shows equilibration of garnet, perthite, antiperthite, kyanite, quartz, and rutile at 810–860°C, ~1.2 GPa, sillimanite growth during decompression, and upper amphibolite facies cooling at 510 ± 4 Ma. Though the thermobarometric data are just within the methodological errors, the U/Pb zircon ages imply the HP granulites did not evolve coherently. The HP granulites either represent foundered lower arc crust or originated from subduction erosion because their geochemistry is indistinguishable from that of the hanging-wall plate. Published and new pressure–temperature–time–deformation paths converge at ~710°C, ~0.9 GPa, and ≲470 Ma, implying exhumation tectonics juxtaposed the HP granulites with a mélange of eclogite and mantle dunite at lower crustal levels. This study highlights that lower arc crust can comprise material of diverse evolution. 相似文献