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 - Mining-induced fracture plays a key role in gas drainage for gas burst-prone underground coal mines, especially for closely multilayered coal seams. The layout and... 相似文献
Barley(Hordeum vulgare L.) is one of the earliest domesticated crop species and ranked as the fourth largest cereal production worldwide. Forward genetic studies in barley have greatly advanced plant genetics during the last century; however, most genes are identified by the conventional mapping method. Array genotyping and exome-capture sequencing have also been successfully used to target the causal mutation in barley populations, but these techniques are not widely adopted because of associated costs and partly due to the huge genome size of barley. This review summarizes three mapping cases of barley cuticle mutants in our laboratory with the help of RNA-sequencing. The causal mutations have been successfully identified for two of them and the target genes are located in the pericentromeric regions. Detailed information on the mapping-by-sequencing, mapping-and-sequencing, and RNA-sequencing assisted linkage mapping are presented and some limitations and challenges on the mapping assisted by RNA sequencing are also discussed. The alternative and elegant methods presented in this review may greatly accelerate forward genetics of barley mapping, especially for laboratories without large funding. 相似文献
In the field of seismic exploration, ground roll seriously affects the deep effective reflections from subsurface deep structures. Traditional curvelet transform cannot provide an adaptive basis function to achieve a suboptimal denoised result. In this paper, we propose a method based on empirical curvelet transform (ECT) for ground roll attenuation. Unlike the traditional curvelet transform, this method not only decomposes seismic data into multiscale and multi-directional components, but also provides an adaptive filter bank according to frequency content of seismic data itself. So, ground roll can be separated by using this method. However, as the frequency of reflection and ground roll components are close, we apply singular value decomposition (SVD) in the curvelet domain to differentiate the ground roll and reflection better. Examples of synthetic and field seismic data reveal that the proposed method based ECT performs better than the traditional curvelet method in terms of the suppression of ground roll. 相似文献
