基于PSR-WSVM模型的边坡位移预测

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

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.     

液核地球自转速率变化的带谐潮效应Ⅱ--数值计算与比较

地球自转速率的潮汐变化可由无量纲参数k/cm)(k和cm分别为壳幔的有效Love数和有效极转动惯量)来表征。对于一个具有弹性地幔、平衡海潮和核幔不耦合的地球k/cm=0.944,且与潮波频率无关。海潮的非平衡扰动使k/cm为复数,且与频率有关。大气对自转速率有效勒夫数的贡献约为Δkat=0.0075。同时地幔滞弹性对勒夫数也产生扰动。利用本文得到的理论公式和最新的潮汐数据计算了地球自转速率的潮汐变化,及其有关地球物理机制的影响。     

Anthropogenic trace metals in an ice core at Vestfonna,Svalbard, Norway

1　IntroductionAnicecoreobtainedfrom polarglaciersoricesheetsisoneofthemostimportantarchivestoreconstructpaleoclimaticandpaleoatmosphericcondition .Informationonpale o environmentcanbeextractedfromicecoresaschemicaland/orphysicalsignals.Amongthechemicalsignals,heavymetalsarenotedassignalsofterrestrialenvironmentalchangeandanthropogenicpollution (e.g .Murozumietal.1 969;NgandPatterson 1 981 ;Hongetal.1 994) .SinceconcentrationsofmostofthemetalsinpolarsnowincentralGreen landareatorbelowthepptl…     

地下洞库勘察中的深斜孔钻探和压水试验技术

青岛市黄岛区拟建设海底地下油气储藏库,前期地质勘察工作要求施工倾角45°、深220m的取心钻孔4个,并在孔内分段进行压水试验.本文介绍了如何利用XY-4型金刚石小口径钻机施工基岩深斜孔及压水试验技术.     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

Metamorphic evolution of exhumed middle to lower crustal rocks in the Mojave Extensional Belt, southern California, USA

The Waterman Metamorphic Complex of the central Mojave Desert was exposed as a consequence of early Miocene detachment-dominated extension. However, it has evidence consistent with a more extensive geological history that involves collision of a crustal fragment(s), tectonic thickening by overthrusting and two periods of extension. The metamorphic complex contains granitoid intrusives and felsic mylonitic gneisses as well as polymetamorphic rocks that include marble, calc-silicate, quartzite. mafic granulite, pyribolite, amphibolite, migmatite and biotite schist. The latter group of rocks was affected by an initial series of high-grade metamorphic events (M1 and M2) and a localized lower grade overprint (M3). The initial metamorphism (M1) can be separated into two stages along its high-grade P–T path: M1a, a granulite facies metamorphism at 800–850° C and 7.5–9 kbar and Mlb, an upper amphibolite facies overprint at 750–800° C and 10–12 kbar. M1a developed mineral assemblages and textures consistent with granulite facies conditions at a reduced activity of H₂O and is associated with intense ductile deformation (D1) and minor local partial melting. M1b overprinted the granulite assemblages with a series of hydrous phases under conditions of increasing pressure and H₂O activity and is accompanied by little or no deformation. M2 developed at lower pressures and temperatures (650–750° C, 4.5–5.5 kbar) and is distinguished by a second local overprint of hydrous phases that reflects an input of aqueous fluids probably associated with the intrusion of a series of granitic dykes and veins. Effects of M3 are confined to the Mitchel detachment zone, an anastomosing early Miocene detachment fault, and are characterized by local ductile/brittle deformation (D2) of the pre-existing high-grade rocks and granitoid intrusives and by the production of mylonites and mylonitic gneisses under greenschist facies conditions (300–350° C, 3–5 kbar). The initial overprint (M1a) represents metamorphism, devolatilization and minor partial melting of supracrustal rocks under granulite facies conditions as a consequence of tectonic and, possibly, magmatic thickening. The increasing pressure transition of M1a to M1b reflects a period of continued compressional tectonism, thrusting and influx of H₂O, in part, locally related to crystallization of partial melts. The near isothermal decompression between M1b and M2 probably represents a pre-112-Ma extensional episode that may have been the result of a decompressional readjustment of a thickened crust. Following the initial extensional event, the metamorphic complex remained at depths of 10–17 km for at least 90 Ma until it was uplifted following Miocene extension. M3 develops locally in response to this second extensional period resulting from the early Miocene detachment faulting.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

Optimization of grid-drilling using computer simulation

A computer simulation method has been developed to find efficient drilling grids for mineral deposits. A well-known ore deposit is used as a model to develop an efficient pattern for undiscovered ore bodies in the same area or in other prospects where similar geometry is suspected. The model for this study is the Austinville, Virginia deposit, a Mississippi Valley-type deposit composed of 17 ore bodies totaling 34 million short tons (30 million metric tons). The method employs a computer program that simulates drilling the model deposit with different patterns, including various levels of follow-up drilling. Follow-up holes are drilled in fences at one half the original spacing around holes in the grid that show ore-grade mineralization. Each pattern is drilled 100 times from random starting locations to provide a range of outcomes of drilling, including the best, worst, and most likely. For this study, patterns of 100 drill holes were composed of 10 fences spaced 1000–5000 feet (305–1524 m) apart, each with 10 holes spaced 200–1000 feet (61–305 m) apart. In all, 25 grids were used with zero to three levels of follow-up drilling. The 600/2000 grid, with drill holes spaced 600 feet (183 m) apart in fences spaced 2000 feet (610 m) apart, was compared with the 200/5000 grid because they represented contrasting outcomes. The 600/2000 grid penetrated many ore bodies consistently but with few multiple hits to individual ore bodies; whereas the 200/5000 grid inconsistently penetrated few ore bodies with many multiple hits. The 600/2000 grid was more efficient than the 200/5000 grid at hitting large ore bodies of 1,000,000 short tons or greater (900,000 metric tons or greater) and was made more effective by adding one cycle of follow-up drilling. The 600/2000 grid had a 97% chance of hitting one or more large ore bodies with at least one drill hole per ore body, and the 200/5000 grid had a 64% chance. Once hit, there was an 82% chance that the largest ore body would be penetrated by three or more holes when using the 600/2000 grid and an 88% chance using the 200/5000 grid.