Theory and Method of Mineral Resource Prediction Based on Synthetic Information

Metallogenic prognosis of synthetic information uses the geological body and the mineral resource body as a statistical unit to interpret synthetically the information of geology, geophysics, geochemistry and remote sensing from the evolution of geology and puts all the information into one entire system by drawing up digitalized interpretation maps of the synthetic information. On such basis, different grades and types of mineral resource prospecting models and predictive models of synthetic information can be established. Hence, a new integrated prediction system will be formed of metallogenic prognosis (qualitative prediction), mineral resources statistic prediction (determining targets) and mineral resources prediction (determining resources amount).

Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.

Aspects of Regional and Worldwide Mineral Resource Prediction

The purpose of this contribution is to highlight four topics of regional and worldwide mineral resource prediction:(1)use of the jackknife for bias elimination in regional mineral potential assessments;(2)estimating total amounts of metal from mineral potential maps;(3)fractal/multifractal modeling of mineral deposit density data in permissive areas;and(4)worldwide and large-areas metal size-frequency distribution modeling.The techniques described in this paper remain tentative because they have not been widely researched and applied in mineral potential studies.Although most of the content of this paper has previously been published,several perspectives for further research are suggested.

Genetic Modeling of GIS-Based Cell Clusters and Its Application in Mineral Resources Prediction

This paper presents a synthetic analysis method for multi sourced g eo logical data from geographic information system (GIS). In the previous practices of mineral resources prediction, a usually adopted methodology has been sta tistical analysis of cells delimitated based on thoughts of random sampling. Tha t might lead to insufficient utilization of local spatial information, for a cel l is treated as a point without internal structure. We now take “cell clusters ”, i. e. , spatial associations of cells, as basic units of statistics, thus th e spatial configuration information of geological variables is easier to be dete cted and utilized, and the accuracy and reliability of prediction are improved. We build a linear multi discriminating model for the clusters via genetic algor ithm. Both the right judgment rates and the in class vs. between class distan ce ratios are considered to form the evolutional adaptive values of the populati on. An application of the method in gold mineral resources prediction in east Xi njiang, China is presented.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

Resource pre-allocation algorithms for low-energy task scheduling of cloud computing

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

Application of Geophysical and Remote Sensing Methods to Predict for Potash Resource

1 Introduction Potassium is listed as one of the shortage of mineral resources in china.Geophysical and remote sensing technology plays an important role in prospecting for potash ressources.

Expert System for Statistical Prediction of Mineral Deposits on Middle and Large Scales

The expert system for statistical prediction of mineral deposits on middle and large scales takes the system of scientific exploration theories, criteria and methods proposed by Professor Zhao Pengda as the field expert knowledge. At present the developed system focuses on two aspects: synthetic exploration and quantitative exploration. Among the three basic theories for the prediction of deposits, it highlights the applications of seeking anomaly theory. This system is characteristic in the determination of geological background, the study of geological anomalies and the delineation of geological background, the study of geological anomalies and the delineation of mineralization anomalies. The system combines closely the knowledge base, method base and database .integrates the input and output information of multi - sources and mul-ti - variables , data , graphs and imagine processing system and inquiring system as a whole . So the system can meet in general all kinds of demands in statistical prediction of mineral deposits . Since the statistical prediction of mineral resources is a kind of systematic engineering pro ject , a further study should be carried out on the fields of theoretical exploration and ster eo - exploration on the basis of unceasingly perfecting the above-mentioned fields in order to establish a comprehensive intelligent system for scientific exploration , to provide new methods , new techniques and new ideas for fast prospecting appraisal of mineral resources .

An Integrated Image Task Planning in Satellite Networks:From Instruction Release and Observation Perspective

The unreasonable observation arrangements in the satellite operation control center(SOCC)may result in the observation data cannot be downloaded as scheduled.Meanwhile,if the operation instructions released by the satellite telemetry tracking center(STTC)for the on-board payloads are not injected on the specific satellites in time,the corresponding satellites cannot perform the observation operations as planned.Therefore,there is an urgent need to design an integrated instruction release,and observation task planning(I-IRO-TP)scheme by efficiently collaborating the SOCC and STTC.Motivated by this fact,we design an interaction mechanism between the SOCC and the STTC,where we first formulate the I-IRO-TP problem as a constraint satisfaction problem aiming at maximizing the number of completed tasks.Furthermore,we propose an interactive imaging task planning algorithm based on the analysis of resource distribution in the STTC during the previous planning periods to preferentially select the observation arcs that not only satisfy the requirements in the observation resource allocation phase but also facilitate the arrangement of measurement and control instruction release.We conduct extensive simulations to demonstrate the effectiveness of the proposed algorithm in terms of the number of completed tasks.

GIS Predictive Model for Producing Hydrothermal Gold Potential Map Using Weights of Evidence Approach in Gengma Region, Sanjiang District, China

Gengma region, Sanjiang district is known to have some large-scale gold deposits. GIS predictive model for hydrothermal gold potential was carried out in this region using weights of evidence modeling technique. Datasets used include large-scale hydrothermal gold deposit records, geological, geophysical and remote sensing imagery. Based on the geological and mineral characteristics of areas with known gold occurrences in Sanjiang, several geological features were thought to be indicative of areas with potential for the occurrence of hydrothermal gold deposits. Indicative features were extracted from geoexploration datasets for use as input in the predictive model. The features include host rock lithology, geologic structures, wallrock alteration and associated (volcanic-plutonic) igneous rocks. To determine which of the indicative geological features are important spatial predictors of area with potential for gold deposits, spatial analysis was done through the modeling method. The input maps were buffered and the optimum distance of spatial association for each geological feature was determined by calculating the contrast and studentized contrast. Five feature maps were converted to binary predictor patterns and used as evidential layers for predictive modeling. The binary patterns were integrated in two combinations, each of which consists of four patterns in order to avoid over prediction due to the effect of duplicate features in the two structural evidences. The two produced potential maps define almost similar favorable zones. Areas of intersections between these zones in the two potential maps placed the highest predictive favorable zones in the region.

Predictive GIS Model for Potential Mapping of Cu,Pb,Zn Mineralization

ABSTRACT The geologic features indicative of Cu, Pb, Zn mineral deposits in a area are fractures （structure）, and host rock sediments. Datasets used include Cu, Pb, Zn deposit points record, geological data, remote sensing imagery （Landsat TM5）. The mineral potential of the study area is assessed by means of GIS based geodata integration techniques for generating predictive maps. GIS predictive model for Cu, Pb, Zn potential was carried out in this study area （Weixi） using weight of evidence. The weights of evidence modeling techniques is the data driven method in which the spatial associations of the indicative geologic features with the known mineral occurrences in the area are quantified, and weights statistically assigned to the geologic features. The best predictive map generated by this method defines 24 % the area having potential for Cu, Pb, Zn mineralization further exploration work.

The Metallogenetic Regularities of Lithium Deposits in China

Lithium resources support the development of high-technology industries. China has abundant lithium resources which are mainly distributed in Tibet, Qinghai, Sichuan and Jiangxi. Salt lakes in China have significant lithium reserves, but lithium is mainly produced from hard rock lithium deposits because the extraction from salt lakes requires further improvements. The hard rock lithium deposits mainly occur in granitic pegmatite in the Altay region of Xinjiang and the Jiajika deposit in western Sichuan Province; they mainly formed in the Mesozoic and occurred in a relatively stable stage during orogenic processes. On the basis of the information from 151 lithium deposits or spots, 14 lithium metallogenic series were identified, and granitic pegmatite, granite, and sedimentary types were considered to be the main prediction types of lithium resources. Twelve lithium mineralization belts were divided and a series of maps showing the lithium metallogenetic regularity in China were drawn. We conclude that the hard rock and brine type of lithium resources possibly have a similar lithium source related to magmatism. The mctallogenic features of the lithium in China were related with the distinct history of tectonic-magmatic activity in China. This study benefits the assessment of, and prospecting for, lithium resources in China.

A Preliminary Review of the Metallogenic Regularity of Nickel Deposits in China

The nickel deposits mainly distributed in 19 provinces and autonomous regions in China are 339 ore deposits/occurrences, including 4 super large-scale deposits, 14 large-scale deposits, 26 middle- scale deposits, 75 small-scale deposits, and 220 mineralized occurrences. The prediction types of mineral resources of nickel deposits are magmatic type, marine sedimentary type and regolith type. The formation age is from the Neoarchean to the Cenozoic with two peaks in the Neoproterozoic and the late Paleozoic. The nickel deposits formed in the Neoproterozoic are located on the margin of the North China Block and Yangtze Block, and those formed in the late Paleozoic are mainly distributed in the Central Asian Orogenic Belt （CAOB）, Emeishan and the Tarim Large Igneous Provinces （LIPs）. Magmatic nickel deposits are mainly related with broken-up continental margin, post-collision extension of the orogenic belt and mantle plume. According to different tectonic backgrounds and main characteristics of magmatism, the Ni-Cu-Co-PGE metallogenie series types of ore deposits related with mantle-derived mafic-ultramafic rocks can be divided into 4 subtypes： （1） the Ni-Cu-Co- PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in the broken-up continental margin, （2） the Ni-Cu-Co-PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in mantle plume magmatism, （3） the Ni-Cu-Co- PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in the subduction of the orogenic belt, and （4） the Ni-Cu-Co-PGE metallogenic series subtype of ore deposits related with mantle-derived mafic-ultramafic rocks in post-collision extension of the orogenic belt. We have discussed in this paper the typical characteristics and metaliogenic models for Neoproterozoic Ni-Cu-（PGE） deposits related with broken-up continental margin, Cambrian marine sedimentary Ni-Mo-V deposits related with black shale, early Permian Ni-Cu deposits related with post-collision extension of the orogenic belt, late Permian Ni-Cu-（PGE） deposits related with Large Igneous Provinces （LIPs）, and Cenozoic Ni-Au deposits related with regolith. The broken-up continental margin, mantle plume and post-collision extension of the orogenic belt are important ore- forming geological backgrounds, and the discordogenic fault, mafic-ultramafic intrusion, high MgO primitive magma （high-MgO basaltic magma）, deep magmatism, sulfur saturation and sulfide segregation are 6 important geological conditions for the magmatic nickel deposits.

Data-driven resource allocation with trac load prediction

Wireless big data is attracting extensive attention from operators,vendors and academia,which provides new freedoms in improving the performance from various levels of wireless networks.One possible way to leverage big data analysis is predictive resource allocation,which has been reported to increase spectrum and energy resource utilization eciency with the predicted user behavior including user mobility.However,few works address how the trac load prediction can be exploited to optimize the data-driven radio access.We show how to translate the predicted trac load into the essential information used for resource optimization by taking energy-saving transmission for non-real-time user as an example.By formulating and solving an energy minimizing resource allocation problem with future instantaneous bandwidth information,we not only provide a performance upper bound,but also reveal that only two key parameters are related to the future information.By exploiting the residual bandwidth probability derived from the trac volume prediction,the two parameters can be estimated accurately when the transmission delay allowed by the user is large,and the closed-form solution of global optimal resource allocation can be obtained when the delay approaches in nity.We provide a heuristic resource allocation policy to guarantee a target transmission completion probability when the delay is no-so-large.Simulation results validate our analysis,show remarkable energy-saving gain of the proposed predictive policy over non-predictive policies,and illustrate that the time granularity in predicting trac load should be identical to the delay allowed by the user.