Establishment of water source discrimination model in coal mine by using hydrogeochemistry and statistical analysis： a case study from Renlou Coal Mine in northern Anhui Province, China

The demand for energy consumption promotes to find more coal in deep underground up to 1 000 m and brings more serious situation of water disaster. As one of the major methods for water disaster control, hydrogeochemistry attracts a series of studies related to water source discrimination. In this paper, a simple method for constructing the water source discrimination model based on major ions and multivariate statistical analysis was reported using the following procedures： （1） collection of data and interpretation, （2） analysis of controlling factors based on the chemical composition of groundwater, （3） ＂pure＂ sample chosen, and （4） discrimination model establishment. After the processes, two functions and a diagram were established for three aquifers （the Quaternary, Coal bearing, and Taiyuan Fm.） from the Renlou Coal Mine in northern Anhui Province, China. The method can be applied in almost all coal mines and can be used for evaluating the contribution ratios if the water is collected from a mixing source.

Learning a discriminative high-fidelity dictionary for single channel source separation

Sparse-representation-based single-channel source separation,which aims to recover each source’s signal using its corresponding sub-dictionary,has attracted many scholars’attention.The basic premise of this model is that each sub-dictionary possesses discriminative information about its corresponding source,and this information can be used to recover almost every sample from that source.However,in a more general sense,the samples from a source are composed not only of discriminative information but also common information shared with other sources.This paper proposes learning a discriminative high-fidelity dictionary to improve the separation performance.The innovations are threefold.Firstly,an extra sub-dictionary was combined into a conventional union dictionary to ensure that the source-specific sub-dictionaries can capture only the purely discriminative information for their corresponding sources because the common information is collected in the additional sub-dictionary.Secondly,a task-driven learning algorithm is designed to optimize the new union dictionary and a set of weights that indicate how much of the common information should be allocated to each source.Thirdly,a source separation scheme based on the learned dictionary is presented.Experimental results on a human speech dataset yield evidence that our algorithm can achieve better separation performance than either state-of-the-art or traditional algorithms.

Hydrogeochemical Characteristics and Groundwater Inrush Source Identification for a Multi-aquifer System in a Coal Mine

Correct identification of water inrush sources is particularly important to prevent and control mine water disasters.Hydrochemical analysis,Fisher discriminant analysis,and geothermal verification analysis were used to identify and verify the water sources of the multi-aquifer groundwater system in Gubei coal mine,Anhui Province,North China.Results show that hydrochemical water types of the Cenozoic top aquifer included HCO3-Na+K-Ca,HCO3-Na+K-Mg and HCO3-Na+K,and this aquifer was easily distinguishable from other aquifers because of its low concentration of Na++K+and Cl-.The Cenozoic middle and bottom aquifers,the Permian fissure aquifer,and the Taiyuan and Ordovician limestone aquifers were mainly characterized by the Cl-Na+K and SO4-Cl-Na+K or HCO3-Cl-Na+K water types,and their hydrogeochemistries were similar.Therefore,water sources could not be identified via hydrochemical analysis.Fisher model was established based on the hydrogeochemical characteristics,and its discrimination rate was 89.19%.Fisher discrimination results were improved by combining them with the geothermal analysis results,and this combination increased the identification rate to 97.3%and reasonably explained the reasons behind two water samples misjudgments.The methods described herein are also applicable to other mines with similar geological and hydrogeological conditions in North China.

Cepstrum analysis of seismic source characteristics

This paper introduces the concept of cepstrum. By investigating the difference in source characteristics between earthquakes and explosions the paper infers the manifestation of source difference in various variable domains, and seeks for effective means to express such source difference. Extending the approach of source discrimination from time and frequency domain to the cepstrum domain, the paper proposes a method of cepstrum analysis for recognizing the characteristics of seismic sources and establishes criteria for identifying the type of seismic sources. Cepstrum analysis on some recent earthquakes and explosions has been made, and the result shows that the method is quite effective in practice.

Supply of terrigenous organic matter from tidal flat to the marine environment:An example of neritic source rocks in the Eocene Pinghu Formation,Xihu Depression,East China Sea Shelf Basin

The terrigenously-dominated marine shales which were deposited in the lower Eocene Pinghu Formation were thought to be a potential source rock in the Xihu Depression of the East China Sea Shelf Basin.However,the exceptionally high total organic carbon content(TOC,>6%on average)of the tidal sand ridge samples was not compatible with their sedimentary environment,indicating coal-bearing sedimentary debris may have been transferred from the coast to the ocean.In this study,new sights into the origins and supply of organic materials in the coastal environment were proposed in the neritic organic matter of the Eocene Pinghu Formation.A discriminant model was developed using plynofacies analysis data to pinpoint the source of organic materials in marine source rocks.The discrimination results suggested that marine mudstones were associated with tidal flat mudstones rather than deltaic ones.The biomarker characteristics of mudstones deposited in various environments support this assertion,indicating that the supply of plant materials in tidal flats is the primary organic matter source for the marine environment.The organic matter abundance was elevated in tidal flats due to their superior preservation conditions.Additionally,the lithological assemblage of tidal flats suggests that tidal currents can scour marshes and then transport dispersed terrigenous organic materials to neritic areas.These findings indicate that coal-bearing sedimentary debris was likely transferred from the coast to the ocean,and tidal currents are thought to be the dominant mechanism driving organic matter from the tidal to the marine environment.

Artificial neural network algorithm for pulse shape discrimination in 2πα and 2πβ particle surface emission rate measurements

To enhance the accuracy of 2πα and 2πβ particle surface emission rate measurements and address the identification issues of nuclides in conventional methods, this study introduces two artificial neural network(ANN) algorithms: back-propagation(BP) and genetic algorithm-based back-propagation(GA-BP). These algorithms classify pulse signals from distinct α and β particles. Their discrimination efficacy is assessed by simulating standard pulse signals and those produced by contaminated sources, mixing α and β particles within the detector. This study initially showcases energy spectrum measurement outcomes, subsequently tests the ANNs on the measurement and validation datasets, and contrasts the pulse shape discrimination efficacy of both algorithms. Experimental findings reveal that the proportional counter's energy resolution is not ideal, thus rendering energy analysis insufficient for distinguishing between 2πα and 2πβ particles. The BP neural network realizes approximately 99% accuracy for 2πα particles and approximately 95% for 2πβ particles, thus surpassing the GA-BP's performance. Additionally, the results suggest enhancing β particle discrimination accuracy by increasing the digital acquisition card's threshold lower limit. This study offers an advanced solution for the 2πα and 2πβ surface emission rate measurement method, presenting superior adaptability and scalability over conventional techniques.

Single Dose Effects of Pascoflair^(█) on Current Source Density(CSD)of Human EEG

Pascoflair? 425 mg is a herbal drug based on Passiflora incarnata dry extract and is registered in different countries for the treatment of nervous restlessness and anxiety and also as an aid to sleep. The study was initiated for the quantitative assessment of the effect of this preparation on brain electric activity and cognition in human volunteers. Quantitative electroencephalographic current source density (CSD) from 16 healthy male and female human volunteers (average age 49 years) was used in a randomized, placebo-controlled crossover study. Data were taken 0.5, 1.5, 3 and 4 hours after administration of the preparations under the conditions of 6 minutes eyes open, 5 minutes d2 concentration test, mathematical calculation test and memory test respectively. During mental load, changes in spectral band power were used to analyse drug-induced effects. All variables were fed into a linear discriminant analysis (LDA) for comparison with other drug profiles. Spectral power in the delta and theta range was significantly attenuated at 3 and 4 hours after administration in comparison with the time-dependent increase normally observed due to circadian rhythm. Discriminant analysis revealed a difference to placebo for all recordings as early as 30 minutes after intake of 3 coated tablets of Pascoflair? 425 mg. Using LDA data location within the poly-dimensional space, verum was projected into the area of the effects of Gingko/Ginseng as reference drugs tested earlier under identical conditions. Psychometric performance was not disrupted. Pascoflair? 425 mg can be regarded as a well characterized plant-derived drug with anxiolytic and calming properties without negative sedative and cognition-attenuating side effects. Current results document the effecttiveness of the preparation as early as after 30 minutes. In addition, they indicate persistence of good mental performance for hours. Trial registration: the study has been registered at ClinicalTrials.gov under NCT01047605.

OSS Project Assessment Based on Discriminant Analysis and Jump Diffusion Process Model for Fault Big Data

The bug tracking system is well known as the project support tool of open source software. There are many categorical data sets recorded on the bug tracking system. In the past, many reliability assessment methods have been proposed in the research area of software reliability. Also, there are several software project analyses based on the software effort data such as the earned value management. In particular, the software reliability growth models can apply to the system testing phase of software development. On the other hand, the software effort analysis can apply to all development phase, because the fault data is only recorded on the testing phase. We focus on the big fault data and effort data of open source software. Then, it is difficult to assess by using the typical statistical assessment method, because the data recorded on the bug tracking system is large scale. Also, we discuss the jump diffusion process model based on the estimation method of jump parameters by using the discriminant analysis. Moreover, we analyze actual big fault data to show numerical examples of software effort assessment considering many categorical data set.

浅海环境中用于目标深度属性判别的线谱起伏特征量分析

浅海环境中目标辐射线谱的起伏特征可用于水面、水下目标深度属性判别.已有研究未给出表征水面、水下线谱起伏差异的物理量性质,导致利用不同物理量进行目标深度属性判别时性能不佳.针对这一问题,本文从声源深度起伏所导致的线谱起伏出发,采用微分方法与统计方法对线谱起伏规律进行分析,研究了线谱起伏产生的物理机理,得到了适合用于水面、水下目标深度属性判别的物理量.首先,分别推导了复声压实部、声功率与声压幅值关于声源深度的导数关系式,得到了以上三种物理量在声源深度起伏时的起伏大小;其次,利用仿真实验数据从接收深度、声源水平距离、线谱频率等层面对理论推导进行验证,并对线谱的归一化起伏特征进行了分析.最后,利用海试数据验证了相关结论.结果表明,当声源的深度起伏时,水面、水下线谱的起伏差异源自各阶模态的相互作用.声源频率、声源水平距离、接收深度三种因素决定了模态间相互作用的性质.声压幅值适用于构建目标深度属性判别特征量,可用于表征低频线谱的强度起伏差异.数值仿真和海试数据处理结果表明,相较于声压实部和声功率,利用声压幅值构建的归一化水面、水下目标深度属性判别特征量具有更优性能.

基于Fisher判别方法计算断层破裂面积的研究

实时计算并快速得到破坏性地震断层破裂分布范围可及时向潜在破坏区发出预警信息,为大震预警提供重要依据。本研究基于我国发生的5个重大破坏性地震的强震观测数据,利用Fisher线性判别方法,拟合出能够有效将台站记录进行分类的线性判别函数。应用该方法对2008年汶川地震断层破裂面积进行模拟计算,持续更新震后5~74s断层破裂面积,反演得到的断层破裂分布区域与野外地质调查结果基本一致。通过对实际震例测试分析表明,该方法反演的结果能够较准确地覆盖断层破裂区域,震后快速计算的断层破裂分布区域能够为地震预警及后续的烈度速报产出结果进行实时修正。

基于锶同位素的潘二煤矿相邻灰岩含水层突水水源判识模型研究

矿井突水是采矿生产过程中威胁最大的地质灾害之一,快速有效地判别突水水源是预防矿井水害的关键所在。通过分析潘二煤矿含水层的水化学性质,开展了相邻水岩水锶同位素的测试与分析,选取^(87)Sr/^(86)Sr、Ca^(2+)、Na^(+)+K^(+)、Mg^(2+)、HCO_(3)^(-)、SO_(4)^(2-)、Cl^(-)7个判别指标,结合主成分分析与Fisher理论、主成分分析与距离判别理论、主成分分析与BP神经网络,分别建立基于锶同位素的混合水源判别模型(Sr-F模型、Sr-D模型、Sr-B模型),利用模型对未知水样进行判识。结果表明:基于锶同位素的Sr-B判识模型的判识效果最好,其准确率达到95%;基于主成分分析与BP神经网络突水水源判别模型能够有效地提高突水水源识别精度,能准确地判识相邻灰岩含水层突水水源,为矿井安全生产提供保障。

耦合主成分分析下煤矿矿井突水水源识别方法

煤矿矿井突水会导致矿井内部水位迅速上升,对煤矿生产造成严重影响,甚至导致煤矿停产。开展矿井突水水源识别可以及早发现突水风险,从而保障煤矿生产和矿工的生命安全。为此,提出一种耦合主成分分析下煤矿矿井突水水源识别方法。根据水源化学各组分间差异性确定突水水源判别指标变量,从煤矿矿井含水层随机选取18个水样化学信息,通过耦合主成分分析获得各水源组间相关性,提取4个重要的主成分表征所有样本信息特征,结合协方差矩阵、Fisher判别得到各类型水源组别的中心距离,识别出突水水源类型。实验结果表明,所提方法能够解决样本信息各变量间信息叠加问题,提升了突水水源识别准确率。

改进Fisher判别法的突水水源快速判别模型

为快速有效地判别矿井突水水源位置,预防水害事故的发生,根据平煤五矿实际水文地质特征,针对砂岩、太灰和寒灰含水层水质差异性,收集34组水样资料,并选取K^(+)+Na^(+)、Ca^(2+)、Mg^(2+)、SO_(4)^(2-)、Cl^(-)、HCO_(3)^(-)六大常规水化学离子作为判别因子,开展水化学特征分析;结合PCA降维统计算法,建立改进的Fisher水源判别模型,并利用待测样本对比改进前后Fisher模型的判别结果,同时将训练样本回代到改进模型中进行验证。结果表明,根据水化学类型无法准确区分寒灰水与太灰水;利用改进Fisher判别模型测试10组待测样本,判别准确率为100%,相较于基础Fisher模型,准确率提高了20%,应用改进Fisher判别模型可大幅提升水源识别准确率;已知训练样本的回代结果显示,改进Fisher判别结果与实际情况基本吻合。通过2种模型的对比分析,采用改进Fisher模型进行矿井水源识别准确率及可靠性高,具有一定研究价值,可为矿井水源识别提供新的思路。

Optimal reduction of anthropogenic emissions for air pollution control and the retrieval of emission source from observed pollutants II: Iterative optimization using a positive-negative discriminant

In part Ⅰ of this paper series, the application of an incomplete adjoint operator to calculate the optimal reduction of the total emissions S was suggested. This paper, part Ⅱ of the series, focuses on calculating the reduction of the anthropogenic emission source S. As aSc(the source term due to the chemical reaction in the atmosphere) is a complex function of the pollutant concentration, we propose an iterative optimization method using a positive-negative discriminant to obtain Sfrom he aS. Tconvergence of the iterations is also proven in this paper.

基于多方法耦合的矿井突水水源识别方法研究

为有效预防矿井突水事故发生,快速准确识别矿井突水水源是至关重要。以陕西某矿为例,选取K^(+)+Na^(+),Ca^(2+),Mg^(2+),Cl^(-),SO_(4)^(2-),HCO^(-)_(3)六种水化学成分指标作为矿井突水水源识别的样本变量。笔者采用主成分分析(PCA)结合Fisher判别分析法建立突水水源判别,利用不同的水化学特征样本中的27个数据为训练样本,对该方法进行检验和应用,并与传统的Fisher判别分析法的结果进行比较。研究结果表明:利用Fisher判别分析正确率为92.6%,而利用PCA与Fisher突水水源判别方法对样本数据进行判别,正确率达96.3%,并对10个预测样本数据进行判别,正确率100%,判别结果正确率较高,对煤矿突水水源识别具有指导意义。

柳家村隧洞水化学特征分析及涌水水源判别方法研究

柳家村隧洞作为滇中引水工程的重要组成部分,在开挖过程中需穿越高水位富水地层,因而容易发生突涌水事故。隧址区地处红层,地下水作为当地居民生产生活的重要水源,因此揭示研究区地下水水化学特征,构建涌水判别模型,对于当地地下水资源的开发利用具有重要意义。分别从3个不同含水层中采集了11组地下水水样以及2个施工支洞的2组涌水水样,运用Piper三线图、离子比例系数、水化学指标相关性分析等方法揭示了区域地下水水化学特征;以主成分分析法和Fisher判别法作为基础模型,构建了一种由R型聚类分析、主成分分析、Fisher判别分析三者耦合的涌水水源快速判别模型,该模型可以较好地适用于水样数据较少的地区;此外,利用柳家村隧洞的实际样本数据,对该模型进行了验证,并与主成分分析、Fisher判别分析两者结合的方法进行了对比。结果表明:地下水中离子成分主要来源于地层中的方解石、白云石、石膏、芒硝等矿物的溶解;利用本文提出的涌水水源快速判别模型判别涌水水源的精度相比于主成分分析、Fisher判别分析两者耦合的方法提高了9.1%。

面向燃气电站的室内外判别及一体化多源融合人员定位方法

针对燃气电站金属结构多、设备密集及安全管理要求高等特点,结合燃气电站安全管理对人员定位精度高和稳定可靠等要求,提出了一种面向燃气电站的室内外判别以及一体化多源融合定位方法。室内外判别方法综合卫星高度角以及信号信噪比设计了一种判别指标,基于该指标设定合理阈值,可实现人员室内外判别。考虑到室外装置区附近容易受金属干扰和遮挡影响卫星定位的问题,提出了一种多源融合定位方法,将卫星定位与微惯性系统、地磁定位及蓝牙无线信号进行融合,实现了在复杂工业场景下的稳定可靠定位。该方法计算量小、定位连续且定位精度高。最后采用智能手机在实际场景进行了验证,结果表明,室内外切换准确,切换时间小于3 s,室内定位误差1.35 m,室外定位误差3.6 m。

基于多源特征融合的高速公路路网交通状态判别

为满足管理者和出行者全面掌握高速公路路网交通状态的需求,以高速公路路网数据和收费数据为基础,采用多源特征融合的方式构建高速公路路网交通状态判别模型。首先,将高速公路路网划分为路段,提取路段的静态特征和动态特征,以多源特征融合的方式全面刻画路段特征;然后,采用自定义规则结合人工标注的半自动化数据标注方式对数据进行标注,保证原始数据标注的准确性;最后,针对数据不均衡的问题,提出由两层随机森林模型组成的双随机森林模型,利用第二层随机森林模型提升第一层随机森林模型分类的准确性。为了证明所提方法的有效性和准确性,收集了辽宁省高速公路的路网数据和收费数据对模型的效果进行验证,结果显示:畅通状态的准确率为100.0%,基本畅通状态的准确率为94.0%,轻度拥堵状态的准确率为98.0%,中度拥堵状态的准确率为94.0%,重度拥堵状态的准确率为97.0%,全网交通状态的平均准确率为96.6%。

巷道电性源瞬变电磁响应三维数值模拟

近年来地面电性源得到成功应用,巷道电性源瞬变电磁探测将成为热点。本文采用三维时域有限差分法,对巷道底板以及巷道与巷道之间异常体的电性源瞬变电磁响应进行了模拟。模拟结果表明:对于巷道底板异常体,受异常体表面电荷影响,电场水平分量和垂直分量在异常体表面剧烈变化,水平电场对低阻体的分辨能力较强,对高阻体的分辨能力较弱,低阻体与高阻体的垂直电场响应的符号相反;低阻体和高阻体的∂B_(y)/∂t响应在早、晚期的符号相反,低阻体响应在早期的符号为负,晚期为正,而高阻体响应在早期的符号为正,晚期为负。对于巷道与巷道之间的异常体,水平电场对低阻体的分辨能力较强,对高阻体的分辨能力较弱;高阻体模型的∂B_(z)/∂t响应曲线高于背景曲线,低阻体模型的响应曲线低于背景曲线,说明磁场∂B_(z)/∂t响应对高阻体和低阻体均有分辨能力。

BP神经网络判别模型在中关铁矿突水水源预测中的应用

中关铁矿属国内典型大水矿床,其地下水系统相对复杂,为保障工作人员的生命财产安全,需及时分析、准确预测矿井生产过程中突水水源。依据在中关铁矿三个开采水平面现有出水点采集水样的水质测试结果,分析中关铁矿地区水质特征,以BP神经网络判别模型建立矿井水源判别模型,根据测试结果数据分析,BP神经网络判别模型对于奥陶系灰岩水与闪长岩裂隙水判别正确率为100%、对于混水判别的正确率为80%。BP神经网络判别模型在中关铁矿突水水源预测中具有较高的准确性,为防治水提供理论依据。