2011~2020年勘查地球化学数据处理研究进展

我国建立了包含海量数据的高质量的勘查地球化学数据库,为矿产勘查、环境评价和地质调查等提供了重要的数据支撑。如何高效处理勘查地球化学数据,并从中发掘和识别深层次信息一直是勘查地球化学学科研究的热点和前沿领域。本文在系统调研国内外学者过去十年发表的论著基础上,对勘查地球化学数据处理方法进行分析与对比,从勘查地球化学数据库建设、地球化学异常识别及其不确定性评价等方面概述了我国近十年来在该领域取得的主要研究进展,包括:(1)分形与多重分形模型由于考虑了地球化学空间模式的复杂性和尺度不变性,在全球范围内得到极大的发展和推广,我国学者引领了基于分形与多重分形的勘查地球化学数据处理;(2)机器学习和大数据思维开始在该领域启蒙,并迅速得到关注,正在成为研究热点和前沿领域,我国学者率先开展基于机器学习算法的勘查地球化学大数据挖掘研究;(3)我国学者需要进一步加强勘查地球化学数据缺失值处理以及成分数据闭合效应研究。今后该领域应进一步加强对弱缓地球化学异常识别、异常不确定性评价以及异常识别与其形成机理相结合等方面的研究。     

基于欧拉反褶积法的无人机航磁找矿应用

无人机航磁测量以其高效、安全、低成本等优点逐渐应用到中小面积大比例尺资源勘探领域。本文针对安徽芜湖市三山区22 km²区域构造刻画和找矿前景预测,开展1∶20 000高精度无人机航磁测量工作;推导了欧拉反演求解过程,并分析对比构造指数选取,使用构造指数为0的欧拉反褶积对航磁数据进行反演求解,获得地下磁异常体构造边界解集信息;将其应用到研究区域航磁数据反演,根据反演得到的边界位置和深度信息圈定了18处断裂,并结合异常特征给出了8.7 km²岩浆岩分布区,根据宁芜区域铁矿成矿特点,圈定岩浆分布区及边界为成矿详查区。     

新元古代微生物成锰作用——来自扬子北缘城口地区陡山沱组锰质叠层石的证据

叠层石菱锰矿被认为是生物成锰的证据,但少见相关报道.扬子北缘城口地区陡山沱组菱锰矿层中叠层石十分发育,但研究程度较低,其生长机制不明确,其是否是叠层石或者是附枝藻(Epiphyton)仍具有争议.从岩心描述与薄片观察出发,详细描述了研究区锰质叠层石的宏观和微观特征,根据其发育规模及形态特征将其划分为6种类型:微型分叉柱状叠层石、微型指状叠层石、指状叠层石、层状叠层石、柱状叠层石及层—柱状叠层石.研究表明,研究区柱状锰质叠层石直径一般小于l cm,具同步生长特征,与假裸枝叠层石(Pseudogymnosolenaceae)特征相似.研究区微小型、宏体型叠层石基本层常呈有立柱结构的板状,这种基本层由树形石及微型叠层石组成,而不是附枝藻.叠层石与核形石、鲕粒共生,形成于碳酸盐岩台地边缘礁滩及滩后泻湖环境.研究区锰质叠层石垂向演化从指状向层状演化为主,指示陡山沱组沉积末期水体逐渐变浅.锰质叠层石微观结构以凝块结构为主,具有丝状、球状和放射状等微生物岩显微结构.锰质叠层石记录了碳酸锰精美的原始沉积结构,暗示微生物参与了锰元素的沉积成矿作用.     

基于地学规律的科尔沁沙地土地整治与生态修复规划方法

内蒙古科尔沁沙地历史上曾是科尔沁大草原,现在是农牧交错区,该区的土地利用关系到生态安全、国家粮食安全和农民生计。该区域“坨甸相间”地貌特点造成不同地形部位土壤含水量存在差异,降水量年际变化大。通过卫星影像数据解译出土地利用/覆被类型图与土壤图进行叠加,获得了显性沙地和隐性沙地数量与分布信息。首先保留耕地以外的其他地类,再利用卫星影像数据反映不同降水年型耕地的归一化植被指数(NDVI),将偏欠水年NDVI低于平均值的耕地退耕还草,消除这些耕地的冬春沙化风险,以进行土地利用布局调整。这种直接利用遥感数据获取土地利用/覆被类型和耕地的NDVI进行土地利用空间布局调整的方法,十分简单实用。本文还根据地貌和沙地降水入渗快的特点,提出了与高标准农田建设模式不同但与区域地理环境相吻合的农田整治模式,即利用自然沙丘作为区域风沙防护工程,土地开发不做大平整,不修建灌溉系统,发展雨养农业。以上根据区域地理环境条件进行土地利用布局和确定农田开发建设模式,可将农业生产与荒漠化防治和谐结合在一起,实现土地可持续利用。     

基于KronosFlow软件对逆冲推覆带构造-热演化的初探

盆地模拟是含油气盆地勘探评价的重要技术方法之一,然而传统的盆地建模2D模拟技术仅适用于拉张型盆地,使得挤压型盆地横向上的热史重建一直是个难题,从而制约了进一步的油气勘探。目前由法国Beicip-Franlab公司设计的KronosFlow软件突破了传统盆模软件对逆冲推覆带、盐构造和泥底辟等复杂构造的局限性,精确控制了横向和垂向上的构造位移,通过跟踪单个网格连续运动形态、恢复侧向变形量以及与TemisFlow软件无缝衔接,实现了对挤压型盆地埋藏史、热史、生排烃史以及油气运聚史的恢复。以塔里木盆地北缘柯坪逆冲推覆带和库车逆冲推覆带为例,利用KronosFlow软件定量反演了新生代以来的构造-热演化历史,并以实测数据约束并验证挤压环境下盆地模拟结果的有效性。柯坪逆冲推覆带的依木干他乌断层在40~30 Ma再次活动,断层附近志留系—泥盆系的温度大于85 ℃,柯坪塔格断层在15~10 Ma活动,断层附近志留系—泥盆系的温度小于70 ℃,寒武系（烃源岩成熟度为1.3%~1.7%）和奥陶系（烃源岩成熟度为0.7%~1.2%）烃源岩热演化程度高,生烃能力强。库车逆冲推覆带侏罗系北段温度介于50~70 ℃,南段温度介于210~230 ℃,盐构造造成地温异常,其中秋里塔格构造带膏盐岩最厚,降温效果最明显。      

沁水煤田15号煤中氟的赋存状态及其沉积环境分析

氟是煤中有害元素之一,燃煤型氟排放造成的污染已经对人类的健康安全构成威胁。研究煤中氟赋存状态及其沉积环境,对掌握高氟煤分布规律及控制燃煤造成的氟排放具有重要意义。通过对沁水煤田内6座煤矿15号煤样品的分析发现:煤中氟含量与灰分显著相关,与黄铁矿硫、有机硫无相关关系,表明煤中氟主要以无机物形式赋存,且与含硫矿物无关;与钾、镁、硅、铝等元素含量具有显著的相关关系,表明氟有可能的主要赋存形式为金云母、氟金云母等矿物,以及以吸附存在于高岭石、勃姆石、伊蒙混层等黏土矿物中;与钙、磷元素含量无显著相关性,表明氟不以氟磷灰石、含氟羟基磷灰石等矿物大量赋存。此外,通过对沁水煤田15号煤的沉积环境分析发现,弱还原环境、较低盐度、偏酸性、较强的水动力条件有利于该区域煤中氟的富集。     

Homogeneity and mechanical behaviors of sands improved by a temperature-controlled one-phase MICP method

Acta Geotechnica - Microbially induced carbonate precipitation (MICP) has been actively investigated as a promising method to improve soil properties. A burning issue impeding its wide application...     

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.     

Basal stability analysis of braced excavations in anisotropic and non-homogeneous undrained clay using streamline velocity fields

Acta Geotechnica - A basal heave stability analysis in anisotropic and non-homogeneous undrained clay by using the kinematic approach of limit analysis is presented. The proposed failure mechanism...