Down‐slope facies variability within deep‐water channel systems: Insights from the Upper Cretaceous Cerro Toro Formation,southern Patagonia

In southern Patagonia, outcrops of the Upper Cretaceous Cerro Toro Formation preserve a >150 km long deep‐water axial channel belt in the Magallanes–Austral Basin, providing a unique opportunity to investigate longitudinal variations in the depositional characteristics of a deep‐water channel system. This study documents sedimentological, stratigraphical and geochronological data from the Cerro Toro Formation in the Argentine sector of the basin. New results are integrated with previous work from the Chilean basin sector to conduct a basin‐scale comparison of the timing of deposition, provenance and lithofacies proportions. The Cerro Toro channel belt includes a nearly 1000 m thick section characterized by high‐density turbidites and mass‐wasting deposits. Two ash beds from the base of the section yield U–Pb zircon ages of 90·4 ± 2 Ma and 88·0 ± 3 Ma, indicating similar initiation ages as documented in the Chilean sector. The U–Pb detrital zircon age spectra from samples in the study area reveal similar provenance trends to samples from the Chilean basin sector, with peak age populations at 310 to 260 Ma, 160 to 135 Ma and 110 to 82 Ma. The maximum depositional age of the channel belt in the Argentine sector is 87·8 ± 1·5 Ma and all new geochronology data corroborate an 86 to 80 Ma depositional age for the main Cerro Toro channel belt. Statistical analyses of 7370 beds from nearly 8000 m of new and previously published stratigraphic sections along the entire outcrop belt suggest progressive variations in the down‐system proportion of lithofacies. In the up‐slope region, lithofacies representing mass wasting processes (for example, debris‐flow and mass‐transport deposits) account for ca 29% of the stratigraphic thickness, as opposed to 5% in the down‐slope region of the channel belt, where turbidity current deposits are more prevalent. The proportion of beds >1 m thick also decreases systematically down slope, particularly for conglomeratic turbidite deposits. This work highlights that: (i) the proportion of thick beds and distribution of lithofacies are key down‐system changes in the stratigraphic fill of this deep‐water channel belt; (ii) detrital zircon trends suggest a relatively well‐mixed longitudinal depositional system; and (iii) geochronology of the main Cerro Toro outcrop belt supports but does not necessitate the model of a single, roughly age‐equivalent, channel system. This study has implications for understanding the downslope variability in depositional processes, stratigraphic architecture and reservoir quality of submarine channel systems.     

大数据环境下内蒙古浩布高地区铅锌多金属矿智能矿产预测研究

传统机器学习算法已广泛应用于矿产预测,但面对地质大数据的高维稀疏、不平衡小样本等特性仍缺乏有效处理和分析的方法,设计适合地质大数据特点的机器学习算法是智能矿产预测亟需解决的新问题。本文以内蒙古浩布高地区的铅锌多金属矿产预测为例,提出了一种面向地质大数据的半监督协同训练矿产预测模型。首先对研究区地质找矿信息和地球化学异常信息进行定量分析,提取断裂构造、二叠系地层、燕山期侵入岩、地层与岩体接触带、围岩蚀变及Pb、Zn、Sn、Cu地球化学异常共9种找矿因子。然后利用递归特征消除法优选找矿因子组合,不包括Sn异常在内的8个找矿因子组合被选为最优组合。最后,利用支持向量机和随机森林算法作为基分类器进行半监督协同训练矿产预测,绘制成矿概率分布图。ROC曲线和预测度曲线分析结果表明,半监督协同训练模型的AUC值和预测效率都高于随机森林和支持向量机模型。研究结果也为大数据环境下的智能矿产预测提供了一种新的思路。     

基于岩石图像深度学习的岩性自动识别与分类方法

岩石岩性的识别与分类对于地质分析极为重要,采用机器学习的方法建立识别模型进行自动分类是一条新的途径。基于Inception-v3深度卷积神经网络模型,建立了岩石图像集分析的深度学习迁移模型,运用迁移学习方法实现了岩石岩性的自动识别与分类。采用此方法对所采集的173张花岗岩图像、152张千枚岩图像和246张角砾岩图像进行了学习和识别分类研究,通过训练学习建立岩石图像深度学习迁移模型,并分别采用训练集和测试集中的岩石图像对模型进行了检验分析。对于训练集中的岩石图像,每组岩石分别用3张图像测试,三种岩石的岩性分类均正确,且分类概率值均达到90%以上,显示了模型良好的鲁棒性;对于测试集中的岩石图像,每组岩石分别采用9张图像进行识别分析,三种岩石的岩性分类均正确,并且千枚岩组图像分类概率均高于90%,但是花岗岩组2张图像和角砾岩组的1张图像分类概率值不足70%,概率值较其他岩石图像低,推测其原因是训练集中相同模式的岩石图像较少,导致模型的泛化能力减小。为了提高识别精确度,对准确率较低的岩石图像进行截取,分别取其中的3张图像加入训练集进行再训练,增加与测试图像具有相同模式的训练样本;在新的模型中,对3张图像进行二次检验,测试概率值均达到85%以上,说明在数据足够的状况下模型具有良好的学习能力。与传统的机器学习方法相比,所提出的岩石图像深度学习方法具有以下优点:第一,模型通过搜索图像像素点提取物体特征,不需要手动提取待分类物体特征;第二,对于图像像素大小,成像距离及光照要求低;第三,采用适当的训练集可获得较好的识别分类效果,并具有良好鲁棒性和泛化能力。     

卷积神经网络及其在矿床找矿预测中的应用——以安徽省兆吉口铅锌矿床为例

大数据人工智能地质学刚刚起步,基于大数据智能算法的地质研究是非常有意义的探索性实验。利用大数据和机器学习解决矿产预测问题,有助于人们克服不能全面考虑地质变量的困难及评估当前模型在已有数据中的可靠性。元素地表分布特征量主要受原岩成分、成矿作用影响和地表过程的影响,它们携带某些指示矿体就位的信息,即矿体在地下空间就位时在地表的响应,且未在地表过程中消失。以往的地球化学勘查工作仅仅识别异常,但未能发现矿体在地表响应的成矿特征量。本文以安徽省兆吉口铅锌矿床为例,通过机器学习,利用卷积神经网络算法,不断挖掘元素Pb分布特征与矿体地下就位空间的耦合相关性。经过1000次训练后,可以得到准确率0. 93,损失率0. 28的卷积神经网络模型。这种神经网络模型就是矿体在地下就位时元素在地表分布的响应,可以用来进行矿产资源预测。应用该模型对未知区进行预测,结果显示第53号区域具有很大概率存在尚未发现的矿体。     

月表构造与深部构造的关系

地球和月球很可能是通过大撞击形成的。在行星地质学中,研究月球的地质-构造现象,对了解月球、地球乃至太阳系的形成与演化历史都有很大帮助。月球的构造分为深部构造与月表构造,寻找它们在分布或成因上的关系,可以为月球甚至地月系的起源和演化历史提供重要参考。利用LROC的宽视角影像数据以及LOLA数据提取解译月表构造,结合深大断裂进行观察分析,并对月球的撞击盆地进行统计,最后以静海地区为例分析构造分布特征,发现月球的质量瘤盆地中具有环状分布的月岭,外侧具有近环状分布的深大断裂,自前酒海纪至酒海纪,具备上述特征的质量瘤盆地占总撞击盆地的比例突然有一个很大的提升,且静海地区西部具有该构造分布特征。推测该特征与撞击、月海沉降等有关,且在酒海纪与雨海纪期间月球有较多的月海玄武岩分布,由此判断静海西部存在质量瘤,发生过撞击与月海沉降。     

综合物探方法在广西妙皇隐伏多金属矿勘查中的应用

妙皇多金属矿区位于来宾凹陷东缘与大瑶山隆起西侧交接部位,广西大瑶山及其西侧铅锌成矿带中部成矿条件优越,历年来开展过多种研究和找矿方法,但找矿效果不佳。通过研究妙皇铜铅锌银矿区的地质条件和成矿背景,运用综合物探方法,对区内部分地质问题及矿产特征进行了推断和预测。2012年,依据地质物探综合成果布置了ZK22701号验证孔,取得了突破性的进展。同时,依据物探成果推测了矿区成矿机理和成矿模式,在找矿方向和找矿潜力方面进行了分析探讨,对中深部找矿具有很好的指导意义。     

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.     

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.     

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.     

The effects of visual realism,spatial abilities,and competition on performance in map-based route learning in men

We report on how visual realism might influence map-based route learning performance in a controlled laboratory experiment with 104 male participants in a competitive context. Using animations of a dot moving through routes of interest, we find that participants recall the routes more accurately with abstract road maps than with more realistic satellite maps. We also find that, irrespective of visual realism, participants with higher spatial abilities (high-spatial participants) are more accurate in memorizing map-based routes than participants with lower spatial abilities (low-spatial participants). On the other hand, added visual realism limits high-spatial participants in their route recall speed, while it seems not to influence the recall speed of low-spatial participants. Competition affects participants’ overall confidence positively, but does not affect their route recall performance neither in terms of accuracy nor speed. With this study, we provide further empirical evidence demonstrating that it is important to choose the appropriate map type considering task characteristics and spatial abilities. While satellite maps might be perceived as more fun to use, or visually more attractive than road maps, they also require more cognitive resources for many map-based tasks, which is true even for high-spatial users.