浙江乐清湾海岸带功能区划分与海洋产业发展

乐清湾为浙江省重点海湾之，资源的多宜性与不同行业开发利用的矛盾冲突是当前海湾可持续发展面I临的主要障碍。本文从乐清湾海岸带地区的自然环境与社会经济条件出发，探讨了开展海岸带功能区划的必要性，提出了区划的基本方案。在分析乐清湾海岸带地区海洋产业发展现状与问题的基础上，运用层次分析法研究了该湾近期海洋产业的发展方向与主导产业。最后，讨论了实现海洋产业协调发展的若干对策与建议。     

时空协同的精准农业遥感研究

高分辨率遥感对地观测为我们从空间与时间2个维度客观反演地表格局—过程提供了有效的技术支撑。本文遵循时空协同的研究思路,基于高分辨率遥感影像,开展了农业遥感领域2个典型的问题研究：① 提出了一种基于影像视觉特征的耕地分区分层提取方法,该方法在利用DEM数据进行分区的基础上,根据不同区域内耕地所呈现的几何特征和纹理特征差异,分别设计了不同的耕地提取模型;② 构建了一种地块尺度的作物生长参数反演方法,方法以地块为基本单元,在空间、时间及属性组合约束下进行作物理化参数反演。本研究以贵州省安顺市西秀区和广西扶绥县耕地提取进行了耕地地块提取示范,以扶绥县进行了基于耕地地块和中空间分辨率时间序列遥感数据的甘蔗叶面积指数反演。其中,对于安顺市西秀区的耕地地块提取结果而言,形态精度（IoU）大于0.7的地块超过60%,规则耕地、梯田以及林草地等的类型精度均超过了80%;对于扶绥县甘蔗叶面积指数反演的结果而言,其结果可以较为精确地反映出基地甘蔗与非基地甘蔗的差异,基地甘蔗在品质上要优于非基地甘蔗。西南山地区的耕地形态提取/类型判别和地块甘蔗叶面积指数应用验证均证明了方法的可行性。结果表明,协同使用多源高分辨率数据是实现精准农业遥感研究的有效途径。     

信息技术应用与提升企业竞争优势关系的实证研究

探究了信息技术的应用与企业竞争优势提高的关系,以某制造企业在北美,欧洲,亚洲的3家企业实施ERP数据为例进行了实证分析.通过交货期衡量企业绩效,建立了多元回归分析模型.研究结果显示信息技术本身并不存在生产率悖论问题.3家企业在实施ERP之后订单交货期均缩短了,但这并不能够给企业带来长期的竞争优势,并从基于能力的竞争理论的角度分析了其中原因.     

Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data

Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m² (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas.     

Is being in work good for wellbeing? Work Integration Social Enterprises in regional Australia

ABSTRACT

Work Integration Social Enterprises (WISE) offer supported work environments for people experiencing disadvantage, including people with disability. This paper reflects on a research project that is mapping the ways in which social enterprises in regional Australian cities produce wellbeing for their employees. Through supported employment programs, these organisations are transforming individual lives, helping to build a sense of self-worth and purpose, and increase individuals’ social skills and capacities. Many of the perceived benefits are associated with a belief in the innate good of paid work. Based upon interviews with employees in the social enterprises, it is apparent that the hopes attached to providing ‘meaningful work’ are considerable. At the same time social enterprises are taking on social responsibilities that were once provided by the state, while also selling their services as employers of disabled and disadvantaged members of society and maintaining commercial viability of the enterprise. Using feminist political economy alongside discussion of the distributive economy, this paper explores how contemporary policy conditions, productivist biases and normative discourses about the value of wage work inflect the forms of wellbeing that can be experienced by WISE workers.     

岩性识别：方法、现状及智能化发展趋势

岩性识别是地质工作中一项基础而又重要的工作。传统的岩性识别方法过于依赖经验和地质专业知识积累,不仅耗时长、专业性强,还易受主观因素影响,导致准确率不理想。笔者等首先回顾了传统的岩性识别方法,之后总结了最新涌现的智能化识别方法,最后详细介绍了基于岩石图像、镜下图像、图像与元素信息融合等的智能识别方法。基于岩石图像的识别方法对于文中的岩石识别准确率可达90%以上,基于图像与元素融合的岩性识别方法可以缓解图像相似度高、风化破坏表观特征等因素对识别准确度的影响。笔者等认为当前岩性智能化识别研究仍处于初级阶段,无法满足工程需求。综合各类数据源的优势,利用机器学习深度挖掘岩石元素、矿物、光谱和表观特征间的内在关联性,有利于突破单源信息的局限性,实现岩性快速准确识别。     

实时智能地震处理系统在2020年四川石渠M5.6地震序列中的应用

实时智能地震处理系统（RISP）是一套基于深度学习技术的新型地震实时处理系统,于2021年6月在四川开始试运行。为了评估该系统在四川台网稀疏区域的地震检测和处理能力,笔者等采用RISP系统对2020年4月1日四川甘孜州石渠县发生的M5.6级地震序列进行了回溯性分析,获得了较为可靠的自动处理地震序列目录,并与人工目录进行了对比分析。震后10d内,RISP系统自动处理获得了386个地震信号,地震数量是正式地震目录的1.98倍,匹配率为97.43%。分析显示,发震时刻偏差小于±1.0 s的地震占比71.05%,震中位置偏差小于10 km的地震占比96.31%,震级偏差小于±0.3级的地震占比99.47%。根据RISP系统检测地震序列分布特征和震源机制解,认为此次地震的发震断层为五道梁—长沙贡玛断裂北东侧的一条未发现的分支断层。研究结果表明,RISP系统的应用可以提升台网稀疏区域的地震检测能力,可快速产出地震序列目录,对大震应急、震后趋势判定和发震构造研究等具有重要意义。     

基于BERT—BiLSTM—CRF模型的中文岩石描述文本命名实体与关系联合提取

地质调查正在从“数字化”走向“智能化”,需要在大数据思维的指导下,面向非结构化数据开展机器阅读和地质知识的自动提取。地学命名实体和关系联合提取是当前研究的难点和核心。本文采用基于大规模预训练中文语言模型的BERT—BiLSTM—CRF方法开展岩石描述文本命名实体与关系联合提取。首先,通过收集数字地质填图工作中的剖面测量和路线地质观测数据,建立岩石描述语料;然后,在岩石学理论指导下分析岩石知识组成,完成岩石知识图谱命名实体与关系的模式设计,标注岩石语料;最后,开展岩石描述语料知识提取的深度学习训练和消融试验对比。试验结果显示,大规模预训练中文语言模型（BERT）对岩石描述语料知识提取具有较高的适用性。推荐的BERT—BiLSTM—CRF模型方法对岩石命名实体与关系联合提取的准确率（F1值）为91.75%,对岩石命名实体识别的准确率（F1值）为97.38%。消融试验证明基于BERT的词嵌入层对岩石描述知识提取的性能提升影响显著,双向长短时记忆网络模型层（BiLSTM Layer）能提升实体关系联合提取性能。     

基于知识图谱的增强型混合式学习的教学实践与思考

随着互联网与信息技术的快速发展,线上线下混合式学习逐渐得到普及与应用。混合式学习强调学生的主体作用,注重学生的自主学习、协作学习和个性化学习,是良好的适合高等教育的创造性学习模式。但是在目前的混合式学习模式下,学生对知识的加工、梳理、整合能力不够,构建完整知识体系的自主学习能力稍显薄弱,因此亟需更多引导。文章综合分析了混合式学习模式的优缺点,建议引入人工智能领域的知识图谱概念,作为新的教学元素以引导和加强学生的自主学习过程,由此构建了基于知识图谱的增强型混合式学习模式。该模式在2020年南京大学《古无脊椎动物学》课程中进行了实践,进一步证明其在改善学生自主学习能力方面的潜力。     

基于机器学习和测井数据的碳酸盐岩孔隙度与渗透率预测

准确预测碳酸盐岩储层孔隙度和渗透率对于碳酸盐岩油气藏储层评价具有重要意义。碳酸盐岩储层裂缝与溶孔广泛发育,基于经验公式从测井曲线预测储层孔隙度和渗透率具有较大误差。以中东某碳酸盐岩油藏为研究对象,选取914块取心井岩心,测定孔隙度与渗透率,利用随机森林（RF）、K-近邻（KNN）、支持向量机（SVM）和长短期记忆网络（LSTM）4种不同机器学习方法,通过测井数据进行孔隙度与渗透率预测,优化机器学习参数,筛选出适用于碳酸盐岩油藏的测井孔隙度与渗透率预测方法。研究结果表明：4种机器学习方法预测储层孔隙度结果差异不大,通过调整输入参数种类,可进一步提高孔隙度与渗透率预测效果,当以补偿中子（NPHI）、岩性密度（RHOB）和声波时差（DT）3种测井参数数据作为输入时,基于LSTM的储层孔隙度预测精度最高,孔隙度预测结果均方根误差（RMSE）为4.536 2;由于碳酸盐岩储层的强非均质性,基于机器学习的测井储层渗透率预测效果较差,相对而言,仅以NPHI作为机器学习输入参数时,基于RF的储层渗透率预测精度最高,渗透率预测结果的RMSE为45.882 3。