General seismic wave and phase detection software driven by deep learning

We developed an automatic seismic wave and phase detection software based on PhaseNet, an efficient and highly generalized deep learning neural network for P- and S-wave phase picking. The software organically combines multiple modules including application terminal interface, docker container, data visualization, SSH protocol data transmission and other auxiliary modules. Characterized by a series of technologically powerful functions, the software is highly convenient for all users. To obtain the P- and S-wave picks, one only needs to prepare three-component seismic data as input and customize some parameters in the interface. In particular, the software can automatically identify complex waveforms (i.e. continuous or truncated waves) and support multiple types of input data such as SAC, MSEED, NumPy array, etc. A test on the dataset of the Wenchuan aftershocks shows the generalization ability and detection accuracy of the software. The software is expected to increase the efficiency and subjectivity in the manual processing of large amounts of seismic data, thereby providing convenience to regional network monitoring staffs and researchers in the study of Earth's interior.     

Investigation of waste disposal areas using electrical methods: a case study from Chania,Crete, Greece

A geophysical investigation of a landfill area was conducted as inextricable phase of a preliminary geotechnical, geological and hydrogeological study of the area under investigation. Electrical resistivity tomography (ERT), one of the most promising prospecting techniques mainly concerning its effective contribution to resolve several environmental problems, was applied for the geophysical modeling. ERT is a robust imaging method the theory and implementation of which are well documented in geophysical research literature. The survey site is located in Akrotirion Peninsula, about 7 km east of Chania city in Crete I. The geological setting comprises Trypalion limestones, marly limestones and a clayed weathered layer. Cavities are also identified at various scales which are recent tectono-karstic voids. Due to the above-mentioned geological singularity and in the light of the requirement for an environmentally safe construction of the landfill, an ERT survey was carried out. Specifically, seven geoelectrical tomographies were conducted reaching the prospecting depth of 36 m. The resulted images conduced, to overcome geotechnical problems since the spatial distribution of karstic voids was determined, to plan the future sites for waste disposal as the geological conditions were studied in detail and to reliably estimate the thickness of the already deposited wastes. The resulted images were confirmed using available borehole logs and ambient noise measurements.     

基于FPN Res-Unet的高分辨率遥感影像建筑物变化检测

针对城市土地资源变化检测工作繁杂、工作量大、自动化程度低等问题，本文提出一种基于深度学习模型的高分辨率遥感影像建筑物变化检测方法，将语义分割的思想引入到遥感变化检测。基于残差结构特征较卷积层提取性能更优和特征金字塔网络多尺度预测的特点，将残差结构和特征金字塔网络融合到Unet模型中，建立FPN Res-Unet模型。该模型以Unet为基础，引入ResNet18的残差结构作为编码路径特征提取层，在每次卷积后使用边界填充，使得输入图像和输出图像尺寸一致；在解码路径每级上采样过程中，拓展支路径将特征金字塔网络融合到模型的网络主干中，将残差结构、Unet及特征金字塔网络的优点相互融合，增强了Unet的特征提取，弥补了语义分割网络对小目标检测的欠缺；在获取深层语义信息的同时关注细节信息，提高建筑物变化检测精度。实验表明，该方法在所用数据集，准确率、召回率、F₁ 3种指标均达到90%以上。     

基于一类新小波函数基的GPS相位观测值周跳探测

阐述了可通过1个参数因子来调节前后差分点个数的思路,研究了利用小波分析来平滑原始资料和探测GPS相位观测值周跳的方法,并通过CHAMP卫星实测数据进行分析,得出一些有益的结论。实践表明,该方法提高了周跳探测的有效性。     

多时相遥感影像的变化监测方法研究

多时相遥感影像图变化监测已经在国民经济及国防建设领域得到了广泛应用。通过分析同一地域不同时相的遥感图像变化监测提供地物发生变化的信息,对资源环境数据进行更新及利用。论文围绕变化监测中的一些关键理论方法进行了研究。     

图割能量驱动的高分辨率遥感影像震害损毁建筑物检测

为充分利用高分辨率遥感影像提供的细节信息,提高震害损毁建筑物检测精度,提出了一种图割框架下融合形状、边缘、角点等多种类型特征的损毁建筑物检测方法。该方法首先利用震前建筑物线划图提取包含单个建筑物的局部影像,用于图割能量函数建模,并分别根据建筑物的位置、形状、边缘以及角点构造能量函数的各约束项。在此基础上,通过最大流/最小割算法求解能量函数最小值,依据最小割能量进行地震前后局部影像中建筑物的相似性度量。最后利用最大期望算法(expectation maximization,EM)求得最小割能量的分类阈值,并根据错分率估值执行后处理以获得最终变化检测结果。采用2011年3月11日东日本大地震前后石卷港的高分辨率遥感影像进行试验,结果表明本文提出的方法能有效检测出损毁建筑物。     

钢筋混凝土梁结构断裂损伤诊断方法

提出一种基于BP神经网络的结构破损诊断方法,该方法以结构破损前后柔度的变化作为破损诊断网络输入,为了解决由于系统响应样本数据空间分布不均匀对网络收敛速度及网络诊断影响问题,对网络训练样本采用广义空间格点进行了交换,模拟算例及应用实例均表明,本文方法能准确诊断结构破损位置与破坏程度,是一种有效的结构破损诊断方法.     

海蜇幼体环介导等温扩增快速检测方法的建立和应用

针对海蜇早期生活史阶段幼体种类鉴定困难的问题,建立一种快速、灵敏、操作简便的环介导等温扩增(LAMP)快速检测方法.根据海蜇的线粒体细胞色素C氧化酶Ⅰ基因(COⅠ)编码序列,设计LAMP引物,建立了海蜇幼体LAMP快速检测方法,并对LAMP反应体系、反应温度和反应时间进行了优化.结果表明,LAMP方法对海蜇目的基因的最小检测限为0.1 ng DNA.该检测方法特异性较强,与黄渤海其他常见钵水母种类海月水母、白色霞水母、沙海蜇均无交叉反应.利用本研究建立的海蜇LAMP检测方法对不同海域采集的成体海蜇样品和海蜇螅状幼体进行检测,结果显示,成体海蜇和海蜇幼体均呈现阳性结果,表明海蜇LAMP检测方法可以对海蜇幼体进行快速有效的检测.海蜇LAMP检测方法简单快速、灵敏且特异性强,可使其运用于沿海渔业部门海蜇资源的调查工作.     

全张量磁梯度数据的斜导数特征值边界识别方法研究

本文提出了一种全张量磁梯度数据的斜导数边界识别方法,该方法利用全张量磁梯度数据定义了北向斜导数、东向斜导数和垂向斜导数.模型试验表明,北向和东向斜导数无法有效识别磁性异常体边界,异常形态复杂虚假异常较多,而垂向斜导数在高纬度斜磁化或者垂直磁化条件下可以清晰而准确得识别地质体的边界,具有一定的深度识别能力.与现有磁张量梯度的边界识别方法相比,识别效果好,可以有效均衡不同深度地质体的响应.将上述方法应用于大兴安岭地区实测航磁三分量数据转换得到的全张量数据,获得了研究区浅部磁性体的分布,并与三分量数据进行联合解释,取得良好的地质效果.

     

Surfactant Enhance DLLME/FO‐LADS: Assay of Malachite Green Level in Aquatic Environment of Trout Fish

Malachite green (MG), a traditional agent used in aquaculture although is not approved; its low cost and high efficacy make illicit use likely. We developed a small‐scale, simple, and sensitive dispersive liquid–liquid microextraction procedure for the assay of trace amounts of MG in aquatic environment of Trout fish. Fiber optic‐linear array detection spectrophotometry with charge‐coupled device detector benefiting from a microcell was used for this purpose. The method is based on enhancement effect of an anionic surfactant on the extraction of MG in to very fine multidroplets of microextraction solvent which made assisted by disperser solvent. Under the optimum conditions, the enrichment factor 77.5 was obtained from a 5‐mL water sample. The calibration graph was linear up to 5 × 10^?7 mol L^?1 with detection limit of 1 × 10^?8 mol L^?1. The relative standard deviation for seven replicate measurements of 4 × 10^?7 and 5 × 10^?8 mol L^?1 of MG were 3.3 and 4.5%, respectively.