不同结构深度神经网络的时间域航空电磁数据成像性能分析     

李金峰  刘云鹤 《世界地质》2020,39(1):159-166

时间域航空电磁系统采样密集,数据量大,所以在该领域较为实用的数据处理方法主要为一维反演和电阻率成像法。笔者从成像问题出发,建立了庞大的数据模型训练集,研究并分析了不同结构的神经网络的成像精度。通过对比分析测试结果,获得了在一定条件下适用于航空电磁成像的最优网络模型结构,包含其神经元个数和层数等信息。本文采用早停法训练神经网络,压制数据中噪声对成像结果的影响。  相似文献   

古生物学数据库现状与数据驱动下的科学研究     

邓怡颖  樊隽轩  王玥  史宇坤  杨娇  鲁铮博 《高校地质学报》2020,26(4):361-383

化石数据是了解地球历史以及深时生命演化的重要信息来源。通过数百年的积累，古生物学家已经发表了海量的古生物学数据。过去三、四十年里，随着计算机、数据库和互联网技术的快速发展，国内外涌现出大量的古生物学数据库，彼此间的目标、体系架构、数据组织方式和服务对象通常存在显著差异，呈现百花齐放的特点。文章系统介绍了古生物学领域主要数据库的发展历史、数据表结构、数据特征和数据量等建设情况，对比分析了其数据整理方式、核心在线功能、数据共享特点和数据质量控制措施。同时，结合近年来数据驱动下的古生物学领域的科学研究实例，提出一站式全生态链数据平台的建设设想，为深时数字地球（DDE）建设多学科融合、数据开放与共享的大数据平台提供参考。  相似文献   

基于混合聚类的岩体结构面优势分组方法     

许扬  张文  符锐  聂振邦  兰志广  陈怀玉  单博 《世界地质》2020,39(1):113-120

提出一种基于凝聚层次法和模糊C均值法的混合聚类法,用于对岩体结构面的优势组划分。该方法将结构面投放到在单位球面上,并使用欧式距离作为极点的相似性度量准则。先剔除结构面数据中的孤值产状,然后用凝聚层次法得到初步聚类结果,并将其作为FCM法的初始聚类中心,最后用FCM法划分优势组。通对人工生成产状样本的分组,验证了该法的正确性。将该方法应用于大藤峡坝址区实测的结构面数据的划分。在实测数据中寻找到两个孤值产状,成功将大藤峡D1y^1-3地层岩体结构面划分为两组,得到了符合实际的分组结果。  相似文献   

乌海地区剪切波分裂研究     

魏建民  张晖  赵星  张浩鸣  郝亮  阿那尔 《震灾防御技术》2020,15(4):777-787

根据乌海地区构造环境，采用SAM方法研究乌海地区地壳各向异性特征，使用乌海地震台2014年1月至2020年6月数字地震波形进行分析。根据65个有效地震记录，得到乌海地区剪切波分裂参数，其中快剪切波平均优势偏振方向为NE63.1°±46.4°，慢剪切波平均时间延迟为（1.13±0.66）ms/km。乌海地震台快剪切波偏振显示出4个优势偏振方向，分别为NE、EW、NNE、NNW向。将得到的各向异性结果与研究区应力场和地质构造进行分析，认为研究区周边复杂的剪切波分裂变化是主压应力场、原地主压应力、断裂带分布共同作用的结果。  相似文献   

重庆地震应急资料产出系统研发与应用     

龚丽文  陈石  郭卫英  黄世源  李翠平  高见 《地震地磁观测与研究》2020,41(5):192-199

地震应急资料具有较强的时效性、专业性和多学科融合的特点。中强地震发生后启动应急响应，工作人员需在短时间内产出多学科、跨领域、多手段的专业地震会商资料。以地震会商系统为平台，利用其灵活开发、自动计算和智能分析的优势，建立地震应急资料产出系统（包括会商资料处理模块、应急图件产出模块和震情报告生成模块），通过地震触发，根据预设流程和报告模板，自动产出专业报告，为震情形势研判和震后应急救援提供专业可靠的应急资料，最大限度地发挥应急救援和灾损评估的效能。  相似文献   

深时全球海平面变化重建方法的回顾与展望     

许艺炜  胡修棉 《高校地质学报》2020,26(4):395-410

全球海平面变化对古地理、古生物、古气候演化以及能源矿产分布具有重要的控制作用。然而，目前学界对深时海平面变化的驱动机制尚不清楚，部分归因于缺乏高精度全球海平面变化的恢复。文章回顾了全球海平面变化研究的起始与发展过程，归纳出五种类型的深时海平面变化重建方法和技术，即地层学、沉积学、洋盆动力学、同位素地球化学方法和大数据技术。并总结了上述研究方法的原理、优势和不足，并以白垩纪全球海平面重建为实例，讨论了当前深时全球海平面重建的难点和争议点，并对未来深时全球海平面变化重建进行了展望。  相似文献   

An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest     

《Chinese Astronomy and Astrophysics》2020,44(1):41-60

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.  相似文献   

The Basic Shape Classification of Space Debris with Light Curves     

LU Yao  ZHAO Chang-yin 《Chinese Astronomy and Astrophysics》2021,45(2):190-208

We study the machine learning method for classifying the basic shape of space debris in both simulated and observed data experiments, where light curves are used as the input features. In the dataset for training and testing, simulated light curves are derived from four types of debris within different shapes and materials. Observed light curves are extracted from Mini-Mega TORTORA (MMT) database which is a publicly accessible source of space object photometric records. The experiments employ the deep convolutional neural network, make comparisons with other machine learning algorithms, and the results show CNN (Convolutional Neural Network) is better. In simulational experiments, both types of cylinder can be distinguished perfectly, and two other types of satellite have around 90% probability to be classified. Rockets and defunct satellites can achieve 99% success rate in binary classification, but in further sub-classes classifications, the rate becomes relatively lower.  相似文献   

A high accurate automated first-break picking method for seismic records from high-density acquisition in areas with a complex surface     

Yinpo Xu  Cheng Yin  Xuefeng Zou  Yudong Ni  Yingjie Pan  Liangjun Xu 《Geophysical Prospecting》2020,68(4):1228-1252

As the application of high-density high-efficiency acquisition technology becomes more and more wide, the areas with complex surface conditions gradually become target exploration areas, and the first-break picking work of massive low signal-to-noise ratio data is a big challenge. The traditional method spends a lot of manpower and time to interactively pick first breaks, a large amount of interactive work affects the accuracy and efficiency of picking. In order to overcome the shortcoming that traditional methods have weak anti-noise to low signal-to-noise ratio primary wave, this paper proposes a high accurate automated first-break picking method for low signal-to-noise ratio primary wave from high-density acquisition in areas with a complex surface. Firstly, this method determines first-break time window using multi-azimuth spatial interpolation technology; then it uses the improved clustering algorithm to initially pick first breaks and then perform multi-angle comprehensive quality evaluation to first breaks according to the following sequence: ‘single trace → spread → single shot → multiple shots’ to identify the abnormal first breaks; finally it determines the optimal path through the constructed evaluation function and using the ant colony algorithm to correct abnormal first breaks. Multi-azimuth time window spatial interpolation technology provides the base for accurately picking first-break time; the clustering algorithm can effectively improve the picking accuracy rate of low signal-to-noise ratio primary waves; the multi-angle comprehensive quality evaluation can accurately and effectively eliminate abnormal first breaks; the ant colony algorithm can effectively improve the correction quality of low signal-to-noise ratio abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion method, the automated first-break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low signal-to-noise ratio seismic data, has a significant effect on seismic records from high-density acquisition in areas with a complex surface and can meet the requirements of accuracy and efficiency for massive data near-surface modelling and statics calculation.  相似文献   

数字正射影像结合LiDAR数据在山区测绘中的应用     

严慧敏 《测绘通报》2020,(1):115-119

随着信息化社会的到来，现代水利测绘已经由传统测绘向信息化测绘发展，无人机技术应用于测绘行业推进了信息化测绘进程。本文探讨了如何有效利用无人机技术解决测绘领域在山区遇到的问题。固定翼无人机能及时获取地面数字正射影像数据，捕获裸露地面的平面和高程，但是无法获取植被覆盖下的地表高程信息，因此，本文通过机载激光雷达获取植被覆盖下的LiDAR点云数据；将二者数据相结合，再通过EPS软件生成三维地表模型，可以快速获取任何测区地物和地形数据，不仅提高了工作效率，还降低了外业劳动强度。  相似文献