排序方式： 共有75条查询结果，搜索用时 28 毫秒
61.
地震震相拾取是地震数据自动处理的首要环节,包括了信号检测、到时估计和震相识别等过程,震相拾取的准确性直接影响到后续事件关联处理的性能,影响观测报告的质量.为了提高震相拾取的准确性,进而提高观测报告质量,本文采用深度卷积神经网络方法来解决震相拾取问题,构建了多任务卷积神经网络模型,设计了分类和回归的联合损失函数,定义了基于加权的分类损失函数,以三分量地震台站的波形数据作为输入,同时实现对震相的检测识别和到时的精确估计.利用美国南加州地震台网的200万条震相和噪声数据对模型进行训练、验证和测试,对于测试集中直达波P、S震相识别的查全率达到98%以上,到时估计的标准偏差分别为0.067s,0.082s.利用迁移学习和数据增强,将模型用于对我国东北地区台网的6个台站13000条数据的训练、验证和测试中,对该数据集P、S震相查全率分别达到91.21%、85.65%.基于迁移训练后的模型,设计了用于连续数据的震相拾取方法,利用连续的地震数据对该算法进行了实际应用测试,并与国家数据中心和中国地震局的观测报告进行比对,该方法的震相检测识别率平均可达84.5%,验证了该方法在实际应用中的有效性.本文所提出的方法展示了深度神经网络在地震震相拾取中的优异性能,为地震震相和事件的检测识别提供了新的思路. 相似文献
62.
岩石圈各向异性主要由上地幔矿物晶格优势排列方向和上地壳内裂缝、裂隙的定向分布造成.在各向异性特征显著区域,利用SKS震相剪切波分裂获得的延迟时间高达1.5s以上.本文根据方位各向异性,利用广义反射透射系数矩阵方法正演S波接收函数,研究各向异性对不同反方位角接收函数转换震相走时的影响.我们发展了基于HTI模型各向异性走时校正的方法,成功的在单层和多层(快轴方向相同或不同)的各向异性介质中对齐不同反方位角接收函数的Moho面和LAB的转换震相走时.我们将该方法应用于在青藏高原东北缘的流动台站,试图在各向异性强度较大区域对实测数据转换震相走时的校正效果进行测试.结果表明:各向异性走时校正能够加强单台接收函数转换震相的可追踪性,能量增强的叠后转换震相在时深转换后更利于对界面深度的识别与判断;在考虑三维成像的情况下,我们的各向异性校正方法对提高成像结果的准确性有重要意义. 相似文献
63.
Locating an earthquakes focal depth is always a key project in seismology. Precise focal depth is of critical importance for evaluating seismic hazards, deciphering dynamic mechanisms of earthquake generating,estimating aftershock evolutions and risk,as well as monitoring nuclear tests. However,how we determine an accurate focal depth is always a challenge in seismological studies. Aiming to solve these problems, we analyzed and summarized the present status and the future development of earthquake focal depth locating. In this paper we first reviewed the present status of focal depth locating in the world,and summarized the frequently-used relocating methods and ideas at present,and introduced two types of focal depth relocating ideas: arrival time relocating and waveform modeling methods. For these ideas,we systematically described the S-P and the Pn-Pg methods that belong to arrival time method,and polarization focal depth locating and amplitude focal depth locating that belongs to waveform modeling,and further analyzed the advantages and limitations of these methods. Since the depth phase methods are highly sensitive to focal depth,and are relatively free from the uncertainties of crustal models,we mainly reviewed the depth phases of s Pm P,s PL,s Pn,and s Sn,and quantitatively evaluated their availabilities and characteristics. Second,we also discussed the effects of crustal velocity models on the reliability of focal depth locating,and reviewed the advancements of seismic tomography techniques over recent years. Finally,based on the present status of the progress on the focal depth locating,and studies of seismic velocity structures,we proposed an idea of combining multiple datasets and relocating methods,jointly utilizing seismologic and geodetic techniques to relocate focal depth,which should be the major research field in investigating focal depth and source parameters in the near future. 相似文献
64.
First-break picking of microseismic data is a significant step in microseismic monitoring. There is a great error in conventional first-break picking methods based on time domain analysis in low signal to noise ratio. S-transform may provide a novel approach, it can extract the time–frequency features of the signal and reduce the picking error because of its high time–frequency resolution and good time–frequency clustering; however, the S-transform is not well suited for microseismic data with high noise. For applications to array data where the weak signal has spatial coherency as well as some distinct temporal characteristics, we propose to combine the shearlet transform with a time–frequency transform. In the proposed method, the shearlet transform is used to capture spatial coherency features of the signal. The information of the signal and noise in shearlet domain is represented by shearlet coefficients. We use the correlation of signal coefficients at adjacent fine scales to give prominence to signal features to accurately discriminate the signal from noise. The prominent signal coefficients make the signal better gathered in time–frequency spectrum of the S-transform. Finally, we can get reliable and accurate first breaks based on the change of energy. The performance of the proposed method was tested on synthetic and field microseismic data. The experimental results indicated that our method is outstanding in terms of both picking precision and adaptability to noise. 相似文献
65.
磁暴急始的自动识别拾取与磁暴报告自动产出是国家地磁台网数据产品产出中重要的内容。为实现在1s采样率下的磁暴急始高精度拾取,本文针对性地提出基于Walsh变换和Akaike信息准则(AIC)的起跳点检测算法——Walsh-AIC算法,将其应用于98个磁暴急始事件的拾取上,并采用由国际地磁指数服务(ISGI)发布的磁暴时刻为标准对应用效果进行评估。结果表明,该算法不仅能够将急始磁暴起跳时刻的位置更加明显地刻画出来,而且能够有效避免传统识别急始磁暴的AIC算法中存在的识别起跳时刻结果晚于实际起跳时刻的情况。同时,将Walsh-AIC算法与其他主流拾取方法进行对比,发现前者定位结果的平均偏差和标准差均明显小于其他算法。此外,Walsh-AIC算法结果的误差关于0点的对称性较好,表明该算法拾取磁暴急始起跳时刻的精度较高,适合于解决磁暴急始的自动拾取问题。 相似文献
66.
Abstract. A high frequency deep-tow seismic survey was carried out in the Nankai Trough area in 1996. The objective of the survey was to obtain high resolution seismic sections and velocity profiles of the methane hydrate zone, inferred from the strong BSR events seen on conventional seismic data in the area. A special feature of the survey is that both the source and the streamer cable are towed close to the seabed. This special acquisition geometry requires special data processing to handle the varying source and receiver depths. A CMP floating datum processing sequence was designed which led to high quality sections of the shallow geology. A key step in the processing was devising a residual statics technique to compensate for errors in the measured depths.
The processing sequence was applied to a number of lines, totaling 200 km. The final data quality was highly variable. Some lines produced high quality sections and others, much poorer sections with few interpretable events. Conventional seismic data in the area also shows variation in the data quality so part of the reason is a variation in the sub-sea geology, but the deep-tow data is much more sensitive to change in conditions than conventional data. With the current acquired data and processing sequence the deep-tow system offers most advantages when 1) the water depth is around 1 km or greater, 2) the seabed and underlying geology is not too complex, and 3) the acquisition proceeds smoothly with regular shotpoints, slowly varying depths, and with accurate positioning. 相似文献
The processing sequence was applied to a number of lines, totaling 200 km. The final data quality was highly variable. Some lines produced high quality sections and others, much poorer sections with few interpretable events. Conventional seismic data in the area also shows variation in the data quality so part of the reason is a variation in the sub-sea geology, but the deep-tow data is much more sensitive to change in conditions than conventional data. With the current acquired data and processing sequence the deep-tow system offers most advantages when 1) the water depth is around 1 km or greater, 2) the seabed and underlying geology is not too complex, and 3) the acquisition proceeds smoothly with regular shotpoints, slowly varying depths, and with accurate positioning. 相似文献
67.
面向Java手机GPS数据采集与无线网络传输系统的研究 总被引：10，自引：0，他引：10
通过对Java手机硬件性能的分析 ,提出了面向Java手机GPS数据采集与无线网络传输系统方案 ,并以Motorola388手机对GPS数据采集串行通讯和GPRS无线数据传输为试验 ,证明了方案的可行性 相似文献
68.
快速准确地从微震监测数据中提取微地震事件是微地震监测技术的关键。采用理论模拟数据分析了STA/LTA方法的可行性,选择了更能反映微地震信号变化的特征函数代替原始信号,结合实际数据对时窗长度、长短时窗比、阈值等重要参数进行了对比分析。研究结果表明,STA/LTA方法能够从海量微地震监测数据中快速准确地自动识别微地震有效信号,去除冗余信息,大幅减少微地震监测数据的传输量,从而为微地震监测数据的无线实时传输提供了可能,同时也减少了数据存储所需要的磁盘空间,取得了较好的应用效果。 相似文献
69.
微地震事件初至拾取是微地震数据处理的关键步骤之一.实际微地震监测资料中存在大量低信噪比事件,而传统方法对这些事件的应用效果并不理想.为了克服传统方法抗噪性弱的缺点,本文通过综合地震信号与环境噪声在振幅、偏振以及统计特征等方面的存在的差异,设计了一种针对低信噪比微地震事件的初至拾取方法——SLPEA算法.为了检验本文方法的可行性和有效性,分别对模型数据和实际资料进行了处理,并将处理结果与传统方法及手工拾取的结果进行了对比.分析表明,利用本文方法得到的初至到时与手工拾取结果的绝对误差平均值仅为1.33×10~(-3)s,小于3个采样点;方差为3.21×10~(-6)s~2;初至到时在手工拾取结果±0.005s误差范围内的个数占总数的95.8%.这些参数值均优于传统方法的同类参数,证明了本文方法的可靠性. 相似文献
70.