深度神经网络拾取地震P和S波到时

从地震波形数据中快速准确地提取各个震相的到时是地震学中的基础问题.本文针对上述问题提出了利用深度神经网络拾取到时的新方法,建立了用于地震到时提取的17层Inception深度网络模型,在对原始三分量数据进行高通滤波和归一化处理后输入网络直接输出到时信息.整个过程基于神经网络自适应提取波形特征,自动输出结果.通过对100组加了不同强度的噪声数据进行了可靠性检验,相比于其他方法神经网络方法对于噪声具有较高的容忍度以及稳定性,并且与地震目录数据有较高的相似性.相比于AR-AIC+STA/LTA,深度神经网络虽然运算速度稍慢,但整个过程不需设定时窗与阈值,同时具有更高的可用性,并且可以迭代升级以提高精度.此方法作为人工智能方法,为波形到时拾取提供了新思路.

     

基于样本增强的卷积神经网络震相拾取方法

为了快速、高效地从地震数据中识别地震事件和拾取震相,本文利用基于样本增强的卷积神经网络自动震相拾取方法,将西藏林芝地区L0230台站3个月数据作为训练集,该区内另外6个台站连续1个月的波形数据作为测试集,采用高斯噪声、随机噪声拼接、随机挑选噪声、随机截取地震事件等4种样本增强的方法扩增训练集,以提高自动震相拾取技术的准确率。结果显示:样本增强前模型在测试集上的地震事件识别准确率为80%,样本增强后提升至97%,表明样本增强有效地提高了模型的泛化性能和抗干扰能力;在0.5 s误差范围内,震相自动拾取准确率高于81%,在1.0 s误差范围内,准确率高于95%;利用基于样本增强的卷积神经网络震相拾取方法能够检测出人工拾取震相中误标和漏检的震相。      

基于深度卷积神经网络的地震震相拾取方法研究

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0.067 s,0.082 s.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.

     

白鹤滩水库库区基于深度学习的震相拾取与地震定位

近年来深度学习技术广泛应用于震相拾取与地震定位研究,采用深度神经网络搭建的EQTransformer模型对白鹤滩水库库区34个数字地震台站2016—2018年记录的连续数据进行P、 S波震相拾取,并通过REAL进行震相关联和初步定位,然后使用VELEST和hypoDD地震定位算法优化地震位置。研究表明,基于深度学习的震相拾取,与白鹤滩水库地区传统的人工处理方法相比显示出更高的效率,EQTransformer模型可拾取与人工拾取相当的P、 S波震相到时,其时间差的均值分别为0.03 s和0.07 s,符合正态分布。REAL初步定位后的地震个数(13815个)接近常规目录(7862个)的2倍,最终通过hypoDD获得了7108个高精度定位地震。估算的震级比常规目录中的震级平均低0.27,震级差值集中在0.7以内,最小完备震级由常规目录的M_L1.4更改为M_L0.6+0.27,填补了部分常规目录的震级空白,丰富了研究区域内的中小型地震。     

基于深度学习和台阵策略的震相自动拾取方法APP++及其在川滇地区的应用

在APP方法的基础上,使用U-net++模型,提出了进一步的改进算法APP++用于台阵下的震相到时自动拾取,测试结果显示,APP++模型在具有和APP相当的拾取能力的同时,还能够更好地避免误识别,且具有跨区域泛化能力;将该方法用于川滇地区的固定台网和流动台阵上,扫描了2014—2019年6年的连续数据,检测到了7329...     

基于深度学习的地震叠加速度谱自动拾取算法研究

为解决人工拾取地震叠加速度谱时耗时长、效率低等问题, 本文提出了一种基于深度学习的地震速度谱自动拾取算法模型VSAP(Velocity Spectrum Accurate Pickup).该算法运用卷积神经网络Faster R-CNN模型构建的多分类任务拾取目标能量团, 然后将初步拾取后的能量团坐标输入循环神经网络LSTM(Long-Short Term Memory)模型来进行目标能量团拾取时坐标的取舍和微调, 最后输出模型分析和调整过的速度谱自动拾取图像.并通过实际的地震数据集拾取结果验证了该算法模型在叠加速度谱复杂信息的干扰中自动、准确拾取速度谱中能量团的能力, 同时验证了该模型的准确性以及鲁棒性.经过改进, 该算法模型有效地提高了速度谱拾取的效率和拾取精度.

     

用sPn震相测定近震震源深度

用近震深度震相ｓＰｎ与Ｐｎ波的到时差测定近震深度，方法以求简捷准确。为此，对我国部分地区台网记录的ｓＰｎ震相进行了初步分析研究，通过研制的计算ｓＰｎ走时程序运算，给出华北、山西、华东、西北及四川地区的ｓＰｎ－Ｐｎ走时差对应震源深（ｈ）表和ｓＰｎ－Ｐｎ求震源深度列线图。经过震例检验，该表比较适合本地区的走时特性。为应用ｓＰｎ震相测准我国部分地区震源深度，提供了有利工具。     

ObsPy包中的地震事件触发和震相拾取算法梳理

ObsPy包是基于Python语言编写的面向地震学应用的地震数据处理工具软件包,该软件包中实现了5种地震事件触发和2种震相拾取算法。文章主要对这几种算法进行梳理,分析其参数需求与特点,并以临汾台、太原台和大同台3个国家测震台记录到的2016年3月12日运城盐湖ML4.8地震的事件波形为例进行分析,为研究ObsPy包在地震数据自动处理中的应用提供参考与借鉴。     

天然地震识别与震相自动拾取技术进展

震相拾取是地震数据处理过程中最基本的步骤之一。在传统的人工拾取技术不能满足庞大的地震数据处理需求的情况下,震相自动拾取技术从产生到发展至今经历了漫长的过程。本文回顾并总结了震相自动拾取技术的发展状况,重点介绍了长短时窗法、赤池准则法、模板匹配技术、基于自相关盲搜索的FAST法、S波偏振分析法、人工智能方法等,以及近年发展起来的多频率震相识别、全波形叠加、二次方自回归模型等方法,同时分析了每种方法的优势和局限性。     

卷积神经网络在远-近地震震相拾取中的应用及模型解释

利用北京国家观象台的测震记录,探索了样本构建、训练过程、模型结构等因素对远震震相P-S和近震震相Pg-Sg拾取模型性能的影响。结果表明：适中的卷积层深度、正则化和数据清洗能够有效地改善模型性能,而残差块的影响却相对有限。与此同时,基于类模型可视化和平滑GradCAM++的模型解释显示：卷积神经网络复现了震相的关键特征,其决策敏感区域也与震相识别的经验准则一致。最后,连续波形的扫描结果展示了卷积神经网络在远-近地震震相识别的应用前景与提升空间。此外,本文针对模型搭建与训练中存在的问题提出了样本选择、模型架构、标签标注和集成学习等改进方案,以供后续研究参考。     

Seismic phase picking in China Seismic Array using a deep convolutional neuron network*

Seismic phase picking is the preliminary work of earthquake location and body-wave travel time tomography. Manual picking is considered as the most accurate way to access the arrival times but time consuming. Many automatic picking methods were proposed in the past decades, but their precisions are not as high as human experts especially for events with low ratio of signal to noise and later arrivals. As the increasing deployment of large seismic array, the existing methods can not meet the requirements of quick and accurate phase picking. In this study, we applied a phase picking algorithm developed on the base of deep convolutional neuron network (PickNet) to pick seismic phase arrivals in ChinArray-Phase III. The comparison of picking error of PickNet and the traditional method shows that PickNet is capable of picking more precise phases and can be applied in a large dense array. The raw picked travel-time data shows a large variation deviated from the traveltime curves. The absolute location residual is a key criteria for travel-time data selection. Besides, we proposed a flowchart to determine the accurate location of the single-station earthquake via dense seismic array and phase arrival picked by PickNet. This research expands the phase arrival dataset and improves the location accuracy of single-station earthquake.     

一种震相拾取误差的自动判别和计算方法

本文给出了一个主要用于深地震测深数据的震相识别误差（不确定性）的判别和计算方法.该方法集中讨论从记录截面拾取震相这一过程所引起的判别误差.以震相前后一定时窗内的地震记录振幅的均方根之比为判别依据,找出误差分布范围并给出走时误差与振幅比的分级相关函数.由此,当震相确定后,计算程序将根据记录数据自动算出识别误差.实践证明该方法不仅更加客观真实、方便快捷,而且为今后震相提取工作的进一步科学规范打下了基础.     

地震检测与震相自动拾取研究

针对微震事件易受噪声干扰等特点,本文将STA/LTA方法和基于方差的AIC方法（var-AIC）相结合,在震相到时初步拾取的基础上,使用台站的德洛内（Delaunay）三角剖分及台站间最大走时差约束来减少噪声干扰的影响. 利用到时进行地震定位之后,根据台站预测到时,在设定的时间窗内对地震震相进行更精细的分析. 特别是针对微震事件信噪比低的特点,设计了基于偏振分析的拾取函数,根据窗内STA/LTA方法和var-AIC方法的拾取结果自动选择合适的值作为震相到时. 最后,对西昌流动地震台阵2013年304个单事件波形数据的分析处理和检验结果表明,本文方法较传统方法具有更高的地震事件检测能力和更高的震相拾取精度.      

Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network

Seismic phase pickers based on deep neural networks have been extensively used recently, demonstrating their advantages on both performance and efficiency. However, these pickers are trained with and applied to different data. A comprehensive benchmark based on a single dataset is therefore lacking. Here, using the recently released DiTing dataset, we analyzed performances of seven phase pickers with different network structures, the efficiencies are also evaluated using both CPU and GPU devices. Evaluations based on F₁-scores reveal that the recurrent neural network (RNN) and EQTransformer exhibit the best performance, likely owing to their large receptive fields. Similar performances are observed among PhaseNet (UNet), UNet++, and the lightweight phase picking network (LPPN). However, the LPPN models are the most efficient. The RNN and EQTransformer have similar speeds, which are slower than those of the LPPN and PhaseNet. UNet++ requires the most computational effort among the pickers. As all of the pickers perform well after being trained with a large-scale dataset, users may choose the one suitable for their applications. For beginners, we provide a tutorial on training and validating the pickers using the DiTing dataset. We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users. All of our models are open-source and publicly accessible.     

CRPN: A cascaded classification and regression DNN framework for seismic phase picking*

Current deep neural networks (DNN) used for seismic phase picking are becoming more complex, which consumes much computing time without significant accuracy improvement. In this study, we introduce a cascaded classification and regression framework for seismic phase picking, named as the classification and regression phase net (CRPN), which contains two convolutional neural network (CNN) models with different complexity to meet the requirements of accuracy and efficiency. The first stage of the CRPN are shallow CNNs used for rapid detection of seismic phase and picking P and S arrival times for earthquakes with magnitude larger than 2.0, respectively. The second stage of CRPN is used for high precision classification and regression. The regression is designed to reduce the time difference between the probability maximum and the real arrival time. After being trained using 500,000 P and S phases, the CRPN can process 400 hours’ seismic data per second, whose sampling rate is 1 Hz and 25 Hz for the two stages, respectively, on a Nvidia K2200 GPU, and pick 93% P and 89% S phases with the error being reduced by 0.1s after regression correction.     

Automatic onset phase picking for portable seismic array observation

Automatic phase picking is a critical procedure for seismic data processing, especially for a huge amount of seismic data recorded by a large-scale portable seismic array. In this study is presented a new method used for automatic accurate onset phase picking based on the proporty of dense seismic array observations. In our method, the Akaike＇s information criterion （AIC） for the single channel observation and the least-squares cross-correlation for the multi-channel observation are combined together. The tests by the seismic array observation data after triggering with the short-term average/long-term average （STA/LTA） technique show that the phase picking error is less than 0.3 s for local events by using the single channel AIC algorithm. In terms of multi-channel least-squares cross-correlation technique, the clear teleseismic P onset can be detected reliably. Even for the teleseismic records with high noise level, our algorithm is also able to effectually avoid manual misdetections.     

Seismic velocity inversion based on CNN-LSTM fusion deep neural network

Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean squ...     

基于深度神经网络的地震强反射剥离方法

在储层预测工作中,储层弱反射信号淹没在强反射信号之中的情况非常常见,这不利于精确识别和描述储层结构.本文提出了一种基于深度神经网络的强反射剥离方法,用于辅助储层弱反射信号的检测工作.该方法在卷积模型的框架下将强反射预测问题分解为地震子波预测与强反射预测两个子优化问题,并采用AIDNN与U-Net两个深度神经网络分别求解...