Seismic phases from the Moho and its implication on the ultraslow spreading ridge

The Moho interface provides critical evidence for crustal thickness and the mode of oceanic crust accretion. The seismic Moho interface has not been identified yet at the magma-rich segments （46°-52°E） of the ultra- slow spreading Southwestern Indian Ridge （SWIR）. This paper firstly deduces the characteristics and do- mains of seismic phases based on a theoretical oceanic crust model. Then, topographic correction is carried out for the OBS record sections along Profile Y3Y4 using the latest OBS data acquired from the detailed 3D seismic survey at the SWIR in 2010. Seismic phases are identified and analyzed, especially for the reflected and refracted seismic phases from the Moho. A 2D crustal model is finally established using the ray tracing and travel-time simulation method. The presence of reflected seismic phases at Segment 28 shows that the crustal rocks have been separated from the mantle by cooling and the Moho interface has already formed at zero age. The 2D seismic velocity structure across the axis of Segment 28 indicates that detachment faults play a key role during the processes of asymmetric oceanic crust accretion.

Identification method of seismic phase in three-component seismograms on the basis of wavelet transform

This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.

Numerical modeling of global seismic phases and its application in seismic phase identification

Earthquake data include informative seismic phases that require identification for imaging the Earth's structural interior.In order to identify the phases,we created a numerical method to calculate the traveltimes and raypaths by a shooting technique based upon the IASP91 Earth model,and it can calculate the traveltimes and raypaths for not only the seismic phases in the traditional traveltime tables such as IASP91,AK135,but also some phases such as pPcP,pPKIKP,and PPPPP.It is not necessary for this method to mesh the Earth model,and the results from the numerical modeling and its application show that the absolute differences between the calculated and theoretical traveltimes from the ISAP91 tables are less than 0.1 s.Thus,it is simple in manipulation and fast in computation,and can provide a reliable theoretical prediction for the identification of a seismic phase within the acquired earthquake data.

Amplitude Ratio of Regional Seismic Phases: An Efficient Indicator of Thermal State of the Lithosphere

By analyzing seismograms of short period records at the Beijing SeismoJogicaJ Observatory, the present paper investigates the amplitude ratio of seismic phases. The results indicate that the amplitude ratio of Sn/Lg is correlated with the lithosphere structure, the thermal state, and strong earthquake occurrence in the region the seismic rays pass through. The significance of such a correlation in the study on the genesis and prediction of strong earthquakes is discussed.

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.

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 threecomponent 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.

DiTing:A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.

On some problems of seismic crustal phase

In this paper, some practical problems in seismological observations are discussed on the identification of seismiccrustal phase, such as: “whether P (Pg) and S(Sg) are direct waves or not?”; “whether S(Sg) and Lg as well asng and P (Pg) are two kinds of waves or identical wave?”;“on seismoiogicai phases in regional travel-timetable”;“on the symbols and identifications of phases in seismological observation repors”;“on the relation between mLg and ML”, and so on. Some confused ideas on these problems are clarified.

Phase spectrum estimation of the seismic wavelet based on a criterion function

Phase spectrum estimation of the seismic wavelet is an important issue in high-resolution seismic data processing and interpretation. On the basis of two patterns of constant-phase rotation and root transform for wavelet phase spectrum variation, we introduce six sparse criteria, including Lu’s improved kurtosis criterion, the parsimony criterion, exponential transform criterion, Sech criterion, Cauchy criterion, and the modified Cauchy criterion, to phase spectrum estimation of the seismic wavelet, obtaining an equivalent effect to the kurtosis criterion. Through numerical experiments, we find that when the reflectivity is not a sparse sequence, the estimated phase spectrum of the seismic wavelet based on the criterion function will deviate from the true value. In order to eliminate the influence of non-sparse reflectivity series in a single trace, we apply the method to the multi-trace seismogram, improving the accuracy of seismic wavelet phase spectrum estimation.

PhaseNet与EQTransformer的震相拾取对比研究

将湖北测震台网的地震波形作为数据集,采用PhaseNet和EQTransformer模型进行震相拾取,并对其表现及泛化能力进行测试评估。结果表明,对于P波而言,当震相概率阈值为0.1或0.3时,PhaseNet有较高的召回率,可检测到更多微震事件;EQTransformer的召回率略低,但精确率较高。S波的拾取效果差于P波,PhaseNet的精确率低于EQTransformer,但其召回率较高,F_(1)值也能保持在0.8左右,拾取表现较为稳定。进一步分析2种模型的拾取结果与事件震中距、信噪比及震级之间的关系发现,PhaseNet的震相拾取效果与震中距、信噪比的关联较强,与震级关系不大,信噪比越高的数据拾取效果越好;EQTransformer与信噪比的关联较强,信噪比越高拾取效果越好,与震中距和震级关系不大。

Seismic attenuation in the lower mantle beneath Northeast China constrained from short-period reflected core phases at short epicentral distances

The thermal structure of the lower mantle plays a key role in understanding the dynamic processes of the Earth's evolution and mantle convection.Because intrinsic attenuation in the lower mantle is highly sensitive to temperature,determining of the attenuation of the lower mantle could help us determine its thermal state.We attempted to constrain the attenuation of the lower mantle by measuring the amplitude ratios of p to ScP on the vertical component and s to ScS on the tangential component at short epicentral distances for seismic wave data from deep earthquakes in Northeast China.We calculated the theoretical amplitude ratios of p to ScP and s to ScS by using ray theory and the axial-symmetric spectral element method AxiSEM,as well as by considering the effects of radiation patterns,geometrical spreading,and ScP reflection coefficients.By comparing the observed amplitude ratios with the synthetic results,we constrained the quality factors as Qα≈3,000 and Qβ≈1,300 in the lower mantle beneath Northeast China,which are much larger than those in the preliminary reference Earth model(PREM)model of Qα~800 and Qβ~312.We propose that the lower mantle beneath Northeast China is relatively colder than the average mantle,resulting in weaker intrinsic attenuation and higher velocity.We estimated the temperature of the lower mantle beneath Northeast China as approximately 300–700 K colder than the global average value.

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.

影响山西台网地震定位因素的定量分析

基于山西地震台网现有的台站布局,利用数值模拟方法定量分析地壳速度模型、台网布局及震相拾取精度等因素对震源位置测定的影响,结果显示,3种因素对准确测定震源位置的影响不同。地壳速度模型中上地壳速度对震中位置的影响最大,而震中位置对莫霍面速度变化的反映更为灵敏。单独改变地壳速度模型引起的震中位置偏差通常不超过5 km,震源深度对地壳速度模型的变化十分敏感,尤其是莫霍面深度,最大偏差可能超过20 km。在不考虑其他因素的条件下,P波拾取精度在4 s以内测定的震中位置和震源深度最大偏差不超过2 km和5 km。当地壳速度模型适宜且震相拾取精度较高时,最大空隙角在0°~180°的台站布局对震源位置的约束较好,震中偏差基本小于1 km,震源深度偏差略大(约为2 km)。

EPS混合土地基-沉箱抗震性能振动台模型试验

采用振动台试验研究了饱和砂土地基(CSS)和EPS混合土地基(CES)两种地基模型下沉箱结构的抗震性能。对比了两种不同地基模型下土体动力响应特征及沉箱结构动力稳定性,探讨了动力作用下沉箱的受力状态及失稳机制,分析了EPS混合土地基对上部沉箱结构地震稳定性的影响。试验结果表明:振动过程中CES工况的超孔压发展速率明显低于CSS,EPS混合土可显著提高模型抗液化性能。墙后填土作用于沉箱的动土压力沿深度方向呈三角形分布,沉箱中部位置动土压力最大。CES工况沉箱动力响应,包括加速度、位移及转动角等均小于CSS,表现出更好的抗震性能。CES沉箱所受动土推力与惯性力存在明显的相位差,有利于提高沉箱结构的稳定性。

自动编目软件在山西预警台网中的应用

本文采用基于深度学习的地震检测算法对山西预警台网记录的连续波形,开展离线的震相自动拾取工作,从连续波形检测、到时拾取、震相关联,地震定位和震级测定,最终形成完整的地震目录,即自动编目结果。将自动编目形成的地震参数从发震时刻、震中位置和震级这3个方面,与中国地震台网中心统一编目结果相比较,对比结果显示:发震时刻偏差平均值为-0.1 s,震中位置偏差平均值为3.9 km,震级偏差平均值为0.2级,偏差结果不大,说明了预警台网自动识别的地震定位精度高、与台网中心编目的结果有很好的一致性,下一步可纳入预警台网日常工作。

利用sPL震相测定2022年山西古交M_(L)4.1地震震源深度

选取2022年2月20日山西古交M_(L)4.1地震事件波形,筛选出震中距在10~70 km之间的5个地震台站,基于sPL震相特征对波形数据进行处理,在震中距23 km的GUJ台观测到sPL震相。使用山西2015一维地壳速度模型,通过SEIS-CAP软件计算该地震的震源机制解,并利用F-K方法计算理论地震图,与观测波形拟合对比,确定该地震震源深度约4 km。sPL震相方法、CAP方法测定的震源深度和正式编目结果基本一致,表明利用sPL震相测定山西古交M_(L)4.1地震震源深度是可靠的。

地震沉积学中90°相位转换技术适用性的讨论

地震沉积学理论及应用技术研究中90°相位转换技术是地震沉积学研究的核心技术之一,但在实际研究与应用中,该技术并未取得如期的成果。在地震勘探原理中,地震反射同相轴代表着具有波阻抗差的地层界面。90°相位转换拟通过相移的方式,将代表着地层界面的地震反射同相轴赋于岩性地层的意义。由于地下地层的复杂多变,多种岩性频繁薄互层。利用薄互层地质模型的地震正演模拟结果,证实地震反射同相轴并不能与地层界面一一对应,因此,地震剖面中的同相轴是多套薄互层组合效应。受地震资料分辨力的限制,90°相位转换后的地震资料既不能将原始地震资料由界面型地震剖面转换成岩性地层剖面,也缺乏明确的地质含义,其在地震构造解释和地震岩性解释中的应用能力均很有限,所以很难称其为地震沉积学的应用技术。因此,将90°相位转换技术作为地震沉积学的核心技术并不合适,该技术无法提高地震资料分辨能力,也不能精确地将地震剖面转化成岩性剖面。

基于CCFE自动构建安徽霍山地区微震目录

使用单台波形信号中检测区域地震信号(震中距<150 km)并识别P波和S波震相的CCFE模型,利用安徽霍山地区佛子岭台2017-06~08记录到的连续波形数据进行地震检测和震相识别。共检测出164次地震,约为地震目录的2.16倍,使地震目录中ML-1.7~0.0范围内的地震完整性得到较明显的改善。检测到编目地震的P波和S波震相到时与编目结果相差0.03 s左右。同目前比较常见的深度学习模型CRED、EQT和GPD相比,CCFE模型对于震级较小及某些特殊情形,如2个距离较近,尤其是较大地震前震级相对小很多的地震波形,具备较高的地震检测和震相识别成功率。

双向近场地震下结构性软黏土震陷特性研究

软黏土的震陷效应与土体的结构性有着密切联系,但目前关于土体的结构强度对震陷影响的研究还较少。自然界中很难获取到物质成分相同而结构性质不同的土,为了对P波和S波耦合作用下结构性软黏土的震陷特性进行深入研究,人工制备了物质成分相近而结构强度不同的软黏土,通过开展两组动态围压下的动三轴试验,探讨结构强度、双向动荷载和P波与S波的相位差对其震陷特性的影响规律。试验结果表明:双向激振下,轴向动荷载与结构屈服应力比值越大,双向动荷载的耦合对轴向残余应变积累速度的加速作用越明显;相位差对轴向残余应变的影响则取决于土体结构强度,结构强度越小,影响越大。双向近场地震中,径向动荷载一般会加速震陷的发展,加速作用的大小与土体结构强度大小有关,结构强度越小,加速作用越明显,但轴向动荷载的剪切作用对结构性软黏土的残余应变积累仍起主导作用。研究结果可为软土地区综合防灾提供有益参考。

黄沙矿区复杂生产环境下的多通道微震信号辨识分析

文章介绍了铁山垅钨业有限公司黄沙矿区井下的各种震源,在广泛现场监测的基础上通过震相分析对地下各种信号如爆破、机械作业设备、人员作业、岩体破裂等产生的震源信号进行了辨识,给出了各种震源波形的基本特点;同时,在理论上采用傅里叶变换辨识方法进一步对受干扰信号的波形进行了分析,说明了采用该方法可以更好地排除噪音信号的干扰,提取有用信号、正确选取信号初到时刻。本文介绍的信号辨识方法简便易行,而且基本可以满足矿山日常监测中对岩体破裂微震信号的提取需要。