Selecting time windows of seismic phases and noise for engineering seismology applications: a versatile methodology and algorithm

Seismic signal windowing is the preliminary step for many analysis procedures in engineering seismology (standard spectral ratio, quality factor, general inversion techniques, etc.). Moreover a noise window is often necessary for the data quality control through the signal-to-noise verification. Selecting the noise window can be challenging when large heterogeneous datasets are considered, especially when they include short pre-event noise signals. This study proposes a fully automatic and configurable (i.e., with default parameters that can also be user-defined) algorithm to windowing the noise and the P, S, coda and full signal once the P-wave (T _P ) and S-wave (T _S ) first arrivals are known. An application example is given on a KiK-net dataset. A Matlab language implementation of this algorithm is proposed as an online resource.     

The influence of one-dimensional velocity sections on determining the key parameters of the earthquake sources in Azerbaijan

The paper addresses the construction of one-dimensional (1D) velocity models in the seismogenic regions of Azerbaijan taken individually and the analysis of implications of these models for estimating the key parameters of earthquake sources in Azerbaijan. We considered and analyzed the seismological data from the local earthquakes, the arrival times of the P-, P-g, Pn-, S-, Sg-, and Sn-waves recorded by the network of telemetry stations during the period from 2005 to 2014 with ml ≥ 2.5. For constructing the models, we used the VELEST program which calculates 1D velocity models from travel times of seismic waves. As a result, the 1D models were built for ten regions of Azerbaijan; the key parameters of the hypocenters of the earthquakes were recalculated; and the corrections to the body-wave arrival times at the observation stations were obtained, which increased the accuracy of locating the hypocenter of earthquakes.     

一种地震P波和S波初至时间自动拾取的新方法

地震P波、S波初至时间的拾取是地震波分析的一项基础性工作.本文提出了一种新的地震波初至时间自动拾取的方法:首先,把地震波的三分量时程曲线变换为一组空间向的能量变化率时程曲线;然后对能量变化率时程曲线进行STA/LTA(Short Time Average/Long Time Average,短时间的均值/长时间的均值)处理,拾取地震P波和S波的大致初至时间;最后提出采用一种二次方自回归模型对初至附近的能量变化率曲线进行二次方自回归处理,精确拾取出P波和S波的初至时间.本文采用了10组芦山地震的记录数据和150组汶川地震的记录数据对此方法的可靠性进行了检验.以人工拾取结果为参考,此方法具有很高的准确率和稳定性,同时,相比于常用的STA/LTA方法和AIC(Akaike Information Criterion,Akaike信息准则)方法,此方法在计算时间效率方面稍微逊色,但是对S波初至时间的拾取精度和可靠性更高.此方法丰富了地震P波、S波初至时间的自动拾取方法.     

Local Seismic Events in the Area of Poland Based on Data from the PASSEQ 2006–2008 Experiment

PASSEQ 2006–2008 (Passive Seismic Experiment in TESZ; Wilde-Piórko et al. 2008) was the biggest passive seismic experiment carried out so far in the area of Central Europe (Poland, Germany, the Czech Republic and Lithuania). 196 seismic stations (including 49 broadband seismometers) worked simultaneously for over two years. During the experiment, multiple types of data recorders and seismometers were used, making the analysis more complex and time consuming. The dataset was unified and repaired to start the detection of local seismic events. Two different approaches for detection were applied for stations located in Poland. The first one used standard STA/LTA triggers (Carl Johnson’s STA/LTA algorithm) and grid search to classify and locate the events. The result was manually verified. The second approach used Real Time Recurrent Network (RTRN) detection (Wiszniowski et al. 2014). Both methods gave similar results, showing four previously unknown seismic events located in the Gulf of Gdańsk area, situated in the southern Baltic Sea. In this paper we discuss both detection methods with their pros and cons (accuracy, efficiency, manual work required, scalability). We also show details of all detected and previously unknown events in the discussed area.     

Estimation of site effects using strong motion data of BYTNet array in Turkey

Simultaneous estimation of effects of source, propagation path, and local site amplification was carried out using observed strong motion records in a frequency range from 0.8 to 20 Hz for the purpose of empirical evaluation of the local site effects in different geological conditions in the northwestern part of Turkey. The analyzed data are S-wave portions of 162 accelerograms from 39 shallow events observed at 14 sites of BYTNet array. A spectral separation method was applied to the observed S-wave spectra. The solutions for source spectra, inelasticity factor of propagation path for S-waves (Q _s-value), and factor of site amplification at each site were obtained in a least squares sense. In the analysis, we assumed that the factor of the site amplification at a reference site is the same as that of theoretical amplification of S-waves to the soil model whose bottom layer has an S-wave velocity around 2.15 km/s. The estimated Q _s-value of the propagation path is modeled as Q _s(f)?=?87.4f^0.78. The estimated site amplifications are characterized into three groups. The sites in the first group belong to rock site with no dominant peaks at a frequency range of 2 to 10 Hz. The second group of hard soil sites is characterized with moderately dominant peaks at a frequency of 5 Hz. The last group for soft soil sites has common peaks at a frequency of 4 Hz with larger amplitudes than those in the hard soil group. We, then, compare the amplifications with average S-wave velocity in top 30 m of the shallow S-wave profiles and proposed linear empirical formula between them at each frequency. We, furthermore, inverted the observed amplification factors into S-wave velocity and Q _s-value profiles of the deep soil over the basement.     

Deep learning for P-wave arrival picking in earthquake early warning

Fast and accurate P-wave arrival picking significantly affects the performance of earthquake early warning(EEW)systems.Automated P-wave picking algorithms used in EEW have encountered problems of falsely picking up noise,missing P-waves and inaccurate P-wave arrival estimation.To address these issues,an automatic algorithm based on the convolution neural network(DPick)was developed,and trained with a moderate number of data sets of 17,717 accelerograms.Compared to the widely used approach of the short-term average/long-term average of signal characteristic function(STA/LTA),DPick is 1.6 times less likely to detect noise as a P-wave,and 76 times less likely to miss P-waves.In terms of estimating P-wave arrival time,when the detection task is completed within 1 s,DPick′s detection occurrence is 7.4 times that of STA/LTA in the 0.05 s error band,and 1.6 times when the error band is 0.10 s.This verified that the proposed method has the potential for wide applications in EEW.     

Crustal Structure Along Sunda-Banda Arc Transition Zone from Teleseismic Receiver Functions

We analyzed receiver function of teleseismic events recorded at twelve Indonesian-GEOFON (IA-GE) broadband stations using nonlinear Neighbourhood Algorithm (NA) inversion and H-k stacking methods to estimate crustal thickness, V _p /V _s ratios and S-wave velocity structure along Sunda-Banda arc transition zone. We observed crustal thickness of 34–37 km in Timor Island, which is consistent with the previous works. The thick crust (> 30 km) is also found beneath Sumba and Flores Islands, which might be related to the arc-continent collision causing the thickened crust. In Timor and Sumba Islands, we observed high V _p /V _s ratio (> 1.84) with low velocity zone that might be associated with the presence of mafic and ultramafic materials and fluid filled fracture zone. The high V _p /V _s ratio observed at Sumbawa and Flores volcanic Islands might be an indication of partial melt related to the upwelling of hot asthenosphere material through the subducted slab.     

Magnetic field variations in the umbra of single sunspots during their passage across the solar disk

Temporal variations of the maximum (B _max) and average (〈B〉) magnetic inductions, minimum (α _min) and average (〈α〉) inclination angles of the field lines to the radial direction from the center of the Sun, and areas of the sunspot umbra S in the umbra of single sunspots during their passage across the solar disk are investigated. The variation of the properties of single sunspots has been considered at different stages of their existence, i.e., during formation, the “quiet” period, and the disappearance stage. It has been found that, for the majority of the selected single sunspots, there is a positive correlation between B _max and S and between 〈B〉 and S defined at different times during the passage of sunspots across the solar disk. It is shown in this case that the nature of the dependence between the parameters α _min and B _max, α _min and S, as well as between 〈α〉 and 〈B〉, 〈α〉 and S, can vary from sunspot to sunspot, but for many sunspots the inclination angle of the field lines decreases on average with the growth of the sunspot umbra area and the field strength.     

A study of Guptkashi,Uttarakhand earthquake of 6 February 2017 (<Emphasis Type="Italic">M</Emphasis><Subscript><Emphasis Type="Italic">w</Emphasis></Subscript>5.3) in the Himalayan arc and implications for ground motion estimation

The 2017 Guptkashi earthquake occurred in a segment of the Himalayan arc with high potential for a strong earthquake in the near future. In this context, a careful analysis of the earthquake is important as it may shed light on source and ground motion characteristics during future earthquakes. Using the earthquake recording on a single broadband strong-motion seismograph installed at the epicenter, we estimate the earthquake’s location (30.546° N, 79.063° E), depth (H?=?19 km), the seismic moment (M₀?=?1.12×10¹⁷ Nm, M _w 5.3), the focal mechanism (φ?=?280°, δ?=?14°, λ?=?84°), the source radius (a?=?1.3 km), and the static stress drop (Δσ _s ~22 MPa). The event occurred just above the Main Himalayan Thrust. S-wave spectra of the earthquake at hard sites in the arc are well approximated (assuming ω^?2 source model) by attenuation parameters Q(f)?=?500f^0.9, κ?=?0.04 s, and f_max?=?infinite, and a stress drop of Δσ?=?70 MPa. Observed and computed peak ground motions, using stochastic method along with parameters inferred from spectral analysis, agree well with each other. These attenuation parameters are also reasonable for the observed spectra and/or peak ground motion parameters in the arc at distances ≤?200 km during five other earthquakes in the region (4.6?≤?M _w ?≤?6.9). The estimated stress drop of the six events ranges from 20 to 120 MPa. Our analysis suggests that attenuation parameters given above may be used for ground motion estimation at hard sites in the Himalayan arc via the stochastic method.     

New magnitude scaling relations for earthquake early warning in the Alborz region,Iran

Rapid magnitude estimation relations for earthquake early warning systems in the Alborz region have been developed based on the initial first seconds of the P-wave arrival. For this purpose, a total of 717 accelerograms recorded by the Building and Housing Research Center in the Alborz region with the magnitude (M_w) range of 4.8–6.5 in the period between 1995 and 2013 were employed. Average ground motion period (\( \tau_{\text{c}} \)) and peak displacement (\( P_{\text{d}} \)) in different time windows from the P-wave arrival were calculated, and their relation with magnitude was examined. Four earthquakes that were excluded from the analysis process were used to validate the results, and the estimated magnitudes were found to be in good agreement with the observed ones. The results show that using the proposed relations for the Alborz region, earthquake magnitude could be estimated with acceptable accuracy even after 1 s of the P-wave arrival.     

The seismic wave absorption in the crust and upper mantle in the vicinity of the Kislovodsk seismic station

The Q-factor estimates of the Earth’s crust and upper mantle as the functions of frequency (Q(f)) are obtained for the seismic S-waves at frequencies up to ~35 Hz. The estimates are based on the data for ~40 earthquakes recorded by the Kislovodsk seismic station since 2000. The magnitudes of these events are M_W > 3.8, the sources are located in the depth interval from 1 to 165 km, and the epicentral distances range from ~100 to 300 km. The Q-factor estimates are obtained by the methods developed by Aki and Rautian et al., which employ the suppression of the effects of the source radiation spectrum and local site responses in the S-wave spectra by the coda waves measured at a fixed lapse time (time from the first arrival). The radiation pattern effects are cancelled by averaging over many events whose sources are distributed in a wide azimuthal sector centered at the receiving site. The geometrical spreading was specified in the form of a piecewise-continuous function of distance which behaves as 1/R at the distances from 1 to 50 km from the source, has a plateau at 1/50 in the interval from 50–70 km to 130–150 km, and decays as \({\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 {\sqrt R }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${\sqrt R }$}}\) beyond 130–150 km. For this geometrical spreading model and some of its modifications, the following Q-factor estimates are obtained: Q(f) ~ 85f^0.9 at the frequencies ranging from ~1 to 20 Hz and Q(f) ~ 75f^1.0 at the frequencies ranging from ~1 to 35 Hz.     

Stochastic models of severe weather watches and warnings: transition probabilities

To alert the public to the possibility of tornado (T), hail (H), or convective wind (C), the National Weather Service (NWS) issues watches (V) and warnings (W). There are severe thunderstorm watches (SV), tornado watches (TV), and particularly dangerous situation watches (PV); and there are severe thunderstorm warnings (SW), and tornado warnings (TW). Two stochastic models are formulated that quantify uncertainty in severe weather alarms for the purpose of making decisions: a one-stage model for deciders who respond to warnings, and a two-stage model for deciders who respond to watches and warnings. The models identify all possible sequences of watches, warnings, and events, and characterize the associated uncertainties in terms of transition probabilities. The modeling approach is demonstrated on data from the NWS Norman, Oklahoma, warning area, years 2000–2007. The major findings are these. (i) Irrespective of its official designation, every warning type {SW, TW} predicts with a significant probability every event type {T, H, C}. (ii) An ordered intersection of SW and TW, defined as reinforced warning (RW), provides additional predictive information and outperforms SW and TW. (iii) A watch rarely leads directly to an event, and most frequently is false. But a watch that precedes a warning does matter. The watch type \(\{SV\), TV, \(PV\}\) is a predictor of the warning type \(\{SW\), RW, \(TW\}\) and of the warning performance: It sharpens the false alarm rate of the warning and the predictive probability of an event, and it increases the average lead time of the warning.     

Variation of <Emphasis Type="Italic">Q</Emphasis> value before and after the 1999 Xiuyan,Liaoning Province, <Emphasis Type="Italic">M</Emphasis>=5.4 earthquake on the basis of analysis on attenuation dispersion of P waves

A method for determining medium quality factor is developed on the basis of analyzing the attenuation dispersion of the arrived first period P wave. In order to enhance signal to noise ratio, improve the resolution in measurement and reduce systematic error we applied the data resampling technique. The group velocity delay of P wave was derived by using an improved multi-filtering method. Based on a linear viscoelastic relaxation model we deduced the medium quality factor Q _m, and associated error with 95% confidence level. Applying the method to the seismic record of the Xiuyan M=5.4 earthquake sequences we obtained the following result: (1) High Q _m started to appear from Nov. 9, 1999. The events giving the deduced high Q _m value clustered in a region with their epicenter distances being between 32 and 46 km to the Yingkou station. This Q _m versus distance observation obviously deviates from the normal trend of Q _m linearly increasing with distance. (2) The average Q _m before the 29 Dec. 1999 M=5.4 earthquake is 460, while the average Q _m between the M=5.4 event and the 12 Jan. 2000 M=5.1 earthquake is 391, and the average Q _m after the M=5.1 event is 204.     

Precursor times of abnormal <Emphasis Type="Italic">b</Emphasis>-values prior to mainshocks

Seismic observations exhibit the presence of abnormal b-values prior to numerous earthquakes. The time interval from the appearance of abnormal b-values to the occurrence of mainshock is called the precursor time. There are two kinds of precursor times in use: the first one denoted by T is the time interval from the moment when the b-value starts to increase from the normal one to the abnormal one to the occurrence time of the forthcoming mainshock, and the second one denoted by T _p is the time interval from the moment when the abnormal b-value reaches the peak one to the occurrence time of the forthcoming mainshock. Let T* be the waiting time from the moment when the abnormal b-value returned to the normal one to the occurrence time of the forthcoming mainshock. The precursor time, T (usually in days), has been found to be related to the magnitude, M, of the mainshock expected in a linear form as log(T)?=?q?+?rM where q and r are the coefficient and slope, respectively. In this study, the values of T, T _p , and T* of 45 earthquakes with 3?≤?M?≤?9 occurred in various tectonic regions are compiled from or measured from the temporal variations in b-values given in numerous source materials. The relationships of T and T _p , respectively, versus M are inferred from compiled data. The difference between the values of T and T _p decreases with increasing M. In addition, the plots of T*/T versus M, T* versus T, and T* versus T-T* will be made and related equations between two quantities will be inferred from given data.     

Behavior of Parameters of Enhancements in the Nighttime Electron Concentration in the Ionospheric <Emphasis Type="Italic">F</Emphasis>2 Layer

We have analyzed the behavior of the F2 layer parameters during nighttime periods of enhanced electron concentration by the results of vertical sounding of the ionosphere carried out with five-minute periodicity in Almaty (76°55′ E, 43°15′ N) in 2001–2012. The results are obtained within the frameworks of the unified concept of different types of ionospheric plasma disturbances manifested as variations in the height and half-thickness of the layer accompanied by an increase and decrease of N _m F2 at the moments of maximum compression and expansion of the layer. A good correlation is found between height h _Am , which corresponds to the maximum increase, and layer peak height h _m F, while h _Am is always less than h _m F. The difference between h _Am and h _m F linearly increases with increasing h _m F. Whereas the difference is ~38 km for h _m F = 280 km, it is ~54 km for h _m F = 380 km. Additionally, the correlation is good between the increase in the electron concentration in the layer maximum ΔN _m and the maximum enhancement at the fixed height ΔN; the electron concentration enhancement in the layer maximum is about two to three times lower than its maximum enhancement at the fixed height.     

Resources and environmental systems management under synchronic interval uncertainties

Resources and environmental systems management (RESM) is challenged by the synchronic effects of interval uncertainties in the related practices. The synchronic interval uncertainties are misrepresented as random variables, fuzzy sets, or interval numbers in conventional RESM programming techniques including stochastic programming. This may lead to ineffectiveness of resources allocation, high costs of recourse measures, increased risks of unreasonable decisions, and decreased optimality of system profits. To fill the gap of few corresponding studies, a synchronic interval linear programming (SILP) method is proposed in this study. The proposition of interval sets and interval functions and coupling them with linear programming models lead to development of an SILP model for RESM. This enables incorporation of interval uncertainties in resource constraints and synchronic interval uncertainties in the programming objective into the optimization process. An analysis of the distribution-independent geometric properties of the feasible regions of SILP models results in proposition of constraint violation likelihoods. The tradeoff between system optimality and constraint violation is analyzed. The overall optimality of SILP systems under synchronic intervalness is quantified through proposition of integrally optimal solutions. Integration of these efforts leads to a violation-constrained interval integral method for optimization of RESM systems under synchronic interval uncertainties. Comparisons with selected existing methods reveal the effectiveness of SILP at eliminating negativity of synchronic intervalness, enabling risk management of and achieving overall optimality of RESM systems, and enhancing the reliability of optimization techniques for RESM problems. The exploited framework for analyzing synchronic interval uncertainties in RESM systems is helpful for addressing synchronisms of other uncertainties such as randomness or fuzziness and avoiding the resultant decision mistakes and disasters due to neglecting them.     

A novel approach for classification of earthquake ground-motion records

This paper presents a new clustering procedure based on K-means and self-organizing map (SOM) network algorithms for classification of earthquake ground-motion records. Six scalar indicators are used in data analysis for describing the frequency content features of earthquake ground motions, named as the average spectral period (T _avg ), the mean period (T _m ), the smoothed spectral predominant period (T ₀), the characteristic period (T _4.3), the predominant period based on velocity spectrum (T _gSv ), and the shape factor (Ω). Different clustering validity indexes were applied to determine the best estimates of the number of clusters on real and synthetic data. Results showed the high performance of proposed procedure to reveal salient features of complex seismic data. The comparison between the results of clustering analyses recommend the smoothed spectral predominant period as an effective indicator to describe ground-motion classes. The results also showed that K-means algorithm has better performance than SOM algorithm in identification and classification procedure of ground-motion records.     

基于小波包和峰度赤池信息量准则的P波震相自动识别方法

基于小波包变换和峰度赤池信息量准则(AIC), 提出了一种新的自动识别P波震相的综合方法, 即小波包-峰度AIC方法． 首先对由加权长短时窗平均比(STA/LTA)法粗略确定的P波到时前后3 s的记录进行小波包三尺度的分解与重构, 分别计算每个尺度重构信号的峰度AIC曲线并将其叠加, 叠加曲线的最小值则为P波震相到时; 然后对原始地震记录进行有限冲激响应自适应滤波以提高信噪比和识别精度; 最后将小波包-峰度AIC方法应用到合成理论地震图及实际地震记录的P波初至自动识别中. 结果表明: 初至清晰度对识别精度的影响比信噪比对其影响更大; 与单独使用加权STA/LTA方法和峰度AIC法相比, 小波包-峰度AIC法具有更强的抗噪能力, 识别精度更高; 当初至清晰时, 小波包-峰度AIC法自动识别与人工识别的P波到时平均绝对差值为(0.077±0.075) s．      

A first-order seismotectonic regionalization of Mexico for seismic hazard and risk estimation

The purpose of this work is to define a seismic regionalization of Mexico for seismic hazard and risk analyses. This seismic regionalization is based on seismic, geologic, and tectonic characteristics. To this end, a seismic catalog was compiled using the more reliable sources available. The catalog was made homogeneous in magnitude in order to avoid the differences in the way this parameter is reported by various agencies. Instead of using a linear regression to converts from m _b and M _d to M _s or M _w , using only events for which estimates of both magnitudes are available (i.e., paired data), we used the frequency-magnitude relations relying on the a and b values of the Gutenberg-Richter relation. The seismic regions are divided into three main categories: seismicity associated with the subduction process along the Pacific coast of Mexico, in-slab events within the down-going COC and RIV plates, and crustal seismicity associated to various geologic and tectonic regions. In total, 18 seismic regions were identified and delimited. For each, the a and b values of the Gutenberg-Richter relation were determined using a maximum likelihood estimation. The a and b parameters were repeatedly estimated as a function of time for each region, in order to confirm their reliability and stability. The recurrence times predicted by the resulting Gutenberg-Richter relations obtained are compared with the observed recurrence times of the larger events in each region of both historical and instrumental earthquakes.     

Site Specific Ground Motion Modeling and Seismic Response Analysis for Microzonation of Baku,Azerbaijan

We investigated ground response for Baku (Azerbaijan) from two earthquakes of magnitude M6.3 occurred in Caspian Sea (characterized as a near event) and M7.5 in Shamakhi (characterized as a remote extreme event). S-wave velocity with the average shear wave velocity over the topmost 30 m of soil is obtained by experimental method from the V _P values measured for the soils. The downtown part of Baku city is characterized by low V_S30 values (< 250 m/s), related to sand, water-saturated sand, gravel-pebble, and limestone with clay. High surface PGA of 240 gal for the M7.5 event and of about 190 gal for the M6.3 event, and hence a high ground motion amplification, is observed in the shoreline area, through downtown, in the north-west, and in the east parts of Baku city with soft clays, loamy sands, gravel, sediments.