Influence of parameters selection in Chebyshev filters on the strong motion data processing

This contribution discusses the application of Chebyshev Type I filter for processing real earthquake records. Consideration is given to the effects of filtering parameters (passband amplitude ripple and order of the filter) on the time series, strong-motion parameters, Fourier Amplitude Spectrum of acceleration, and elastic displacement response spectra. Time histories of five earthquakes with different moment magnitudes have been examined (from stations located close to the epicenters). Data processing is based on application of bandpass Chebyshev filtering over frequency range with substantial signal to noise ratio (level of 3 or approximately 3 dB). Applying different filters, we have monitored several important strong-motion parameters: peak values of acceleration, velocity, and displacement; Arias intensity, acceleration/velocity spectrum intensity, significant duration, etc. Some new results and conclusions concerning the influence of Chebyshev filter in data processing of records have been summarized. The graphical and numerical outcomes obtained, as well as the comparison with a Butterworth causal filter, are included in the work. The results could be potentially useful to engineering seismologists who need to evaluate and better understand the merits of this type of filtering for strong-motion data processing.     

Estimating coseismic deformations from near source strong motion records: methods and case studies

Digital strong-motion accelerographs have opened up the possibility of extracting ground motion characteristics at much lower frequencies than was offered by analogue instruments. High-quality digital data obtained close to the faults have tempted several efforts to retrieve permanent ground displacements after an earthquake. Such attempts have been partly successful, and somewhat subjective, the main reason being the presence of baseline offsets in the accelerometric data. We review existing methods for such applications, discuss their limitations and propose a more objective and improved scheme to make baseline adjustments and obtain permanent displacements. The proposed technique is applied to 26 digital recordings from the 29 May 2008 Ölfus Earthquake in South Iceland and 9 recordings from the 1999 Chi-Chi, Taiwan, Earthquake, and the permanent displacements obtained are compared with published results and GPS measurements from nearby stations. Our case studies show that the proposed technique, in addition to being simple and objective, is effective in making adjustments for baseline errors in accelerometric data.     

Processing of strong-motion accelerograms: needs, options and consequences

Recordings from strong-motion accelerographs are of fundamental importance in earthquake engineering, forming the basis for all characterizations of ground shaking employed for seismic design. The recordings, particularly those from analog instruments, invariably contain noise that can mask and distort the ground-motion signal at both high and low frequencies. For any application of recorded accelerograms in engineering seismology or earthquake engineering, it is important to identify the presence of this noise in the digitized time-history and its influence on the parameters that are to be derived from the records. If the parameters of interest are affected by noise then appropriate processing needs to be applied to the records, although it must be accepted from the outset that it is generally not possible to recover the actual ground motion over a wide range of frequencies. There are many schemes available for processing strong-motion data and it is important to be aware of the merits and pitfalls associated with each option. Equally important is to appreciate the effects of the procedures on the records in order to avoid errors in the interpretation and use of the results. Options for processing strong-motion accelerograms are presented, discussed and evaluated from the perspective of engineering application.     

Reference database for seismic ground-motion in Europe (RESORCE)

This paper presents the overall procedure followed in order to assemble the most recent pan-European strong-motion databank: Reference Database for Seismic Ground-Motion in Europe (RESORCE). RESORCE is one of the products of the SeIsmic Ground Motion Assessment (SIGMA; projet-sigma.com) project. RESORCE is intended to be a single integrated accelerometric databank for Europe and surrounding areas for use in the development and testing of ground-motion models and for other engineering seismology and earthquake engineering applications. RESORCE aims to contribute to the improvement of earthquake risk studies in Europe and surrounding areas. RESORCE principally updates and extends the previous pan-European strong-motion databank (Ambraseys et al. in Bollettino di Geofisica Teorica ed Applicata 45:113–129, 2004a) with recently compiled Greek, Italian, Swiss and Turkish accelerometric archives. The updates also include earthquake-specific studies published in recent years. The current content of RESORCE includes 5,882 multi-component and uniformly processed accelerograms from 1,814 events and 1,540 strong-motion stations. The moment magnitude range covered by RESORCE is $2.8 \le \hbox {M}_{\mathrm{w}} \le 7.8$ . The source-to-site distance interval extends to 587 km and distance information is given by the common point- and extended-source distance measures. The paper presents the current features of RESORCE through simple statistics that also quantify the differences in metadata and strong-motion processing with respect to the previous version of the pan-European strong-motion databank.     

基于L1范数正则化的强震动加速度记录基线漂移识别方法

本文提出了一种基于L₁范数正则化的基线校正新方法,即以拟合速度时程误差最小为目标,以基线漂移本身尽可能小为约束条件,经过凸优化多次迭代自动求解出满足条件的基线漂移,避免了人为选取基线漂移分段次数和基线漂移起止时刻的主观干扰;随后利用该方法对多组加入了基线漂移噪声模型的强震动加速度记录进行验证。结果表明:本文方法对于识别和处理单段式、两段式和多段式的基线漂移噪声具有普适性,能敏锐地捕捉到速度时程发生漂移的趋势（斜率变化）,无需预先设定加速度基线漂移模型也可有效地识别出多种基线漂移噪声的起止位置和漂移程度;地震记录事前部分对本文方法处理结果影响较大,当记录事前部分足够长时（如20 s）,识别基线漂移噪声的准确性较高,位移时程可以较好地与原始位移匹配;而对于发生漂移的速度时程,本文方法可以不受地震事前部分长短的干扰,甚至在加速度记录出现明显丢头现象时,也能很好地实现峰值速度和整个速度时程的恢复。      

Filtering vibroseis data in the precorrelation domain

Vibroseis data recorded at short source–receiver offsets can be swamped by direct waves from the source. The signal-to-noise ratio, where primary reflections are the signal and correlation side lobes are the noise, decreases with time and late reflection events are overwhelmed. This leads to low seismic resolution on the vibroseis correlogram. A new precorrelation filtering approach is proposed to suppress correlation noise. It is the ‘squeeze-filter-unsqueeze’ (SFU) process, a combination of ‘squeeze’ and ‘unsqueeze’ (S and U) transformations, together with the application of either an optimum least-squares filter or a linear recursive notch filter. SFU processing provides excellent direct wave removal if the onset time of the direct wave is known precisely, but when the correlation recognition method used to search for the first arrival fails, the SFU filtering will also fail. If the tapers of the source sweeps are badly distorted, a harmonic distortion will be introduced into the SFU-filtered trace. SFU appears to be more suitable for low-noise vibroseis data, and more effective when we know the sweep tapers exactly. SFU requires uncorrelated data, and is thus cpu intensive, but since it is automatic, it is not labour intensive. With non-linear sweeps, there are two approaches to the S,U transformations in SFU. The first requires the non-linear analytical sweep formula, and the second is to search and pick the zero nodes on the recorded pilot trace and then carry out the S,U transformations directly without requiring the algorithm or formula by which the sweep was generated. The latter method is also valid for vibroseis data with a linear sweep. SFU may be applied to the removal of any undesired signal, as long as the exact onset time of the unwanted signal in the precorrelation domain is known or determinable.     

High-frequency filtering of strong-motion records

The influence of noise in strong-motion records is most problematic at low and high frequencies where the signal to noise ratio is commonly low compared to that in the mid-spectrum. The impact of low-frequency noise (<1 Hz) on strong-motion intensity parameters such as ground velocities, displacements and response spectral ordinates can be dramatic and consequentially it has become standard practice to low-cut (high-pass) filter strong-motion data with corner frequencies often chosen based on the shape of Fourier amplitude spectra and the signal-to-noise ratio. It has been shown that response spectral ordinates should not be used beyond some fraction of the corner period (reciprocal of the corner frequency) of the low-cut filter. This article examines the effect of high-frequency noise (>5 Hz) on computed pseudo-absolute response spectral accelerations (PSAs). In contrast to the case of low-frequency noise our analysis shows that filtering to remove high-frequency noise is only necessary in certain situations and that PSAs can often be used up to 100 Hz even if much lower high-cut corner frequencies are required to remove the noise. This apparent contradiction can be explained by the fact that PSAs are often controlled by ground accelerations associated with much lower frequencies than the natural frequency of the oscillator because path and site attenuation (often modelled by Q and κ, respectively) have removed the highest frequencies. We demonstrate that if high-cut filters are to be used, then their corner frequencies should be selected on an individual basis, as has been done in a few recent studies.     

What is a Poor Quality Strong-Motion Record?

Some accelerograms are affected by non-standard recording and digitization problems that mean they are often not used in strong-motion studies. These non-standard problems cannot be corrected by the standard processing techniques that remove low and high-frequency noise from the time-history. Records from analogue instruments are more prone to these problems but even records from digital instruments, which are becoming increasingly common, can be affected by such errors. Since all strong-motion data is valuable it is important to know whether any useful information can be obtained from accelerograms that are affected by such problems. This article examines whether strong-motion records from analogue instruments that are missing their initial part due to late triggering of the instrument and also strong-motion records from digital instruments with low A/D converter resolution can be used for response spectral studies. It is found, by simulating such errors on high-quality strong-motion records, that good response spectral ordinates can be obtained from such `poor-quality' records within the period range of most engineering interest. This revised version was published online in July 2006 with corrections to the Cover Date.     

Application of a cascading filter implemented using morphological filtering and time–frequency peak filtering for seismic signal enhancement

Inspired by the idea of the iterative time–frequency peak filtering, which applies time–frequency peak filtering several times to improve the ability of random noise reduction, this article proposes a new cascading filter implemented using mathematic morphological filtering and the time–frequency peak filtering, which we call here morphological time–frequency peak filtering for convenience. This new method will be used mainly for seismic signal enhancement and random noise reduction in which the advantages of the morphological algorithm in processing nonlinear signals and the time–frequency peak filtering in processing nonstationary signals are utilized. Structurally, the scheme of the proposed method adopts mathematic morphological operation to first preprocess the signal and then applies the time–frequency peak filtering method to ultimately extract the valid signal. Through experiments on synthetic seismic signals and field seismic data, this paper demonstrates that the morphological time–frequency peak filtering method is superior to the time–frequency peak filtering method and its iterative form in terms of valid signal enhancement and random noise reduction.     

强震动记录的低频截止频率自动搜索算法

本文针对传统方法在强震动记录处理中确定滤波低频截止频率效率较低的问题,提出一种确定滤波低频截止频率的自动搜索模型,并利用统计学习方法中的损失函数确定自动搜索模型流程的结束条件。基于2008年汶川和2013年芦山两次地震主震和余震获得的强震动记录,将自动搜索算法得出的结果与传统方法确定的低频截止频率进行比较,分析自动搜索算法产生误差的原因,进而提出自动搜索算法应遵循的原则和适用条件。结果表明该算法计算效率比传统方法有很大提升,特别适合海量强震动记录的批量处理。     

基于经验模态分解的分数维地震随机噪声衰减方法

经验模态分解算法(EMD)是一种基于有效波和噪声尺度差异进行波场分离的随机噪声压制方法,但由于实际地震数据波场复杂,导致模态混叠较严重,仅凭该方法进行去噪很难达到理想效果.本文基于EMD算法对信号多尺度的分解特性,结合Hausdorff维数约束条件,提出一种用于地震随机噪声衰减的新方法.首先对地震数据进行EMD自适应分解,得到一系列具有不同尺度的、分形自相似性的固有模态分量(IMF);在此基础上,基于有效信号和随机噪声的Hausdorff维数差异,识别混有随机噪声的IMF分量,对该分量进行相关的阈值滤波处理,从而实现有效信号和随机噪声的有效分离.文中从仿真信号试验出发,到模型地震数据和实际地震数据的测试处理,同时与传统的EMD处理结果相对比.结果表明,本文方法对地震随机噪声的衰减有更佳的压制效果.     

GHM类正交多小波变换及其在地震资料去噪中的应用

多小波是对小波理论的一个新发展,它可以同时满足正交性、对称性、短支撑等良好的特性要求。文中介绍了多小波基本理论、多小波变换具体过程及预处理方法,提出了基于GHM类多小波变换的地震资料软阈值去噪方法,通过对合成数据和实际资料进行处理分析,表明多小波变换在有效压制随机噪声的同时,能较好地保留原信号的特征信息,是一种行之有效的去噪方法     

A new algorithm for tight integration of real-time GPS and strong-motion records, demonstrated on simulated, experimental, and real seismic data

The complementary advantages of GPS and seismic measurements are well recognized in seismotectonic monitoring studies. Therefore, integrated processing of the two data streams has been proposed recently in an attempt to obtain accurate and reliable information of surface displacements associated with earthquakes. A hitherto still critical issue in the integrated processing is real-time detection and precise estimation of the transient baseline error in the seismic records. Here, we report on a new approach by introducing the seismic acceleration corrected by baseline errors into the state equation system. The correction is performed and regularly updated in short epochs (with increments which may be as short as seconds), so that station position, velocity, and acceleration can be constrained very tightly and baseline error can be estimated as a random-walk process. With the adapted state equation system, our study highlights the use of a new approach developed for integrated processing of GPS and seismic data by means of sequential least-squares adjustment. The efficiency of our approach is demonstrated and validated using simulated, experimental, and real datasets. The latter were collected at collocated GPS and seismic stations around the 4 April 2010, E1 Mayor-Cucapah earthquake (Mw, 7.2). The results have shown that baseline errors of the strong-motion sensors are corrected precisely and high-precision seismic displacements are real-timely obtained by the new approach.     

A note on the L’Aquila earthquake of 6 April 2009: Permanent ground displacements obtained from strong-motion accelerograms

On 6 April 2009, 01:32 GMT, an M_w 6.3 earthquake hit the Abruzzi region of central Italy causing widespread damage in the City of L’Aquila and its nearby villages. The mainshock of this earthquake was recorded by 57 digital strong-motion instruments, four of which are located on the hanging wall of the Paganica Fault near L’Aquila. These stations are no more than 6 km from the epicentre. We use accelerometric data from these four stations to estimate permanent ground displacements caused by the mainshock. Our numerical results reveal south-east and downwards directed permanent co-seismic displacements which are in fair agreement with the outcomes of GPS and InSAR measurements reported in preliminary Istituto Nazionale di Geofisica e Vulcanologia (INGV) reports.     

Median filter behaviour with seismic data1

Median filters may be used with seismic data to attenuate coherent wavefields. An example is the attenuation of the downgoing wavefield in VSP data processing. The filter is applied across the traces in the ‘direction’ of the wavefield. The final result is given by subtracting the filtered version of the record from the original record. This method of median filtering may be called ‘median filtering operated in subtraction’. The method may be extended by automatically estimating the slowness of coherent wavefields on a record. The filter is then applied in a time- and-space varying manner across the record on the basis of the slowness values at each point on the record. Median filters are non-linear and hence their behaviour is more difficult to determine than linear filters. However, there are a number of methods that may be used to analyse median filter behaviour: (1) pseudo-transfer functions to specific time series; (2) the response of median filters to simple seismic models; and (3) the response of median filters to steps that simulate terminating wavefields, such as faults on stacked data. These simple methods provide an intuitive insight into the behaviour of these filters, as well as providing a semiquantitative measurement of performance. The performance degradation of median filters in the presence of trace-to-trace variations in amplitude is shown to be similar to that of linear filters. The performance of median filters (in terms of signal distortion) applied obliquely across a record may be improved by low-pass filtering (in the t-dimension). The response of median filters to steps is shown to be affected by background noise levels. The distortion of steps introduced by median filters approaches the distortion of steps introduced by the corresponding linear filter for high levels of noise.     

Incorporation of a Non-linear Image Filtering Technique for Noise Reduction in Seismic Data

Seismic noise is a fundamental part of seismic data which cannot be avoided when conducting any seismic survey. It consists of coherent and random noise. Noise removal or filtering is one of the major concerns in the field of seismic processing. In this paper, we introduce an image filtering technique based on a detection-estimation algorithm for Gaussian and random noise removal in seismic data, namely the trilateral filter, based on a statistic called rank-ordered absolute differences. The non-linear and adaptive behaviour of this filter makes it very robust in the presence of random and coherent noise, in addition to its computational simplicity and its ability to automatically identify noise in data. We have modified the strategy of trilateral filtering by adapting the rank-ordered absolute differences formula in order to extract the signal component. We have successfully used this filter for the removal of surface waves and random spiky noise from synthetic and field data. Results are very encouraging and show the superiority of this filter compared with other filters, particularly when used recursively.     

Using the Wiener–Levinson algorithm to suppress ground-roll

In land seismic surveys, the seismic data are mostly contaminated by ground-roll noise, high amplitude and low frequency. Since the ground-roll is coherent with reflections and depends on the source, the spectral band of seismic signal and ground-roll always overlap, which can be clearly seen in the spectral domain. So, separating them in time or frequency domain commonly causes waveform distortions and information missing due to cut-off effects. Therefore, the combination of these factors leads to search for alternative filtering methods or processes. We applied the conventional Wiener–Levinson algorithm to extract ground-roll from the seismic data. Then, subtracting it from the seismic data arithmetically performs the ground-roll suppression. To set up the algorithm, linear or nonlinear sweep signals are used as reference noise trace. The frequencies needed in creating a reference noise trace using analytical sweep signal can be approximately estimated in spectral domain. The application of the proposed method based on redesigning of Wiener–Levinson algorithm differs from the usual frequency filtering techniques since the ground-roll is suppressed without cutting signal spectrum. The method is firstly tested on synthetics and then is applied to a shot data from the field. The result obtained from both synthetics and field data show that the ground-roll suppression in this way causes no waveform distortion and no reduction of frequency bandwidth of the data.     

音频大地电磁法“死频带”畸变数据的Rhoplus校正

为研究AMT数据的"死频带"畸变特征及其校正方法,在长江中下游地区,利用凤凰MTU-5A仪器,系统观测了音频电磁场的"死频带"数据.对不同季节、不同时段的AMT"死频带"数据进行了分析.提出AMT"死频带"频率域畸变数据的Rhoplus校正方法,给出了该方法的适用条件、关键技术与评价方案,并提供了大量实测数据论证其应用效果.观测实验表明:在"死频带"内天然音频电磁场信号强度极低,正交电磁场分量相干度极低;造成了阻抗视电阻率、相位及相位张量等数据的畸变;畸变范围可达10k~100Hz,以5~1kHz频段最为明显;并且数据畸变程度与观测时段密切相关,在长江中下游地区为秋冬季畸变强夏季弱,日间畸变强夜间弱.处理实例表明:Rhoplus校正方法可以有效地处理AMT"死频带"内畸变的视电阻率、相位数据,相干度阈值与人机交互相结合的处理策略快速客观,所得结果曲线光滑连续,数据与实测未畸变数据基本一致.     

Approximate coseismic displacements and near-field fling pulse from the Mw 6.5 30 October 2016 Central Italy earthquake

A study of the coseismic displacement and fling pulse recorded during the Mw 6.5 30 October 2016 Central Italy earthquake is presented. The near-field has been well documented, owing to the deployment of additional strong-motion stations following the earlier events of the 2016 Central Italy seismic sequence. As a result, there are numerous stations with evidence of coseismic displacement and fling pulse. In this study, 25 records with strike distance of less than 25 km and rupture distance under 28 km are considered. Approximate coseismic displacements have been recovered by a bilinear model to remove the low frequency noise in the records. The bilinear noise model uses two linear regression segments on the velocity trace to remove baseline offsets. After obtaining the coseismic displacement time series, the fling pulse period is examined. Existing methods of obtaining the fling pulse period are reviewed and a proposed algorithm is considered for automatic fling pulse detection. Both horizontal and vertical fling periods are obtained, unlike many studies which neglect the vertical fling. It is shown that the fling pulse period is highly variable (~?2–16 s) in the near-field region but exhibits some trends with various site-to-source distances.