Velocity Spectra Research Articles

Statistical Characteristics of Mesoscale Fluctuations of Wind Velocity, Temperature, and Gas Concentrations Obtained from Aircraft Measurements in the Troposphere of the Arctic Region

Abstract The results of airborne measurements and statistical characteristics of mesoscale fluctuations of wind velocity, temperature, and concentrations of gas constituents at different heights of a stably stratified troposphere are presented. The measurements were carried out in September 2022 in the Arctic region of Russia with the aircraft laboratory Tu-134 “Optik.” The obtained spectra and structure functions of the fluctuations are interpreted with the theoretical model of formation of the spectrum of mesoscale wind velocity and temperature fluctuations described in the paper. The presence at high wavenumbers of a steep section in the obtained horizontal wavenumber spectra of the fluctuations of wind velocity and greenhouse gas concentration with a slope close to −3 is discussed. The fluctuation spectra along different slanted tracks of the aircraft crossing the tropospheric layer between altitudes of 1 and 9 km are also obtained and analyzed with the theoretical model.

A new machine learning approach for estimating shear wave velocity profile using borelog data

The shear wave velocity is among the key parameters that are responsible for damage caused during an earthquake. Determining shear wave velocity is a costly and time-consuming direct geophysical method. In the present paper, a machine learning model has been developed to obtain the subsurface shear wave velocity profile without using direct methods. The bore log information and the subsurface shear wave velocity profile available at various stations of Japan’s Kyoshin network (K-NET) have been utilized for training this machine learning model. The parametric correlation study indicates that simple parameters like rock/soil type, the thickness of the layer in the model, and standard penetration test (SPT-N) value directly correlate with the medium’s shear wave velocity. A stacked ensemble machine learning model named VelProfES (an acronym for Velocity Profiler using Ensemble machine learning models) has been developed in this paper and has been utilized for effective prediction of the shear wave velocity profile using basic information from borelog data. The dataset used in the training and testing of the machine learning model consists of borelog and shear wave velocity information from 1101 stations. Of 1101 stations, 657, 283, and 71 stations have been utilized for training, testing, and validating the machine learning model. Training, testing, and validation of the developed machine learning model consist of parameters from 12351, 5279, and 1388 velocity layers. The problem of data imbalance based on soil type has been addressed using an additional 10510 layers of synthetic borelog data generated from conditional generative adversarial networks (CTGANs). A feature and model ablation study was conducted for the VelProfES model to provide substantiation for the model and feature selection choices. The predicted shear wave velocity profiles were compared at specific stations, focusing on average velocities at 5, 10, 15, and 20 depths. Further, the predicted values have been compared with the empirical relation of Sil and Haloi (2017) and a trained polynomial model. The machine learning model demonstrates close alignment between predicted and actual values across a broad spectrum of velocities, a contrast not observed in the empirical relation and polynomial model. The results show that the machine learning models and augmented data generated using CTGANs can efficiently minimize the error between actual and predicted subsurface shear wave velocity values.

Automatic velocity picking based on optimal key points tracking algorithm

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method (CM), the proposed tracking method (TM), and the variational method (VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

Feature selection of ground motion intensity measures for data‐driven surrogate modeling of structures

AbstractIn the probabilistic seismic performance assessment of structures, intensity measures (IMs) represent seismic characteristics and variations. Traditional fragility analysis method based on the assumption of linear regression requires selecting an optimal IM as input variable. By introducing machine learning (ML) techniques, nonparametric fragility analysis theoretically allows for considering all potential IMs as inputs. Nevertheless, to reduce input dimensionality and improve training efficiency, the feature selection of IMs remains imperative. This paper proposes a method to select optimal ground motion IMs for data‐driven surrogate modeling of structures. Specifically, the elastic net algorithm is employed to select the optimal multiple IMs based on the coefficient of determination and regression coefficient, differing from the efficiency and practicality emphasized in the traditional method. Using the optimal multiple IMs as input variables, several ML techniques are employed to construct surrogate models for seismic damage assessment of structures, thereby developing fragility functions, that is, the conditional probability of exceeding a damaged state given seismic intensity. A 3‐span, 6‐storey, reinforced concrete frame is utilized to illustrate the proposed methodology. The predictive performance of all ML models with the optimal multiple IMs outperforms that of the models with the commonly used IM (e.g., peak ground acceleration, PGA) as sole input and all candidate IMs as inputs. Additionally, the surrogate models with the optimal multiple IMs enable a more comprehensive seismic fragility modeling of structures under two or more IMs simultaneously, such as the fragility surface under spectral acceleration at 1.0s (Sa‐1.0s) and velocity spectrum intensity (VSI).

Flow Field Analysis of a Turbulent Channel Controlled by Scalloped Riblets

Riblets are small protruding surfaces along the direction of the flow, and are one of the most well-known passive turbulent drag reduction methods. We investigated a scalloped riblet, the shape of which was constructed by smoothly connecting two third-order polynomials and was not as sharp in the tip as corresponding triangular riblets with the same height-width ratio. Numerical simulations were performed for turbulent channel flow with and without riblet control at an estimated optimum width of W+=20 and a height-width ratio of 0.5. A drag reduction rate of 5.77% was obtained, which is generally larger than the reported drag reduction rates of corresponding triangular riblets from the literature. Mean flow fields and second-order statistics of velocity, vorticity, and Liutex, a quantity introduced to represent vortices, were reported. It was found that streamwise vortices just above the riblet tips, which have a length scale of 200−300 in wall units, have an important influence on those statistics and thus the turbulence generation cycle and the drag reduction mechanism. Pre-multiplied energy spectra of streamwise velocity and the Liutex component were reported to reveal the length scales in the flow field. Instantaneous vortical flow fields visualized by the Liutex method were provided with emphases on the streamwise vortices just above riblet tips. It should be noted that the class of scalloped riblets is suitable for investigations on the influences of curvatures at the tip and the valley of the riblet in future.

Resonance, Velocity, Dispersion, and Attenuation of Ultrasound-Induced Shear Wave Propagation in Blood Clot In Vitro Models.

Improve the characterization of mechanical properties of blood clots. Parameters derived from shear wave (SW) velocity and SW amplitude spectra were determined for gel phantoms and in vitro blood clots. Homogeneous phantoms and phantoms with gel or blood clot inclusions of different diameters and mechanical properties were analyzed. SW amplitude spectra were used to observe resonant peaks. Parameters derived from those resonant peaks were related to mimicked blood clot properties. Three regions of interest were tested to analyze where resonances occurred the most. For blood experiments, 20 samples from different pigs were analyzed over time during a 110-minute coagulation period using the Young modulus, SW frequency dispersion, and SW attenuation. The mechanical resonance was manifested by an increase in the number of SW spectral peaks as the inclusion diameter was reduced (P < .001). In blood clot inclusions, the Young modulus increased over time during coagulation (P < .001). Descriptive spectral parameters (frequency peak, bandwidth, and distance between resonant peaks) were linearly correlated with clot elasticity values (P < .001) with R2 = .77 for the frequency peak, .60 for the bandwidth, and .48 for the distance between peaks. The SW dispersion and SW attenuation reflecting the viscous behavior of blood clots decreased over time (P < .001), mainly in the early stage of coagulation (first minutes). The confined soft inclusion configuration favored SW mechanical resonances potentially challenging the computation of spectral-based parameters, such as the SW attenuation. The impact of resonances can be reduced by properly selecting the region of interest for data analysis.

Separating 21-cm M31 signal from background

By investigating 21 cm radio wave emitted by Hydrogen gas in Milky Way galaxy we can determine the intensity map of M31(a.k.a.,"Andromeda"). The author applied the data sets collected by 18m radio telescope pointing at different locations of M31 and using FFT, double subtraction method and matplotlib to visualize the data in the term of power spectral density-Doppler velocity spectrum. Then the author compared the spectrum and used the structure of M31 to explain the difference between the on-source spectrum and background spectrum.

Ground-motion effects on liquefaction response

This article evaluates the influence of ground-motion characteristics on the liquefaction response of free-field level-ground deposits. Ground motions imposed on liquefiable soils have historically been characterized by the peak ground acceleration (PGA). In simplified procedures for liquefaction evaluation, the use of the ground surface PGA can be justified by the fact that it is closely related to the peak shear stresses developing in the critical zone for liquefaction manifestation, at shallow depths. In nonlinear dynamic analysis (NDA), however, such simplified characterization of the input motion using the bedrock-level PGA can result in large record-to-record variability in the computed response. Record-to-record variability can be substantially reduced by employing more appropriate ground-motion intensity measures (IMs) for specific aspects of the liquefaction response observed in NDA. Among 25 IMs examined in this study, the Modified Acceleration Spectrum Intensity (MASI), defined as the integral of the acceleration spectrum over periods 0.1–1.5 s, is found to be the most efficient IM for the prediction of the pore-pressure response of liquefiable deposits, whereas Velocity Spectrum Intensity (VSI) ranks among the best-performing IMs for response parameters related to ground deformations. Importantly, such “multiple-period spectrum IMs” are shown to perform consistently better than several highly-regarded time-integrated IMs (e.g. Arias intensity, CAV). Conclusions and recommendations are drawn on the basis of a comprehensive series of NDAs involving 180 input motions and 13 deposit models based on characteristic soil profiles from Christchurch.

Neural network identification of the weakly coherent mode in I-mode discharge on EAST

The improved energy confinement mode (I-mode) is widely considered as an important operation regime for ITER. I-mode implementation depends on the specified basic plasma parameters and certain operation conditions, which are discovered by statistical plasma characteristics from a large number of I-mode discharges on a tokamak. The extraction process of I-mode plasma characteristics is complicated, time-consuming, and limited to the sampling rate of the measured signals. Experimental observation of the I-mode is accompanied by the appearance of a weakly coherent mode (WCM). However, it takes much time to accurately scan and quantify WCM characteristics when analyzing many I-mode discharges. Recently, a neural network identification method was developed as an I-mode detector to traverse a whole database as a replacement for manual identification. Two fully connected neural network models were trained with the spectrum of propagation velocity of density perturbation from Doppler backward scattering and the electron density measured by a polarimeter-interferometer system with the experimental advanced superconducting tokamak I-mode database. An accuracy of 98.30% in identifying WCMs in I-mode discharges is achieved with the WCM classification model. In addition, the regime classification model was also utilized to successfully distinguish between the low confinement mode (L-mode), I-mode, and high confinement mode (H-mode) with 96.03% accuracy. Finally, ablation experiments were performed on the regime classifiers, showing that there is potential for further performance improvement with future use of RNN model.

Automatic Extraction of Surface Wave Dispersion Curves Using Unsupervised Learning and High‐Resolution Tau‐p Transform

AbstractDispersion curves for surface wave recordings are required input for many surface wave inversion methods to image subsurface shear wave velocity distribution, while the conventional extraction of dispersion curves requires significant amount of human interaction. This step impedes efficiency enhancement of surface wave analysis and its automation. In this paper, we present an unsupervised learning scheme to achieve efficient automatic picking of dispersion curves of the fundamental mode for surface wave gathers. This scheme is composed of four major steps: computing a frequency velocity(f‐v) spectrum for the surface wave gather using a high‐resolution Tau‐p transform improved by the iteratively shrinkage Thresholding algorithm (ISTA) algorithm, generating clusters points along the dispersion energy in the f‐v spectrum via a weighted Kmeans algorithm, filtering these cluster points by principal component analysis (PCA) and Local Outlier Factor (LOF) algorithms to remove the erroneous clusters, and fitting the remaining clusters to form the dispersion curve. Tests with synthetic and field noisy surface wave recordings demonstrated the effectiveness of this approach and its potential in automatic processing of noisy surface wave data sets.

A Transverse Velocity Spectral Estimation Method for Ultrafast Ultrasound Doppler Imaging.

A novel transverse velocity spectral estimation method is proposed to estimate the velocity component in the direction transverse to the beam axis for ultrafast imaging. The transverse oscillation was introduced by filtering the envelope data after the axial oscillation was removed. The complex transverse oscillated signal was then used to estimate the transverse velocity spectrum and mean velocity. In simulations, both steady flow with a parabolic flow profile and temporally varying flow were simulated to investigate the performance of the proposed method. Next, the proposed approach was used to estimate the flow velocity in a phantom with pulsatile flow, and finally, this method was applied in vivo in a small animal model. Results of the simulation study indicate that the proposed method provided an accurate velocity spectrogram for beam-to-flow angles from 45° to 90°, without significant performance degradation as the angle decreased. For the simulation of temporally varying flow, the proposed method had a reduced bias ( % versus 73.3%) and higher peak-to-background ratio (PBR) (>15.6 versus 10.5 dB) compared to previous methods. Results in a vessel phantom show that the temporally varying flow velocity can be estimated in the transverse direction obtained using the spectrogram produced by the proposed method operating on the envelope data. Finally, the proposed method was used to map the microvascular blood flow velocity in the mouse spinal cord, demonstrating the estimation of pulsatile blood flow in both the axial and transverse directions in vivo over several cardiac cycles.

Research on The Source Parameters and Scaling Relations of Earthquakes in Weihe Basin and Its Adjacent Area

By using the data recorded in Shaanxi seismic network, seismic source parameters were determined through spectrum analyzing basing on Brune’s source model for 125 small to moderate earthquakes (1.1 ≤ ML ≤ 4.8) from 1997 to 2016 in Weihe basin and its adjacent area. Seismic radiated energies were also obtained through integral of observed velocity spectra. The relations between stress drop, scaled energy E s / M 0 and Zúñiga parameter ε with seismic moment are analyzed. The results show that the earthquakes are with low stress drop in this area, the mean value is 0.16 MPa. More than 90% of the events in the region observed the ε value is less than 1. The low stress drop and low ε value indicate that earthquakes in this area can be explained by fractional stress drop mechanism or low effective stress model. These may be the result of mature fault activity, however, it cannot be ruled out effect of underground fluid. The scaled energy of earthquakes (1.3 ≤ MW ≤ 3.6) in this area is less than that of larger earthquakes (3.6 ≤ MW ≤ 6.0) in other areas. The earthquakes in this area are fairly small, so the heat energy generated in the rupture process is small, which is not enough to produce melting along the fault plane. With large dynamic friction force, it is not easy to produce rapid brittle rupture, resulting in low scalsed energy.

Shallow relic gravitational wave spectrum with acoustic peak

We study the gravitational wave (GW) spectrum produced by acoustic waves in the early universe, such as would be produced by a first order phase transition, focusing on the low-frequency side of the peak. We confirm with numerical simulations the Sound Shell model prediction of a steep rise with wave number k of k 9 to a peak whose magnitude grows at a rate (H/k p)H, where H is the Hubble rate and k p the peak wave number, set by the peak wave number of the fluid velocity power spectrum. We also show that hitherto neglected terms give a shallower part with amplitude (H/k p)2 in the range H ≲ k ≲ k p, which in the limit of small H/k rises as k. This linear rise has been seen in other modelling and also in direct numerical simulations. The relative amplitude between the linearly rising part and the peak therefore depends on the peak wave number of the velocity spectrum and the lifetime of the source, which in an expanding background is bounded above by the Hubble time H -1. For slow phase transitions, which have the lowest peak wave number and the loudest signals, the acoustic GW peak appears as a localized enhancement of the spectrum, with a rise to the peak less steep than k 9. The shape of the peak, absent in vortical turbulence, may help to lift degeneracies in phase transition parameter estimation at future GW observatories.

High‐frequency wide‐angle reflection and refraction method for structural engineering ground‐penetrating radar surveys

AbstractElectromagnetic wave velocity in ground‐penetrating radar (GPR) constant offset data can be estimated via the diffraction hyperbola fitting method. This method is applicable when radargrams contain diffraction events (hyperbolic patterns) caused by scatters in the host smaller or equal to the dominant wavelength. An alternative method for velocity estimation, if no intrusive information is available for a direct correlation, requires the collection of multi‐offset data. The method is quite common for broad geophysical applications; however, it seems not to be fully utilized for engineering applications, such as slabs/walls where thickness estimation and depth of the embedded features are critical requirements for structural assessments. This method would also overcome the limitations in velocity calibration in environments with no hyperbolic signal signatures. The aim of this study is to explore multi‐offset high‐frequency GPR applications, specifically the wide‐angle reflection and refraction method, for structural engineering, to understand whether it is feasible, possible limitations, and advantages. Numerical models reproducing reinforced concrete elements and a cavity wall were analysed to understand the wave behaviour and predict the response prior to testing on real cases. The main purpose is to explore how reinforcing bars can affect the velocity spectra derived from semblance analysis and what the response would be in a case of multi‐layered structure with increasing velocity with depth (cavity wall). The comparison with real cases showed that, despite some intrinsic limitations, high‐frequency multi‐offset approach could be part of standard workflow for all those surveys where no other velocity estimation method is feasible.

GPR velocity correction method in transversely heterogeneous media based on CMP data

AbstractDetermining ground‐penetrating radar (GPR) velocity has always been a critical problem. The GPR velocity estimation method based on common midpoint (CMP) data has been widely used because of its simplicity. However, we found that in sediment investigation and soil assessment, transversal heterogeneity is universal, which violates the basic assumption of velocity estimation through CMP data. Due to the rapid change of underground media and the limitation of the scope of some surveying areas, the CMP survey line will inevitably be selected in the area where the velocity changes laterally, making it difficult to obtain accurate velocity. To address this problem, we propose a velocity correction method. First, we determined the characteristics of CMP data and the corresponding velocity spectra acquired in transversely heterogeneous media through numerical simulations. Subsequently, we found that the simulated CMP data could determine the location of changes in the underground medium, and that the velocity obtained from the semblance analysis could be corrected according to the location where the medium changes laterally. We then used models wherein the thickness, relative permittivity and proportion of abnormal parts varied independently or simultaneously to verify the proposed velocity correction method. The results show that our method can control the GPR velocity error within 3.44%, and the precision is about 0.002 m/ns. Finally, we conducted a sediment investigation experiment on a channel bar in the lower reaches of the Yarlung Zangbo River. We determined the interface at which the sediment changed transversely and obtained the corresponding electromagnetic velocity using the proposed method. This study provides a reliable method for determining the GPR velocity in transversal heterogeneous media, which is of great significance for various practical applications.

Parallel ribbon vortex: A phenomenological flow feature in an atmospheric boundary layer near sloped terrain

In this study, flow behaviors and vortex morphology around the sloping terrain that is covered by an atmospheric boundary layer are investigated using a combination of numerical and experimental methods. An array of wind tunnel experiments and high-fidelity large eddies simulations have been conducted, considering different slope angles θ = 90°, 45°, 26.5°, and 18.2°. Systematic analysis of turbulence phenomena pertaining to the flow field's topography-driven distortions and accelerations was performed quantitatively via time-averaged velocity field, terrain-induced turbulence intensity, velocity spectrum, and acceleration ratio. Two recirculation zones, one originating from the upstream boundary layer's gradual separation bubble (GSB) and the other from the forced separation bubble (FSB) behind the downstream sharp leading-edge, were confirmed by detailed visualization of the flow field. An acceleration zone of considerable magnitude is also observed, connecting the GSB and FSB. Subsequently, a new vortex structure—the parallel ribbon vortex (PRV)—was disclosed. The PRVs originate from the disordered turbulence structures inside the GSB, spiraling into coherent structures. The accelerated mainstream up the step then stretches and elongates the coherence structures into ribbon-like vortices parallel to the mainstream, completing the formation process. Finally, the PRVs transport fluid up the step before encountering the strong spanwise vortices and dissipating thereafter. The investigation into the slope flow characteristics and turbulence structure conducted in this study has the potential to enhance the prediction of wind characteristics and wind load specifications in mountainous regions.

Frequency spectra of horizontal current velocities on the northeastern shelf of the Black Sea

The paper presents a technique for restoring gaps in the original data, which made it possible to study the variability of fluctuations in the currents speed over significant time intervals. Analysis of the reconstructed continuous series of observations showed that high-frequency fluctuations in current speed predominate in the coastal zone, the possible source of which is the submesoscale eddies present here most of the time. On the continental slope, i.e. in the deep-water part of the sea, larger-scale processes dominate, associated with the dynamics of mesoscale eddies, meandering of the main Black Sea current, as well as global (on the scale of the entire sea) wind variability.

Seismic fragility functions for earthquake-induced landslide risk assessment using identified optimal earthquake intensity measures

Seismic fragility analysis can relate the probability of unsatisfactory performance of slopes with increasing intensity of ground motions, which is the basic component of landslide hazard risk assessment. Determination of the optimal earthquake intensity measure (IM) is one of important prerequisite for seismic fragility analysis. Although a small number of studies have already performed seismic fragility analysis of slopes, the optimal IM for seismic fragility analysis of slopes have not been discussed and researched. In the present study, 15 IMs and two different engineering demand parameters (EDPs), including factor of safety (FOS) and Newmark permanent displacement (NPD), are selected for probabilistic seismic demand and seismic fragility analysis of slopes. The metrics of correlation, practicality, efficiency, and proficiency are adopted to identify the optimal earthquake IMs among the studied 15 IMs for two EDPs in seismic fragility analysis of slopes respectively. The results have found that the optimal IM is different for slope fragility analysis when different EDPs are selected. Acceleration spectrum intensity is the most optimal IM among the examined 15 IMs for seismic fragility analysis of slopes when FOS is adopted as the EDP. When NPD is selected as the EDP, root mean square of acceleration and velocity spectrum intensity are in general more appropriate than other IMs. In the last, the seismic fragility curves with regard to two EDPs and corresponding optimal IMs are derived for interpreting more properly slope seismic performance and earthquake-induced landslide risk.

A multi-information combined convolutional neural network velocity spectrum automatic picking method

Abstract Seismic velocity is a critical parameter in seismic exploration, and its accuracy significantly affects the reliability of data processing and interpretation results. However, manual velocity picking methods are not only inefficient but also time consuming, making them increasingly inadequate for meeting the demands of practical production work. This paper introduces the multi-information combination convolutional neural network (MCCN) velocity auto-picking method. Building on the foundation of convolutional neural networks, we have designed the network structure of the MCCN method specifically tailored to the characteristics of stacked velocity picking tasks. Given that velocity spectrum energy clusters exhibit both morphological and trend features, we use a regression convolutional neural network to enhance the accuracy of velocity picking. Furthermore, as the velocity spectrum contains interference from multiple waves and other noise, we employ a coordinate attention mechanism to mitigate the influence of interfering information. Our approach involves the simultaneous incorporation of velocity spectrum and CMP information through a dual-combination network, thereby further enhancing velocity picking accuracy. Finally, we compare our method with fully connected convolutional neural networks and manual velocity picking methods, demonstrating the practicality and precision of our proposed approach.

Assessment of uncertainties in damping reduction factors using ANN for acceleration, velocity and displacement spectra

Structure's damping force during an earthquake is very different from what was anticipated during design. This adds uncertainty to the process of designing structures exposed to seismic loads which may be a major cause of significant variation in the seismic response reliability of these structures. This work is focused on the investigation of the structural damping uncertainties effect on the structure’s response spectra through the assessment of uncertainties in the damping reduction factors (DRF) derived from the acceleration, velocity and displacement spectra. An Artificial Neural Networks (ANN) was also developed for the stochastic DRF calculation. The Monte Carlo method, one of the methods of computational algorithms that rely on repeated random sampling to obtain numerical results, is used for the estimation of the stochastic DRF. The obtained results indicates that the difference between the deterministic and the stochastic DRF are around of 21 % for displacement and velocity and 28.7 % for acceleration spectra. As a consequence, the DRF derived from the acceleration spectra is more sensible to the uncertainties inherent on damping than the DRF obtained from displacement and velocity. Therefore, it is important to take this conclusion into account when using these factors previously. The ANN constitutes a sample and efficiency method to predict the stochastic DRF since the error obtained is always less than 6 %. Practice oriented results are searched for, to be incorporated in future seismic standards.