Matthias Ohrnberger

Surface-wave inversion using a direct search algorithm and its application to ambient vibration measurements

Passive recordings of seismic noise are increasingly used in earthquake engineering to measure in situ the shear-wave velocity profile at a given site. Ambient vibrations, which are assumed to be mainly composed of surface waves, can be used to determine the Rayleigh-wave dispersion curve, with the advantage of not requiring artificial sources. Due to the data uncertainties and the non-linearity of the problem itself, the solution of the dispersion-curve inversion is generally non-unique. Stochastic search methods such as the neighbourhood algorithm allow searches for minima of them is fit function by investigating the whole parameter space. Due to the limited number of parameters in surface-wave inversion, they constitute an attractive alternative to linearized methods. An efficient tool using the neighbourhood algorithm was developed to invert the one-dimensional V s profile from passive or active source experiments. As the number of generated models is usually high in stochastic techniques, special attention was paid to the optimization of the forward computations. Also, the possibility of inserting a priori information into the parametrization was introduced in the code. This new numerical tool was successfully tested on synthetic data, with and without a priori information. We also present an application to real-array data measured at a site in Brussels (Belgium), the geology of which consists of about 115 m of sand and clay layers overlying a Palaeozoic basement. On this site, active and passive source data proved to be complementary and the method allowed the retrieval of a V s profile consistent with borehole data available at the same location.

Scaling relations of earthquake source parameter estimates with special focus on subduction environment

Abstract Earthquake rupture length and width estimates are in demand in many seismological applications. Earthquake magnitude estimates are often available, whereas the geometrical extensions of the rupture fault mostly are lacking. Therefore, scaling relations are needed to derive length and width from magnitude. Most frequently used are the relationships of Wells and Coppersmith (1994) derived on the basis of a large dataset including all slip types with the exception of thrust faulting events in subduction environments. However, there are many applications dealing with earthquakes in subduction zones because of their high seismic and tsunamigenic potential. There are no well-established scaling relations for moment magnitude and length/width for subduction events. Within this study, we compiled a large database of source parameter estimates of 283 earthquakes. All focal mechanisms are represented, but special focus is set on (large) subduction zone events, in particular. Scaling relations were fitted with linear least-square as well as orthogonal regression and analyzed regarding the difference between continental and subduction zone/oceanic relationships. Additionally, the effect of technical progress in earthquake parameter estimation on scaling relations was tested as well as the influence of different fault mechanisms. For a given moment magnitude we found shorter but wider rupture areas of thrust events compared to Wells and Coppersmith (1994). The thrust event relationships for pure continental and pure subduction zone rupture areas were found to be almost identical. The scaling relations differ significantly for slip types. The exclusion of events prior to 1964 when the worldwide standard seismic network was established resulted in a remarkable effect on strike-slip scaling relations: the data do not show any saturation of rupture width of strike-slip earthquakes. Generally, rupture area seems to scale with mean slip independent of magnitude. The aspect ratio L / W , however, depends on moment and differs for each slip type.

Tracking the rupture of the Mw = 9.3 Sumatra earthquake over 1,150 km at teleseismic distance.

On 26 December 2004, a moment magnitude Mw = 9.3 earthquake occurred along Northern Sumatra, the Nicobar and Andaman islands, resulting in a devastating tsunami in the Indian Ocean region. The rapid and accurate estimation of the rupture length and direction of such tsunami-generating earthquakes is crucial for constraining both tsunami wave-height models as well as the seismic moment of the events. Compressional seismic waves generated at the hypocentre of the Sumatra earthquake arrived after about 12 min at the broadband seismic stations of the German Regional Seismic Network (GRSN), located approximately 9,000 km from the event. Here we present a modification of a standard array-seismological approach and show that it is possible to track the propagating rupture front of the Sumatra earthquake over a total rupture length of 1,150 km. We estimate the average rupture speed to be 2.3–2.7 km s-1 and the total duration of rupture to be at least 430 s, and probably between 480 and 500 s.

Deriving Wavefield Characteristics and Shear-Velocity Profiles from Two- Dimensional Small-Aperture Arrays Analysis of Ambient Vibrations in a Small-Size Alluvial Basin, Colfiorito, Italy

We analyze the dispersion characteristics of ambient noise vibrations. For this purpose, two-dimensional (2d) seismic array data were acquired in four different sites in the Colfiorito plain, an alluvial intramountain basin that exhibits strong site effects. Assuming seismic noise being mainly composed of surface waves, we derive one-dimensional (1d) shallow shear-velocity profiles through the inversion of dispersion curves measured by frequency–wavenumber ( f-k ) methods. The inverted shear-wave velocity profiles are consistent with a priori information for those sites that can be approximated by 1d simple models. In these cases, the use of passive records of seismic vibrations can be a valuable tool for determining the shallow velocity profile if a detailed depiction of velocity structure is not required. The theoretical dispersion curves for Rayleigh and Love waves were compared with the measured dispersion curves for vertical and horizontal components, respectively. This allows us to discuss qualitatively the composition of ambient vibrations (outlining a large proportion of Love waves in the noise wave field) and the effects of higher modes. We also use the single-station method for investigating the origin of the horizontal-to-vertical (h/v) peak in the plain of Colfiorito in terms of ellipticity of the fundamental Rayleigh mode.

Direct Inversion of Spatial Autocorrelation Curves with the Neighborhood Algorithm

Ambient vibration techniques are promising methods for assessing the subsurface structure, in particular the shear-wave velocity profile ( Vs ). They are based on the dispersion property of surface waves in layered media. Therefore, the penetration depth is intrinsically linked to the energy content of the sources. For ambient vibrations, the spectral content extends in general to lower frequency when compared to classical artificial sources. Among available methods for processing recorded signals, we focus here on the spatial autocorrelation method. For stationary wavefields, the spatial autocorrelation is mathematically related to the frequency-dependent wave velocity c ( ω ). This allows the determination of the dispersion curve of traveling surface waves, which, in turn, is linked to the Vs profile. Here, we propose a direct inversion scheme for the observed autocorrelation curves to retrieve, in a single step, the Vs profile. The powerful neighborhood algorithm is used to efficiently search for all solutions in an n -dimensional parameter space. This approach has the advantage of taking into account the existing uncertainty over the measured curves, thus generating all Vs profiles that fit the data within their experimental errors. A preprocessing tool is also developed to estimate the validity of the autocorrelation curves and to reject parts of them if necessary before starting the inversion itself. We present two synthetic cases to test the potential of the method: one with ideal autocorrelation curves and another with autocorrelation curves computed from simulated ambient vibrations. The latter case is more realistic and makes it possible to figure out the problems that may be encountered in real experiments. The Vs profiles are correctly retrieved up to the depth of the first major velocity contrast unless low-velocity zones are accepted. We demonstrate that accepting low-velocity zones in the parameterization has a dramatic influence on the result of the inversion, with a considerable increase in the nonuniqueness of the problem. Finally, a real data set is processed with the same method.

Automatic hypocenter determination of volcano induced seismic transients based on wavefield coherence — an application to the 1998 eruption of Mt. Merapi, Indonesia

In order to monitor the seismic activity of Mt. Merapi (Indonesia) over a long period of time, we installed a permanent array of both broadband and short-period seismometers during the summer of 1997. Considering the requirements of an automatic classification and localization system for seismic monitoring and surveillance at active volcanoes, we split this network into three small-aperture arrays distributed around the volcano. We introduce here a newly developed method to determine the hypocenters in an automatic, non-linear manner using the coherence of seismic waves observed at the different arrays. To test this method, we analyze a swarm of VT-B events recorded by the network. The first step in this algorithm is based on a modified smoothed coherence transform. In the second step, we perform a semblance analysis applied to the 3D problem, evaluating the quality of the estimated relative onset-times. After more than one year of dormancy, Mt. Merapi renewed its activity at the end of June 1998. This gave us the opportunity to analyze all stages of dome growth, collapse and new intrusion of magma using the associated seismicity in a post-processing sense. This also allowed us to calibrate and test our newly developed automatic monitoring system using the more pronounced waveforms of VT-B events. By detecting and classifying different event types automatically, we are able to localize a large number of VT-B events, which occurred just before the initial eruption. We are also able to resolve some properties of the wavefield at Mt. Merapi, which are essential for further interpretations. Finally, the results show that the source region of the VT-B type seismicity just before the 1998 eruption is closely related to the region of subsequent high volcanic activity and therefore may represent a promising tool to forecast future eruptions.

A Seismic‐Event Spotting System for Volcano Fast‐Response Systems

Volcanic eruptions are often preceded by seismic activity that can be used to quantify the volcanic activity. In order to allow consistent inference of the volcanic activity state from the observed seismicity patterns, objective and time‐invariant classification results achievable by automatic systems should be preferred. Most automatic classification approaches need a large preclassified data set for training the system. However, in case of a volcanic crisis, we are often confronted with a lack of training data due to insufficient prior observations. In the worst case (e.g., volcanic crisis related reconfiguration of stations), there are even no prior observations available. Finally, due to the imminent crisis there might be no time for the time‐consuming process of preparing a training data set. For this reason, we have developed a novel seismic‐event spotting technique in order to be less dependent on previously acquired data bases and classification schemes. We are using a learning‐while‐recording approach based on a minimum number of reference waveforms, thus allowing for the build‐up of a classification scheme as early as interesting events have been identified. First, short‐term wave‐field parameters (here, polarization and spectral attributes) are extracted from a continuous seismic data stream. The sequence of multidimensional feature vectors is then used to identify a fixed number of clusters in the feature space. Based on this general description of the overall wave field by a mixture of multivariate Gaussians, we are able to learn particular event classifiers (here, hidden Markov models) from a single waveform example. To show the capabilities of this new approach we apply the algorithm to a data set recorded at Soufriere Hills volcano, Montserrat. Supported by very high classification rates, we conclude that the suggested approach provides a valuable tool for volcano monitoring systems.

From Non-invasive Site Characterization to Site Amplification: Recent Advances in the Use of Ambient Vibration Measurements.

A series of investigations has been carried out over the last decade in Europe aimed at deriving quantitative information on site amplification from non-invasive techniques, based principally on surface wave interpretations of ambient noise measurements. The present paper focuses on their key outcomes regarding three main topics. First, methodological, hardware and software developments focusing on the acquisition and the processing of both single point and array microtremor measurements, led to an efficient tool with in situ control and processing, giving rise to robust and reproducible results. A special attention has been devoted to the derivation and use of the Rayleigh wave ellipticity. Second, the reliability of these new tools has been assessed through a thorough comparison with borehole measurements for a representative – though limited – set of sites located in Southern Europe, spanning from stiff to soft, and shallow to thick. Finally, correlations between the site parameters available from such non-invasive techniques, and the actual site amplification factors as measured with standard techniques, are derived from a comprehensive analysis of the Japanese KIKNET data. This allows to propose alternative, simple site characterization providing an improved variance reduction compared with the “classical” VS30 classification. While these results could pave the road for the next generation of building codes, they can also be used now for regulatory site classification and microzonation studies, in view of improved mapping and estimation of site amplification factors, and for the characterization of existing strong motion sites.

Recommendation for the discrimination of human-related and natural seismicity

Various techniques are utilized by the seismological community, extractive industries, energy and geoengineering companies to identify earthquake nucleation processes in close proximity to engineering operation points. These operations may comprise fluid extraction or injections, artificial water reservoir impoundments, open pit and deep mining, deep geothermal power generations or carbon sequestration. In this letter to the editor, we outline several lines of investigation that we suggest to follow to address the discrimination problem between natural seismicity and seismic events induced or triggered by geoengineering activities. These suggestions have been developed by a group of experts during several meetings and workshops, and we feel that their publication as a summary report is helpful for the geoscientific community. Specific investigation procedures and discrimination approaches, on which our recommendations are based, are also published in this Special Issue (SI) of Journal of Seismology.

Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields

This study presents an unsupervised feature selection and learning approach for the discovery and intuitive imaging of significant temporal patterns in seismic single-station or network recordings. For this purpose, the data are parametrized by real-valued feature vectors for short time windows using standard analysis tools for seismic data, such as frequency-wavenumber, polarization, and spectral analysis. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure, which is in particular suitable for high-dimensional data sets. Our feature selection method is based on significance testing using the Wald-Wolfowitz runs test for individual features and on correlation hunting with SOMs in feature subsets. Using synthetics composed of Rayleigh and Love waves and real-world data, we show the robustness and the improved discriminative power of that approach compared to feature subsets manually selected from individual wavefield parametrization methods. Furthermore, the capability of the clustering and visualization techniques to investigate the discrimination of wave phases is shown by means of synthetic waveforms and regional earthquake recordings.