Earthquake Signals Research Articles

Christchurch Women's Hospital: Analysis of Measured Earthquake Data during the 2011–2012 Christchurch Earthquakes

A network of acceleration and displacement sensors installed in the Christchurch Women's Hospital (CWH) in July 2011 captured an extensive range of earthquake signals, allowing for a unique opportunity to analyze the performance of the New Zealand South Island's only base-isolated structure. Key characteristics of a range of earthquake signals, including frequency spectra and response patterns, are identified, with particular focus on the swarm of earthquakes on 23 December 2011, including four earthquake events greater than magnitude 5.0 on the Richter scale. The findings indicate that the response of the isolators and the superstructure was essentially elastic for the events analyzed during this period. Accelerations measured above and below the isolators were similar, indicating that the behavior of the devices resembled that of rigid blocks. No significant rocking or torsional motion of the building was observed.

Vector regression introduced

AbstractThis study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable) is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables) and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables) also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

Krauklis wave initiation in fluid-filled fractures by seismic body waves

Krauklis waves are a special wave mode that is bound to and propagates along fluid-filled fractures. They can repeatedly propagate back and forth along a fracture and eventually fall into resonance emitting a seismic signal with a dominant characteristic frequency. They are of great interest because this resonant behavior can lead to strongly frequency-dependent propagation effects for seismic body waves and may explain seismic tremor generation in volcanic areas or affect microseismic signals in fractured fluid reservoirs. It has been demonstrated that Krauklis waves can be initiated by a seismic source inside the fracture, for example by hydrofracturing. Here, the aim is to study Krauklis wave initiation by an incident plane P- or S-wave in numerical simulations. Both seismic body waves are reflected and scattered at the fracture, but also, two Krauklis waves are initiated with significant amplitude, one at each fracture tip (i.e., at the diffraction-points of the fracture). Generally, the incident S-wave initiates larger-amplitude Krauklis waves compared to the incident P-wave case. For both incident wave modes, the initiation of Krauklis waves strongly depends on the fracture orientation. In the case of an incident P-wave, large-amplitude Krauklis waves are initiated at moderate (12°–40°) and high ([Formula: see text]) inclination angles of the fracture with a distinct gap at approximately 50°. The dependency of Krauklis wave initiation on fracture orientation is almost inversed in the case of an incident S-wave and the largest-amplitude Krauklis waves are initiated at an S-wave incidence angle of approximately 50°. The initiation of large-amplitude Krauklis waves by both P- and S-waves has important implications for earthquake signals propagating through fluid-bearing fractured rocks (volcanic areas, fluid-reservoirs) or for seismic exploration surveys in fractured reservoir situations.

Using an inerter‐based device for structural vibration suppression

SUMMARYThis paper proposes the use of a novel type of passive vibration control system to reduce vibrations in civil engineering structures subject to base excitation. The new system is based on the inerter, a device that was initially developed for high‐performance suspensions in Formula 1 racing cars. The principal advantage of the inerter is that a high level of vibration isolation can be achieved with low amounts of added mass. This feature makes it an attractive potential alternative to traditional tuned mass dampers (TMDs). In this paper, the inerter system is modelled inside a multi‐storey building and is located on braces between adjacent storeys. Numerical results show that an excellent level of vibration reduction is achieved, potentially offering improvement over TMDs. The inerter‐based system is compared to a TMD system by using a range of base excitation inputs, including an earthquake signal, to demonstrate how the performance could potentially be improved by using an inerter instead of a TMD. Copyright © 2013 John Wiley & Sons, Ltd.

Observing coseismic gravity change from the Japan Tohoku‐Oki 2011 earthquake with GOCE gravity gradiometry

AbstractThe Japan Tohoku‐Oki earthquake (9.0 Mw) of 11 March 2011 has left signatures in the Earth's gravity field that are detectable by data of the Gravity field Recovery and Climate Experiment (GRACE) mission. Because the European Space Agency's (ESA) satellite gravity mission Gravity field and steady‐state Ocean Circulation Explorer (GOCE)—launched in 2009—aims at high spatial resolution, its measurements could complement the GRACE information on coseismic gravity changes, although time‐variable gravity was not foreseen as goal of the GOCE mission. We modeled the coseismic earthquake geoid signal and converted this signal to vertical gravity gradients at GOCE satellite altitude. We combined the single gradient observations in a novel way reducing the noise level, required to detect the coseismic gravity change, subtracted a global gravity model, and applied tailored outlier detection to the resulting gradient residuals. Furthermore, the measured gradients were along‐track filtered using different gradient bandwidths where in the space domain Gaussian smoothing has been applied. One‐year periods before and after earthquake occurrence have been compared with the modeled gradients. The comparison reveals that the earthquake signal is well above the accuracy of the vertical gravity gradients at orbital height. Moreover, the obtained signal from GOCE shows a 1.3 times higher amplitude compared with the modeled signal. Besides the statistical significance of the obtained signal, it has a high spatial correlation of ~0.7 with the forward modeled signal. We conclude therefore that the coseismic gravity change of the Japan Tohoku‐Oki earthquake left a statistically significant signal in the GOCE measured gravity gradients.

Long range correlation in earthquake precursory signals

Research on earthquake prediction has drawn serious attention of the geophysicist, geologist and investigators in different fields of science across the globe for many decades. Researchers around the world are actively working on recording pre-earthquake changes in non-seismic parameters through a variety of methods that include anomalous changes in geochemical parameters of the Earth’s crust, geophysical properties of the lithosphere as well as ionosphere etc. Several works also have been done in India to detect earthquake precursor signals using geochemical and geophysical methods. However, very few works have been done so far in India in this field through the application of nonlinear techniques to the recorded geophysical and geochemical precursory signals for earthquakes. The present paper deals with a short review of the early works on geochemical precursors that have been carried out in India as yet. With a view to detect earthquake precursory signals by means of gas-geochemical method we developed a network of seismo-geochemical monitoring observatories in India in hot springs and mud volcano crater. In the last few years we detected several geochemical anomalies and those were observed prior to some major earthquakes that occurred within a radius of 1500 km from the test sites. In the present paper we have applied nonlinear techniques to the long term, real-time and natural data sets of radon-222 and associated gamma originated out of the terrestrial degassing process of the earth. The results reveal a clear signature of the long range correlation present in the geochemical time series. This approach appears to be a potential tool to explore intrinsic information hidden within the earthquake precursory signals.

Investigation of the characteristics of geoelectric field signals prior to earthquakes using adaptive STFT techniques

Abstract. An earthquake is one of the most destructive natural disasters that can occur, often killing many people and causing large material losses. Hence, the ability to predict earthquakes may reduce the catastrophic effects caused by this phenomenon. The geoelectric field is a feature that can be used to predict earthquakes (EQs) because of significant changes in the amplitude of the signal prior to an earthquake. This paper presents a detailed analysis of geoelectric field signals of earthquakes which occurred in 2008 in Greece. In 2008, 12 earthquakes occurred in Greece. Five of them were recorded with magnitudes greater than Ms = 5R (5R), while seven of them were recorded with magnitudes greater than Ms = 6R (6R). In the analysis, the 1st significant changes of the geoelectric field signal are detected. Then, the signal is segmented and windowed. The adaptive short-time Fourier transform (adaptive STFT) technique is then applied to the windowed signal, and the spectral analysis is performed thereafter. The results show that the 1st significant changes of the geoelectric field prior to an earthquake have a significant amplitude frequency spectrum compared to other conditions, i.e. normal days and the day of the earthquake, which can be used as input parameters for earthquake prediction.

Assessing the potential improvement in short‐term earthquake forecasts from incorporation of GPS data

AbstractWe validate that changes of ground deformation recorded by GPS contain useful information for earthquake forecasting. A moving rate of variation filter is used to extract short‐term signals from GPS time series in New Zealand, California, and Japan. The precursory information of these signals for large earthquakes is evaluated using Molchan's error diagram. The results suggest that the GPS signals provide a probability gain of 2–4 for forecasting large earthquakes against a Poisson model. Further tests show that the GPS signals are not triggered by large earthquakes, and that the probability gain is not derived from forecasting aftershocks. This demonstrates that noncatalog information, such as GPS data, can be used to augment probabilistic models based on seismic catalog data to improve forecasting of large earthquakes.

Automatic Earthquake Signal Onset Picking Based on the Continuous Wavelet Transform

This paper presents a method for automatic earthquake signal phase picking based on the continuous wavelet transform (CWT) of the seismogram, namely, the wavelet phase picker (WPP). A characteristic function is defined using the envelope function of CWT coefficients of the vertical component seismogram. The performance of the WPP method is evaluated on a database consisting of more than 460 local seismograms, and the P-phases detected by the algorithm are compared with those reported in the database. This evaluation confirms the high accuracy of automatically detected picks for the majority of seismograms. In addition, the results obtained by the algorithm are compared with a well-known method of P-phase picking referred to as the autoregressive Akaike information criteria. This comparison demonstrates the reliability of the proposed method.

Correction to "Automatic earthquake signal onset picking based on the continuous wavelet transform" [May 13 2666-2674

In the above paper (ibid., vol. 51, no. 5, pp. 2666-2674, May 2013), there is an error in equation (7) due to a typesetting error. The correct form of the equation is presented here.

Nature of “transparency windows” in the broadband spectrum of seafloor microseisms

The peculiarities of the microseisms spectra shape when recording on the ocean floor in the frequency band of 0.003–20 Hz are examined. The origin of the stable minima in the microseisms spectrum (“transparency windows”) at frequencies of about 0.02–0.1 Hz and 5–15 Hz is analyzed. In these frequency bands, weak earthquake signals are recorded by bottom instruments. The origin of the low-frequency “transparency windows” can be explained by the conditions of the microseisms propagation in the oceanic waveguide (between the bottom and the water’s surface) in the abyssal plain zones. The results of the full-waveform numerical simulation of the seismoacoustic waves propagation in the oceanic environment and on the ocean-continent border are presented, and the experimental data as well. The peculiarities of the microseisms spectra in the band of high-frequency “transparency windows” can be caused by the constructive resonance in the water-saturated layer of bottom sediments. The theoretical foundation and experimental results are given.

Extracting seismic core phases with array interferometry

AbstractSeismic body waves that sample Earth's core are indispensable for studying the most remote regions of the planet. Traditional core phase studies rely on well‐defined earthquake signals, which are spatially and temporally limited. We show that, by stacking ambient‐noise cross‐correlations between USArray seismometers, body wave phases reflected off the outer core (ScS), and twice refracted through the inner core (PKIKP2) can be clearly extracted. Temporal correlation between the amplitude of these core phases and global seismicity suggests that the signals originate from distant earthquakes and emerge due to array interferometry. Similar results from a seismic array in New Zealand demonstrate that our approach is applicable in other regions and with fewer station pairs. Extraction of core phases by interferometry can significantly improve the spatial sampling of the deep Earth because the technique can be applied anywhere broadband seismic arrays exist.

A High- and Low-Noise Model for High-Quality Strong-Motion Accelerometer Stations

We present reference noise models for high-quality strong-motion accelerometer installations. We use continuous accelerometer data to derive very broadband (50 Hz–100 s) high- and low-noise models. The proposed noise models are compared (1) to the broadband seismometer Peterson (1993) noise models; (2) the datalogger self-noise and background noise levels at existing Swiss and Southern California strong-motion stations; and (3) typical earthquake signals recorded in Switzerland and worldwide. The accelerometer low-noise model (ALNM) is dominated by instrument noise from the sensor and datalogger. The accelerometer high-noise model (AHNM) reflects (1) at high frequencies the acceptable site noise in urban areas, (2) at mid-periods the microseismal peaks and (3) at long periods the maximum noise observed from well-insulated sensor/datalogger systems placed in vault quality sites. This study also provides confirmation of the remarkable capability of modern strong-motion accelerometers to record low-amplitude ground motions with seismic observation quality over a broad frequency range.

On the multi-fractal characteristics of the ULF geomagnetic field before the 1993 Guam earthquake

Abstract. Ida et al. (2005) document significant changes in the multi-fractal parameters of the ULF geomagnetic field H component starting about one month before the 1993 Guam earthquake. According to the authors, these multi-fractal signatures can be considered as precursory signals of the Guam earthquake. As a consequence, they conclude that the multi-fractal analysis may have an important role in the development of short-term earthquake prediction capabilities. Since this and other similar reports have motivated the idea that earthquake prediction based on electromagnetic precursory signals may one day become a routine technique, the presumed precursors need to be validated through independent datasets. In this review the seismogenic origin of the multi-fractal magnetic signatures documented by Ida et al. (2005) before the 8 August 1993 Guam earthquake is seriously put into question. By means of the geomagnetic ΣKp index, it is demonstrated that these multi-fractal parameter changes are normal signals induced by the variation of the global geomagnetic activity level.

A low-g electrostatically actuated resonant switch

This work investigates a new concept of an electrostatically actuated resonant switch (EARS) for earthquake detection and low-g seismic applications. The resonator is designed to operate close to the instability bands of frequency–response curves, where it is forced to collapse dynamically (pull-in) if operated within these bands. By careful tuning, the resonator can be made to enter the pull-in instability zone upon the detection of the earthquake signal, thereby snapping down as an electric switch. Such a switching action can be functionalized for alarming purposes or can be used to activate a network of sensors for seismic activity recording. The EARS is modeled and its dynamic response is simulated using a nonlinear single-degree-of-freedom model. Experimental investigation is conducted demonstrating the EARS’ capability of being triggered at small levels of acceleration as low as 0.02g. Results for the switching events for several levels of low-g accelerations using both theory and experiments are presented and compared.

Response spectra for differential motion of structures supports during earthquakes in Egypt

Differential motions of ground supports of stiff structures with large plan dimensions and separate foundations under earthquakes were studied by researchers during the last few decades. Such a type of structural response was previously underestimated. The importance of studying such a response comes up from the fact that usually the structures affected are of strategic importance such as bridges. During their expected life, structures may experience vibrations excited by ground waves of short wavelengths during near-source earthquakes, or during amplified earthquake signals, during explosions, or during vibrations induced from nearby strong vibration sources. This is the case when the differential motion of supports becomes considerable. This paper aims to review the effects of seismic signal variations along the structures dimensions with emphasis on Egypt as a case study. The paper shows some patterns of the damage imposed by such differential motion. A replication of the differential motion in the longitudinal direction is applied on a frame bridge model. The resulting straining actions show the necessity for considering the differential motion of supports in the design of special structures in Egypt. Finally, response spectra for the differential motion of supports, based on the available data from previous earthquakes in Egypt, is derived and proposed for designers to include in the design procedure when accounting for such type of structural response, and especially in long-span bridges.

Wavelet-based AR–SVM for health monitoring of smart structures

This paper proposes a novel structural health monitoring framework for damage detection of smart structures. The framework is developed through the integration of the discrete wavelet transform, an autoregressive (AR) model, damage-sensitive features, and a support vector machine (SVM). The steps of the method are the following: (1) the wavelet-based AR (WAR) model estimates vibration signals obtained from both the undamaged and damaged smart structures under a variety of random signals; (2) a new damage-sensitive feature is formulated in terms of the AR parameters estimated from the structural velocity responses; and then (3) the SVM is applied to each group of damaged and undamaged data sets in order to optimally separate them into either damaged or healthy groups. To demonstrate the effectiveness of the proposed structural health monitoring framework, a three-story smart building equipped with a magnetorheological (MR) damper under artificial earthquake signals is studied. It is shown from the simulation that the proposed health monitoring scheme is effective in detecting damage of the smart structures in an efficient way.

System identification of smart structures using a wavelet neuro-fuzzy model

This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure–MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input–output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure–MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

Evaluation of earthquake signal characteristics for early warning

This paper evaluates different characteristics for earthquake early warning. The scaling relationships between magnitude, epicenter distance and calculated parameters are derived from earthquake event data from USGS. The standard STA/LTA method is modified by adding two new parameters to eliminate the effects of the spike-type noise and small pulsetype noise ahead of the onset of the P-wave. After the detection of the P-wave, the algorithm extracts 12 kinds of parameters from the first 3 seconds of the P-wave. Then stepwise regression analysis of these parameters is performed to estimate the epicentral distance and magnitude. Six different parameters are selected to estimate the epicentral distance, and the median error for all 419 estimates is 16.5 km. Four parameters are optimally combined to estimate the magnitude, and the mean error for all events is 0.0 magnitude units, with a standard deviation of 0.5. Finally, based on the estimation results, additional work is proposed to improve the accuracy of the results.

Testing of an automatic earthquake detection method on data from Station Nord, Greenland

Earthquakes are continuously monitored by a global network of several thousand seismic stations equipped with highly sensitive digital seismometers. The Geological Survey of Denmark and Greenland (GEUS) takes part in it by operating five seismic stations in Denmark and 18 in Greenland, some of the latter in collaboration with international partners. There are two main ways of detecting earthquakes from digital recordings of seismometers: (1) by a manual review of the data by an expert in processing seismic earthquake signals and (2) by an automatic method that uses a computerised algorithm to analyse the recordings.