Analysis Of Seismic Signals Research Articles

Generalized S Transform with Adaptive Optimized Window and its Application in Seismic Signal Analysis

Generalized S Transform with Adaptive Optimized Window and its Application in Seismic Signal Analysis

Automatic Recognition of Landslides Based on Neural Network Analysis of Seismic Signals: An Application to the Monitoring of Stromboli Volcano (Southern Italy)

In the last 9 years, the amount and the quality of geophysical and volcanological observations of Stromboli's' activity have undergone a marked increase. This new information highlighted that the landslides on the Sciara del Fuoco flank are tightly linked to the volcanic activity. Actually, at the beginning of the December 28, 2002, effusive eruption, the seismic monitoring network was less dense than now, and therefore it is not known if there was an increase in the landslide rate before the eruption. Despite this, it is known that a big landslide occurred 2 days after the beginning of the eruption which caused a tsunami (December 30, 2002). More recently, the effusive eruption in February 2007 was preceded by an increase in landslides on the Sciara del Fuoco flank, which were recorded by the seismological monitoring system that had been improved after the 2002–2003 crisis. These episodes led us to believe that monitoring the Sciara del Fuoco flank instability is an important topic, and that landslides might be significant short-term precursors of effusive eruptions at the Stromboli volcano. To automatically detect landslide signals, we have developed a specialized neural algorithm. This can distinguish between landslides and the other types of seismic signals usually recorded at the Stromboli volcano (i.e., explosion quakes and volcanic tremor). The discrimination results show an average performance of 98.67 %. According to the experience of the crisis of 2007, to identify changes that can be considered as precursors of effusive eruptions, we set up an automatic decision-making method based on the neural network responses. This method can operate on a continuous data stream. It calculates a landslide percentage index (LPI) that depends on the number of records that are classified by the net as landslides over a given time interval. We tested the method on February 27, 2007, including the beginning of the effusive phase. The index showed an increase as early as at 09:00 UTC on that day and reached its maximum value (100 %) at 12:00, about 40 min before the onset of the eruption. After the beginning of the effusive phase, the index remains high due to the blocks that roll down along the slope from the front of the lava flow. On the basis of these tests, we propose a decision-making method that is able to recognize a trend in the LPI similar to that of 2007 eruption, allowing the identification of precursors of effusive phases at the Stromboli volcano.

B3-07 阿蘇における地震・空振相関解析(モニタリング手法の開発,口頭発表)

B3-07 阿蘇における地震・空振相関解析(モニタリング手法の開発,口頭発表)

An application of matching pursuit time-frequency decomposition method using multi-wavelet dictionaries

In the time-frequency analysis of seismic signals, the matching pursuit algorithm is an effective tool for non-stationary signals, and has high time-frequency resolution and a transient structure with local self-adaption. We expand the time-frequency dictionary library with Ricker, Morlet, and mixed phase seismic wavelets, to make the method more suitable for seismic signal time-frequency decomposition. In this paper, we demonstrated the algorithm theory using synthetic seismic data, and tested the method using synthetic data with 25% noise. We compared the matching pursuit results of the time-frequency dictionaries. The results indicated that the dictionary which matched the signal characteristics better would obtain better results, and can reflect the information of seismic data effectively.

Experiences of a Multidisciplinary Course on Geo-Signal Processing From a DSP Perspective Offered in Electrical Engineering at King Fahd University of Petroleum and Minerals

The purpose of this paper is to share the experience gained in, and the efforts made toward, introducing and implementing a new course in the challenging and important area of geophysical signal processing at the Electrical Engineering (EE) Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia. The new course, titled “Geo-Signal Processing,” was offered both at the graduate level and as a special topics course to undergraduates. The course was developed in collaboration with the Earth Sciences Department at KFUPM. This paper contributes new information because it stresses the multidisciplinary aspects of digital signal processing (DSP) technologies when applied to estimating the Earth's layered structure on the basis of seismic data. Unlike many Earth sciences seismic data processing courses, this Geo-Signal Processing course also emphasizes that the perspective taken by those working in DSP is different from that taken by geophysicists. The course presents DSP with particular emphasis on seismic data signals and the artifacts accompanying them while covering the principles and algorithms needed for processing seismic signals in both deterministic and statistical fashion. Topics include, but are not limited to, basic seismic theory, acquisition of seismic data, analysis of seismic signals and noise, deterministic filtering of seismic data, and statistical processing of seismic data.

Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal

The Hilbert-Huang transform (HHT) is a novel signal analysis method in seismic exploration. It integrates empirical mode decomposition (EMD) and classical Hilbert transform (HT), which can express the intrinsic essence using simple and understandable algorithm. But there is a serious mode mixing phenomenon in EMD. To solve the mode mixing problem, a noise-assisted data analysis method called ensemble empirical mode decomposition (EEMD) is adopted instead of EMD. In this paper, the applications of EMD and EEMD on time-frequency analyzing behaviors were compared, and the results show that (1) EMD decomposes an original nonlinear and non-stationary signal into a series of simple intrinsic mode functions (IMFs), but with the mode mixing phenomenon. (2) EEMD skillfully solves the mode mixing problem by adding a white noise to the original signal. (3) The synthetic signal example reveals the remarkable ability of EEMD to decompose the signal into different IMFs and analyze the time-frequency distribution of the signal. (4) The time-frequency spectrum obtained by EEMD more realistically reflects the real geology than by EMD. (c) 2012 Elsevier B.V. All rights reserved.

Monitoring the structural dynamic response of a masonry tower: comparing classical and time-frequency analyses

The monitoring of the evolution of structural dynamic response under transient loads must be carried out to understand the physical behaviour of building subjected to earthquake ground motion, as well as to calibrate numerical models simulating their dynamic behaviour. Fourier analysis is one of the most used tools for estimating the dynamic characteristics of a system. However, the intrinsic assumption of stationarity of the signal imposes severe limitations upon its application to transient earthquake signals or when the dynamic characteristics of systems change over time (e.g., when the frequency of vibration of a structure decreases due to damage). Some of these limitations could be overcome by using the Short Time Fourier Transform (STFT). However, the width of the moving window adopted for the analysis has to be fixed as a function of the minimum frequency of interest, using the best compromise between resolution in both the time and frequency domains. Several other techniques for time-frequency analysis of seismic signals recorded in buildings have been recently proposed. These techniques are more suitable than the STFT for the application described above, although they also present drawbacks that should be taken into account while interpreting the results. In this study, we characterize the dynamic behaviour of the Falkenhof Tower (Potsdam, Germany) while forced by ambient noise and vibrations produced by an explosion. We compare the results obtained by standard frequency domain analysis with those derived by different time-frequency methods. In particular, the results obtained by the standard Transfer Function method, Horizontal to Vertical Spectral Ratio (HVSR), Short Time Fourier Transform (STFT), Empirical Mode Decomposition (EMD) and S-Transform are discussed while most of the techniques provide similar results, the EMD analyses suffer some problems derived from the mode mixing in most of the Intrinsic Mode Functions (IMFs).

Slope instabilities in Dolomieu crater, Réunion Island: From seismic signals to rockfall characteristics

[1] The seismic signals of hundreds of rockfalls within Dolomieu crater, Piton de la Fournaise volcano, Reunion Island, have been analyzed to investigate a possible link between physical rockfall-generating processes and associated seismic signal features. Moreover, indirect observation of rockfalls via the seismic signals they generate can provide useful data for studying volcanoes and the temporal variations of their structure. An increase in the number of rockfall events and their volumes might be an indicator of structural weakness and deformation of the volcano associated with potential eruptive activity. The study focuses on a 10 month period following the 6 April 2007 crater floor collapse within Dolomieu crater, from May 2007 to February 2008. For granular flows a scaling law is revealed between seismic energy and signal duration. A semiempirical approach based on both analytical analysis and numerical simulation of these flows shows that a similar scaling law exists between the difference of potential energy computed for an event and its propagation times and also emphasizes the effect of local topography on this scaling law. Simulated and observed data were compared to evaluate the proportion of potential energy dissipated in the form of seismic waves and confirm a direct link between the seismic energy and potential energy of a given granular flow. The mean ratio of seismic to potential energy is of the order of 10−4, comparable to the range of values observed in previous studies. A simple method based on these ratios is proposed to estimate the volumes of rockfalls from their seismic signal. Observed seismic energy and the frequency of rockfalls decreased at the beginning of the studied period and reached a stable level in July, thus suggesting a postcollapse relaxation time of Dolomieu crater structure of about 2 months from seismic signal analysis, which is confirmed by deformation data. The total rockfall volume over the study period is estimated to be 1.85 Mm3.

Characterization of alpine rockslides using statistical analysis of seismic signals

[1] Seismic data analysis is a powerful tool for remote characterization of rock slope failures. Here we develop quantitative estimates of fundamental rockslide properties (e.g., volume) based solely on data from an existing regional seismic network. We assembled a data set of twenty known rockslides in the central Alps (with volumes between 1,000 and 2,000,000 m3) and analyzed their corresponding seismograms. Common signal characteristics include emergent onsets, slowly decaying tails, and a triangular spectrogram shape. The main component of seismic energy is contained in frequencies below ∼3–4 Hz, while higher-frequency signals may be caused by block impacts. Location estimates were generated using automatic arrival time picks and resulted in a mean location error of 10.9 km. A linear relationship for the detection limit of a rockslide as a function of volume was identified for our seismic station network. To estimate rockslide volume, runout distance, drop height, potential energy, and Fahrboschung (angle of reach), we extracted five simple metrics from each seismogram: signal duration, peak value of the ground velocity envelope, velocity envelope area, risetime, and average ground velocity. Using multivariate linear regression, the combination of duration, peak envelope velocity, and envelope area best estimated event parameters, with r2 values ranging between 0.8 and 0.88. Three new rockslides were then used to validate our method, and volume, runout, drop height, and potential energy were estimated within the correct order of magnitude. When provided with a suitable data set of rockslide events, our method can be easily adapted to other regions and seismic networks.

Interactive DIMAS program for processing seismic signals

The DIMAS program is designed for detailed processing and visual analysis of digital seismic signals from digital seismic stations. It is specially designed for users whose task is to quickly determine the parameters of an earthquake from one or a group of seismic stations in the system of seismic monitoring and tsunami warning services. The program allows for a variety of operations with seismic signals in the temporal and spectral ranges. It has a simple and functional user interface.

S-Wave Velocities of the Lithosphere–Asthenosphere System in the Caribbean Region

An overview of the S-wave velocity (V s) structural model of the Caribbean with a resolution of 2° × 2° is presented. New tomographic maps of Rayleigh wave group velocity dispersion at periods ranging from 10 to 40 s were obtained as a result of the frequency time analysis of seismic signals of more than 400 ray-paths in the region. For each cell of 2° × 2°, group velocity dispersion curves were determined and extended to 150 s by adding data from a larger scale tomographic study (Vdovin et al., Geophys. J. Int 136:324–340, 1999). Using, as independent a priori information, the available geological and geophysical data of the region, each dispersion curve has been inverted by the “hedgehog” non-linear procedure (Valyus, Determining seismic profiles from a set of observations (in Russian), Vychislitielnaya Seismologiya 4, 3–14. English translation: Computational Seismology (V.I. Keylis-Borok, ed.) 4:114–118, 1968), in order to compute a set of V s versus depth models up to 300 km of depth. Because of the non-uniqueness of the solutions for each cell, a local smoothness optimization has been applied to the whole region in order to choose a three-dimensional model of V s, satisfying this way the Occam's razor concept. Several known and some new main features of the Caribbean lithosphere and asthenosphere are shown on these models such as: the west directed subduction zone of the eastern Caribbean region with a clear mantle wedge between the Caribbean lithosphere and the subducted slab; the complex and asymmetric behavior of the crustal and lithospheric thickness in the Cayman ridge; the predominant oceanic crust in the region; the presence of continental type crust in Central America, and the South and North America plates; as well as the fact that the bottom of the upper asthenosphere gets shallower going from west to east.

Analysis of seismic signals recorded on a prone-to-fall rock column (Vercors massif, French Alps)

A small-aperture (40 m) short-period seismic array was installed during four months on the Vercors massif (Western French Alps) at the top of a limestone column which collapsed one month and a half later. During this monitoring period, 193 seismic events were recorded by the seven seismometers of the array. Signal analysis yielded three main types of nearby seismic events to be identified from temporal and spectral characteristics: microearthquakes (single or multiple events), individual block falls and rock falls. It turned out that 60 per cent of the 193 events were classified as microearthquakes, exhibiting distinct P and S waves, while 17 per cent of these events remained unclassified. Out of the microearthquakes, 40 events with a good signal-to-noise ratio were selected and processed. P- and S-wave traveltimes were picked on the records and the inferred hypocentral distances agree with the two zones of the scarp exhibiting fresh ruptures after the fall. Polarization analysis of the 3-C records, along with numerical simulations, allowed discriminating between the two possible rupture mechanisms (toppling and sliding). Shear rupture (sliding) was the predominant mode in the lower part of the column whereas traction rupture (toppling) affected the upper part. Finally, the comparison between the ground motions recorded on the column and on the rock mass showed a systematic amplification on the column. Signal processing and numerical modelling both suggest that this amplification resulted from the excitation of the natural frequencies of the column and is particularly high (>3) for microearthquakes occurring at the column-to-mass interface.

Detection and diagnosis of model parameter and noise variance changes with application in seismic signal processing

The change detection and diagnosis methods have gained considerable attention in scientific research and appears to be the central issue in various application areas. These applications need some robust change detection schemes to work well and separate the changes in the experimental conditions from the real changes in the system, especially for systems with arbitrary and non-stationary known or unknown inputs. The objective of the paper is to develop such kind of change detection and diagnosis scheme. In the first part of the paper we give the conceptual description of some classical change detection schemes based on sliding windows and likelihood techniques. Then, starting from these classical change detection schemes, a new algorithm able to discriminate between the model parameter and noise variance changes is presented. Finally, we include some Monte Carlo simulations for change detection in a second order FIR model and experimental results obtained in analysis of seismic signals, using the proposed approach.

Wavelet analysis of short range seismic signals for accurate time of arrival estimation in dispersive environments

This study proposes a wavelet-based method for time of arrival estimation for source localisation purposes. The investigated case concerns seismic signals with poor correlation owing to dispersive propagation and high noise level. The Haar wavelet is selected for its low computational complexity and its good locality in time domain which is essential for the analysis of transient signals. The proposed method requires no a priori knowledge about the spectral characteristics of the signals, because the algorithm defines the optimum scales for the extraction of information by time-domain features as the signal is acquired. The performance of the algorithm is evaluated by computer simulations and application on real data for three different thresholding techniques. The results corroborate the suitability of the proposed method for source localisation applications.

Magma dynamics of 2007 Stromboli effusive eruption as revealed by high precision location of seismic events

Stromboli is considered one of the most active volcanoes in the world, and its persistent but moderate explosive activity is only interrupted by occasional episodes of more vigorous activity accompanied by lava flows. A new effusive eruption began in late February 2007 and was characterized by intense seismic activity throughout the whole period. The accurate seismic signals analysis showed the presence of families of events with similar waveform signatures (i.e., multiplets) located beneath the crater region. Since traditional location techniques do not allow obtaining reliable hypocenters, our analysis focused on high precision locations of the seismicity in order to better define the source geometry of the events. Hypocenters, therefore, have been relocated considering two steps: the first, based on a robust probabilistic approach, is used to find the absolute position of the clusters and the second exploits a master-event concept for the relative location of the events. Finally, the shape of the clusters and the temporal migration of the foci were correlated with the eruptive phases. The results show that the occurrence of a cluster of events is related to the opening and closure of a vent opened in the Sciara del Fuoco slope and, in particular, to the intrusion of a dike injected by central conduit in a radial direction, whereas another cluster lies in a narrow vertical volume positioned under the crater area. The geometry of the clusters suggests a source region depicting the shallower feeding system. Overall, the results highlight that the high precision locations method is an efficient and quick tool to obtain a better understanding of the magmatic processes occurring during an ongoing eruption.

Seismic monitoring of Séchilienne rockslide (French Alps): Analysis of seismic signals and their correlation with rainfalls

In the French Alps, Séchilienne rockslide is one of the natural phenomena that presents one of the highest levels of risk in terms of socioeconomical outcomes. This rockslide has been active for a few decades and has been instrumented since 1985. The current very active volume of this rockslide is estimated to be up to 5 million m3, located on the border of a slowly moving mass reaching 50–100 million m3. The velocity of the most active zone reached 1.4 m/yr in 2008, about twice the value of 2000. A seismic network was installed on this rockslide in May 2007 to supplement the monitoring system. It has now recorded several thousand events, mostly rockfalls but also hundreds of local and regional earthquakes, which can be distinguished and classified from their signal characteristics. Rockfalls and microseismicity, which occur in bursts of activity, are found to be weakly, but significantly, correlated with rainfall. Rockfall occurrence increases linearly with precipitation, with however strong fluctuations of the numbers of rockfalls per day for the same rainfall intensity. No threshold was found for rainfall triggering, with even 1 mm of rain being enough to trigger rockfalls. Rockfall activity starts almost immediately during a rainfall episode and lasts for several days after the rainfall. Rain also induces strong accelerations of the rockslide movement, which also start quasi‐instantaneously and last for about a month.

Polarization Analysis in the Discrete Wavelet Domain: An Application to Volcano Seismology

Abstract We propose a method for analyzing the polarization of three-component digital recordings using the discrete wavelet transform (DWT). This method allows for the automatic detection and separation of seismic phases that have a coherent linear or elliptical polarization. It can be correctly used in the analysis of seismic signals relating to volcanic activity because they arise from a complex wave field that consists of near-field and far-field components that have frequency-dependent polarization. First, the analytic extension of the signal is decomposed using DWT, then each single component is used to determine a local complex polarization vector in the timescale domain. This analysis reveals the presence of seismic phases with coherent polarization over a range of DWT scales and finite temporal intervals. Using the orthogonality property of the DWT, it is possible to isolate a single coherent component, reconstructing it in the time domain and computing the full polarization tensor. This procedure can be fully automated, introducing a quantitative definition of wavelet polarization coherence on the DWT dyadic grid. A recursive algorithm (called POLWAV) starts from the wavelet coefficient with the highest modulus, and then selects all of the neighbors that show coherence with it above a given threshold. We show how the POLWAV algorithm can be used for separating wave-field components and for detecting coherent seismic phases on continuous recordings. Example applications to actual seismic recordings at Stromboli Volcano (Tyrrhenian Sea) are presented.

Interactive Matlab software for the analysis of seismic volcanic signals

The computer program presented in this note applies methods commonly used in volcano seismology to the analysis of seismic data. It is complementary to the classic seismological software packages used to process tectonic earthquake seismograms. The program's six user-friendly interfaces provide a large set of tools for reading data in many different formats, for carrying out spectral analysis, autoregressive modelling and deconvolution, for calculating time–frequency representations and Real-time Seismic Amplitude Measurement (RSAM)-type time series, and for detecting volcanic tremors and discrete events in long-duration records. A separate version of the program facilitates signal classification when preparing training databases for automatic recognition systems.

SeisPrho: An interactive computer program for processing and interpretation of high-resolution seismic reflection profiles

SeisPrho is an interactive computer program for processing and interpreting high-resolution seismic reflection profiles developed using the Delphi/Kylix multiplatform programming environment. For this reason, it is available under Windows™ and Linux™ operating systems. The program allows the users to handle SEG-Y data files (and other non-standard formats) carrying out a processing sequence over the data to obtain, as a final result, bitmap images of seismic sections. Some basic algorithms are implemented, including filtering and deconvolution. However, the main feature of SeisPrho is its interactive graphic interface, which provides the user with several tools for interpreting the data, such as reflector picking and map digitizing. Moreover, the program allows importing and geo-referencing maps and seismic profiles in the form of digital images. Trace-by-trace analysis of seismic signal and sea-bottom reflectivity is also implemented, as well as other special functions such as compilation of time-slice maps from close-spaced grids of seismic lines. SeisPrho is distributed as public domain software for non-commercial purposes by the Marine Geology division of the Istituto di Scienze Marine ( ISMAR-CNR). This paper is an introduction to the program and a preliminary guide to the users.

Karhunen–Loève spectral analysis in multiresolution decomposition

In this work we develop a method for pinpointing the scales in a multiresolution decomposition where coherent structures appear. A sequence of images yielded from the wavelet multiresolution analysis of seismic signals are analyzed in the Karhunen–Loeve (KL) space. Using KL decomposition we distinguish two coherent structures in the scale–images: the ground roll and the hyperbolic lines characteristic of geologic layers. Moreover, the KL spectrum also points out the high frequency noise. In this way the method allow us to split three energy mode regimes in the data and, as a consequence, to select the relevant geologic information. The method can be extended to other problems where coherent structures need to be recognized.