Analysis Of Earthquake Data Research Articles

Soil-structure interaction assessment combining deconvolution of building and field recordings with polarization analysis: application to the Matera (Italy) experiment

AbstractIn this study, the wavefield radiated from a building to its surroundings is identified and extracted from M4.6 earthquake recordings collected by sensors installed in a building and on the nearby athletic field in Matera (Italy) using a new approach for soil-structure interaction assessment. The proposed approach for earthquake data analysis combines in an innovative way two methods already used in seismology and engineering seismology: deconvolution and polarization analysis. The approach enables the identification, reconstruction, and characterization of the wavefield radiated from a vibrating building into its surroundings, and the estimation of the amount of energy associated with it. The approach consists of four steps: (1) estimation of the resonant frequencies of the building, (2) deconvolution of the earthquake recordings from a building and its surroundings, (3) identification of the seismic phases, reconstruction of the signal transmitted from the building to its surroundings, and estimation of its energy, and (4) polarization analysis. Analysis of recordings of the M4.6 event highlighted that the motion related to the wavefield radiated from the building to the ground was mostly linearly polarized in the radial and transverse planes, while a clear ellipticity was observed only in the horizontal plane. The wavefield radiated from the building might be dominated by unconventionally polarized surface waves, i.e., quasi-Rayleigh waves or a combination of quasi-Rayleigh and quasi-Love waves. The results indicated that the energy transmitted from the analyzed vibrating building to its surroundings was significant and decreased the ground motion shaking due to the out-of-phase motion.

Agglomerative Clustering of 2022 Earthquakes in North Sulawesi, Indonesia

This paper presents a cluster analysis of earthquake data in the surrounding region of North Sulawesi, Indonesia. The dataset comprises seismic data recorded throughout the year 2022, obtained from the BMKG earthquake repository. A total of 211 earthquakes were included in the analysis, with a minimum magnitude threshold of 2.5 and a maximum depth of 300 km. The agglomerative clustering technique, combined with the elbow method, was employed to determine the optimal and distinct number of clusters. As a result, four unique clusters were identified. Cluster 1 exhibited high magnitudes, with an average magnitude of 4.4, and shallow depths, averaging at 20 km. Cluster 2 also had high magnitudes, averaging at 4.4, but deeper depths, with an average of 199 km. Cluster 3 consisted of earthquakes with low magnitudes, averaging at 3.4, and shallow depths, averaging at 21 km. Lastly, Cluster 4 comprised earthquakes with low magnitudes, averaging at 3.4, but deeper depths, with an average of 136 km. Among the 211 earthquakes, 29 were assigned to Cluster 1, 39 to Cluster 2, 100 to Cluster 3, which had the highest population, and 43 to Cluster 4. This study provides valuable insights into the clustering patterns and characteristics of earthquakes in the region, contributing to a better understanding of seismic activity in North Sulawesi, Indonesia

Lithospheric evolution of eastern Arabia based on surface wave and receiver function analyses

The geodynamic evolution of Eastern Arabia comprises three major events that must have acted on the entire lithosphere below the present-day passive continental margin. Starting with the Pan-African plate assembly during the Late Proterozoic, NNE-oriented structural trends were established which were reactivated during Infracambrian and Ordovician extension. Permian Pangean breakup and rifting created two rift axes to form the present-day geometry of the eastern and northern passive margins of Eastern Arabia. SW-directed obduction of the Semail Ophiolite during the Late Cretaceous was the latest major tectonic event that shapes the present-day geology of northern Oman. Recent anisotropic ambient noise tomography, surface wave analysis of earthquake data and receiver function analysis reveal the present-day lithospheric thickness of ∼100 km, and the previously unknown, highly variable internal structure of the East Arabian continental crust. We integrate all newly available seismological observations into a consistent geodynamic model that describes the evolution of lithospheric thickness and crustal heterogeneity, identifies crustal segmentation and relates inherited structures to lateral variations within the dynamics of ophiolite obduction.A sharp, NNE-striking contrast of ∼10% in shear wave velocity (VS) in the lower crust separates felsic Arabian continental crust in the NW from intermediate lithologies in the NE of Oman. This crustal segmentation is interpreted as being established during the Pan-African orogeny. High VS in the lower crust of northeastern Oman is likely due to Permian mafic intrusions related to the Pangean breakup. Mild crustal thickening below todays high topography is attributed to Late Cretaceous time, where northward continental subduction below the Neo-Tethys led to underthrusting and thickening of the continental crust and ultimately to obduction of the Semail Ophiolite.After post-obduction relaxation, we argue that lithospheric thickness, margin perpendicular fast directions of anisotropy and contemporaneous mid- to late Eocene basanite intrusions are indicative of an overall relaxed state of stress of the lithosphere at that time. In consequence, we propose marginal erosion of the base lithosphere on the passive Arabian plate margin during the Late Eocene as a geodynamic process that facilitated margin-wide emergence of the Oman Mountains. Altogether, the present configuration of the lithosphere in northeastern Arabia preserves imprints from Late Proterozoic Pan-African assemblage, Permian Pangean break-up, Late Cretaceous subduction and ophiolite obduction, and Cenozoic post-obduction evolution.

Intelligent solutions for earthquake data analysis and prediction for future smart cities

The analysis and prediction of the Earthquake for smart cities are of greatest significance because all the critical infrastructure like drinking water resources, mobile networks, healthcare, power grid, and transportation, may affect the social-economic balance of the place which is tampered with such natural disasters. An earthquake has some effect on the environment due to the sudden movement of tectonic plates known as Earthquake Environmental Effects (EEE). These changes can be divided into 2 categories namely primary (e.g. subsidence, surface faulting) and secondary effects (displaced rocks, tsunami, landslides). In this paper, the major contribution revolves around earthquakes and their analysis. Earthquakes occur due to the shifting of tectonic plates on the lithosphere. These movements occur due to the Frictional stress between gliding plate borders builds up and leads to fault line failure. This sudden shaking of the plates releases a certain kind of energy in the Lithosphere which creates seismic waves. Earthquakes are also categorized into different classes ranging from minor to great, depending on their magnitude.

Analysis of Earthquake Activity in Indonesia by Clustering Method

Indonesia is an area where three large tectonic plates meet, namely the Indo-Australian, Eurasian and Pacific plates, so that Indonesia is included in the earthquake-prone category, with 11,660 earthquake vibrations identified in the Meteorology, Climatology and Geophysics Agency (BMKG) database in 2019 The purpose of this study is to develop a classification of the distribution of earthquakes in Indonesia in 2019 based on the values of magnitude, depth, and position. This research was conducted by using the clustering method based on the K-means algorithm and the DBSCAN algorithm as a comparison. The results of the clustering show that the earthquake data analysis using the K-Means algorithm is superior with a silhouette index value of 0.837, while the DBSCAN algorithm has a silhouette index value of 0.730.

Neutrosophic K-means for the analysis of earthquake data in Ecuador

The occurrence of earthquakes may have catastrophic and devastating consequences for the inhabitants of the place where they occur. Some regions are characterized by the high frequency of this type of natural phenomenon. Such is the case of Ecuador, a country with a high seismic index due to its location in a subduction zone between the Pacific Plate and the South American Plate. Predictions in the behavior of earthquakes are a way of prevention that allows taking measures according to vulnerability. Although it is difficult to accurately predict the occurrence of an earthquake, there are dissimilar types of analysis to observe its behavior and patterns of occurrence. The nature of earthquakes and their monitoring variables usually make up large databases. For its processing and subsequent analysis of the results, it is convenient to use statistical techniques of Data Mining such as K-Means. In this work, the classic K-Means method is combined with Neutrosophy to improve the results obtained by taking into account the indeterminacy of such complex data sets and including the diversity of the data and its fluctuation, due to the proximity among the boundaries and their membership clusters.

Detrended fluctuation analysis of earthquake data

The detrended fluctuation analysis (DFA) is extensively useful in stochastic processes to unveil the long-term correlation. Here, we apply the DFA to point processes that mimick earthquake data. The point processes are synthesized by a model similar to the Epidemic-Type Aftershock Sequence model, and we apply the DFA to time series $N(t)$ of the point processes, where $N(t)$ is the cumulative number of events up to time $t$. Crossover phenomena are found in the DFA for these time series, and extensive numerical simulations suggest that the crossover phenomena are signatures of non-stationarity in the time series. We also find that the crossover time represents a characteristic time scale of the non-stationary process embedded in the time series. Therefore, the DFA for point processes is especially useful in extracting information of non-stationary processes when time series are superpositions of stationary and non-stationary signals. Furthermore, we apply the DFA to the cumulative number $N(t)$ of real earthquakes in Japan, and we find a crossover phenomenon similar to that found for the synthesized data.

Earthquake data analysis and data visualization of Maharashtra state, India from 1912 to 2009 using R programming

This paper presents the seismic data analysis and visualization using R programming for the state of Maharashtra, India. Maharashtra has witnessed a significant number of earthquakes ranging from low to high in magnitude. The past earthquake data of a particular location are useful to the civil and structural engineers for the safe and efficient planning, designing and construction of seismic resistant buildings in that locality. In this paper the past earthquake data of Maharashtra from the year 1912 to 2009 are collected and analyzed. There are 53 earthquake data with magnitudes ranging from 3.6 to 7.2. This analysis shows the relationship between magnitude and depth, intensity and PGA, visualization of classification of earthquakes depends on magnitude and intensity.

Morphotectonic and earthquake data analysis of interactional faults in Sabzevaran Area, SE Iran

We used satellite images, earthquake catalogues and field observations to study several active fault systems and their interactions in Sabzevaran Area in SE Iran. The focus of this study is to verify the link between the active faults, their kinematics and seismic activity. Field observations and geomorphological analysis highlight the interaction of the active faults. Moreover, most of the tectonic activity is observed in the area, related to the Chahmazrae- North Faryab shear zone. Most of the earthquakes in this shear zone are reverse and occur in the deeper crust while aftershocks dominantly occur in the shallower crust. The Main Zagros Reverse Fault (MZRF) is the source of reverse events and the Chahmazrae-North Faryab shear zone is source of left-lateral, oblique reverse faulting events, and strike-slip events. These types of the earthquakes in the study area confirm the idea of tectonic proximity of the root faults and shear zone. In the interaction area, minor fractures begin to develop and are progressively linked to the main faults. In the en échelon arrangement of the faults, the minor faults have grown and linked the en échelon segments of the faults. It seems that the earthquake ruptures can spontaneously propagate across both extensional and compressional fault steps. This propagation occurs along strike-slip faults such as Sabzevaran fault and its branches.

Investigation of a seismic teleconnection model between Iran and Iceland regions during 1980–2018

In this study, a rapid statistical model was assumed to answer the research hypothesis, concerning a triggering relationship between Iran and Iceland earthquakes. For this purpose, a long-term analysis of earthquake data (1980–2018) was considered for both Iran and Iceland regions using the cross-correlation function (CCF) and the time-lagged correlation method. Results revealed that the frequency and maximum magnitude of earthquakes in Iran could lead the earthquake frequency and magnitude in Iceland within a period of 4 months, revealing a positive association through the time shift. On this basis, the lagged time of seismic teleconnection can be assumed as 4 months between predictor (Iran) and indicator (Iceland) zones. Particularly, the increasing earthquake occurrences in Iran can notify subsequent increasing earthquake events in Iceland within 78 and 18 day lagged-time, confirming the hypothesis of seismic teleconnection. Owing to the seismically active status of both Iran and Iceland regions, the crustal relationships, e.g., influenced by global scale divergence and convergence of plates, could describe the finding of this study.

Big Data Environment for Realtime Earthquake Data Acquisition and Visualization

Indonesia is a country that has the highest level of earthquake risk in the world. In the past 10 years, there have been ± 90,000 earthquake events recorded and always increasing along with the explosion of earthquake data occurs at any time. The process of collecting and analyzing earthquake data requires more effort and takes a long computational time. In this paper, we propose a new system to acquire, store, manage and process earthquake data in Indonesia in real-time, fast and dynamic by utilizing features in the Big Data Environment. This system improves computational performance in the process of managing and analyzing earthquake data in Indonesia by combining and integrating earthquake data from several providers to form a complete unity of earthquake data. An additional function is the existence of an API (Application Programming Interface) embedded in this system to provide access to the results of earthquake data analysis such as density, probability density function and seismic data association between provinces in Indonesia. The process in this system has been carried out in parallel and improved computing performance. This is evidenced by the computational time in the preprocessing process on a single-core master node, which requires 55.6 minutes, but a distributed computing process using 15 cores can speeds up with only 4.82 minutes.

Model Driven Development Applied to Complex Event Processing for Near Real-Time Open Data.

Nowadays, data are being produced like never before because the use of the Internet of Things, social networks, and communication in general are increasing exponentially. Many of these data, especially those from public administrations, are freely offered using the open data concept where data are published to improve their reutilisation and transparency. Initially, the data involved information that is not updated continuously such as budgets, tourist information, office information, pharmacy information, etc. This kind of information does not change during large periods of time, such as days, weeks or months. However, when open data are produced near to real-time such as air quality sensors or people counters, suitable methodologies and tools are lacking to identify, consume, and analyse them. This work presents a methodology to tackle the analysis of open data sources using Model-Driven Development (MDD) and Complex Event Processing (CEP), which help users to raise the abstraction level utilised to manage and analyse open data sources. That means that users can manage heterogeneous and complex technology by using domain concepts defined by a model that could be used to generate specific code. Thus, this methodology is supported by a domain-specific language (DSL) called OpenData2CEP, which includes a metamodel, a graphical concrete syntax, and a model-to-text transformation to specific platforms, such as complex event processing engines. Finally, the methodology and the DSL have been applied to two near real-time contexts: the analysis of air quality for citizens’ proposals and the analysis of earthquake data.

SEDA a software package for the Statistical Earthquake Data Analysis: a tutorial application to the 2009 L'Aquila and the 2012 Emilia (Italy) sequences

The main purpose of this paper is to provide a tutorial application of SEDAv1.0, the first version of a software package, recently designed for the statistical analysis of earthquake data. SEDAv1.0 consists of a user-friendly Matlab-based interface, to facilitate the interaction with the application, and of a computational core of Fortran codes, to guarantee fast running times. The main part of SEDAv1.0 is devoted to the ETAS modeling. For the first time, an almost complete set of consistent tools based on ETAS models is collected in a single, free software. Moreover, SEDA guarantees the research reproducibility, which is becoming an increasingly major concern among scientists. The peculiarities of some routines of SEDAv1.0 are discussed in this paper, by the application to two important recent seismic sequences occurred in Italy. Specifically, the paper illustrates how using SEDAv1.0, to estimate the completeness magnitude and the b-value, to set and test the ETAS model and, finally, to identify the earthquakes sequences, basing on causal connections.

Self-Optimized Computational Method Calculating Robust b Values for Earthquake Catalogs Containing Small Number of Events.

In the Gutenberg-Richter relation that describes the frequency-magnitude distribution of earthquakes, the b value represents the distribution's slope. Since b values can be used for mapping the dynamic response of earthquake source, methodologies for calculating robust b values are of great importance. Although nowadays software which is meant for statistical analysis of earthquake data can determine b values with high accuracy, in occasions where catalogs that contain small number of earthquake events, the produced results are not satisfactory. In this paper we present a new self-optimized algorithm for a more efficient calculation of the b value. The algorithm's results are compared with two widely known software for statistical analysis of earthquake data, showing a better performance in evaluating b values for earthquake catalogs containing small number of events.

First results of inaugural deployments of the Australian National Ocean Bottom Seismograph Fleet

The Australian National Ocean Bottom Seismograph Fleet is part of AuScope’s Australian Geophysical Observing System (AGOS) — an initiative of the Australian Government funded through the Education Investment Fund. These instruments will greatly contribute to the understanding of the crust beneath oceanic basins surrounding Australia. In 2014–15 the Australian National OBS Fleet was utilised by the petroleum industry on a number of seismic surveys. High-quality data were recorded at all OBS deployment sites, often to offsets sufficiently large to detect Pn phases — refractions from the upper mantle. Analysis of earthquake data recorded during marine seismic surveys suggests strong interaction between anthropogenic signals (airgun source, vessel noise) and the natural environment, and allows arguing that in some instances earthquake energy contaminates marine reflection data in the frequency pass-band needed for petroleum exploration. Recording earthquake and airgun signals at fixed locations opens up a completely new possibility for calibration and comparison of those signal strengths and spectral compositions.

A new dawn for Australian ocean-bottom seismography

The Australian government recently acquired 20 state-of-the-art broadband ocean-bottom seismograph (OBS) units. These instruments were designed and built by Güralp Systems Ltd. (U. K.) with the ability to withstand extreme ocean depths to 6000 m and to remotely record data for as long as 12 months. The Australian national OBS fleet is part of AuScope's Australian Geophysical Observing System (AGOS) — an initiative of the Australian government funded through the Education Investment Fund (EIF). These instruments will contribute greatly to understanding of the crust beneath oceanic basins surrounding Australia. In 2014–2015, the Australian national OBS fleet was used by the petroleum industry on several seismic surveys. High-quality data were recorded at all OBS deployment sites, often to offsets sufficiently large enough to detect possible Pn phases — refractions from the upper mantle. Such information means that crustal thickness as well as the seismic-velocity distribution to the Moho can be determined. As a result, these data can provide important constraint for subsidence and hydrocarbon-maturation modeling. Analysis of earthquake data recorded during marine seismic surveys is ongoing and should provide new information on the interaction between anthropogenic signals (air-gun sources, vessel noise) and the natural environment. Recording earthquake and air-gun signals at fixed locations opens a completely new possibility for calibration and comparison of those signal strengths and spectral compositions. Access to the OBS fleet is open to industry, academia, and government organizations. Application to use Australia's OBS fleet is via a simple process through ANSIR ( www.ansir.org.au ).

Levy Foraging in a Dynamic Environment – Extending the Levy Search

A common task for robots is the patrolling of an unknown area with inadequate information about target locations. Under these circumstances it has been suggested that animal foraging could provide an optimal or at least sub-optimal search methodology, namely the Levy flight search. Although still in debate, it seems that predators somehow follow this search pattern when foraging, because it avoids being trapped in a local search if the food is beyond the sensory range. A Levy flight is a particular case of the random walk. Its displacements on a 2-D surface are drawn from the Pareto-Levy probability distribution, characterized by power law tails. The Levy flight search has many applications in optical material, ladars, optics, large database search, earthquake data analysis, location of DNA sites, human mobility, stock return analysis, online auctions, astronomy, ecology and biology. Almost all studies and simulations concerning the Levy flight foraging examine static or slowly moving (with respect to the forager) uniformly distributed resources. Moreover, in recent works a small swarm of underwater autonomous vehicles has been used to test the standard Levy search in the underwater environment, with good results. In this paper we extend the classical Levy foraging framework taking into consideration a moving target allocated on a 2-D surface according to a radial probability distribution and comparing its performance with the random walk search. The metric used in the numerical simulations is the detection rate. Simulations include the sensor resolution, intended as the maximum detection distance of the forager from the target. Furthermore, contrarily to the usual Levy foraging framework, we use only one target. Results show that Levy flight outperforms the random walk if the sensor detection radius is not too small or too large. We also find the Levy flight in the velocity of the center of mass model of a fish school according the Kuramoto equation, a famous model of synchronization phenomena. Finally, a discussion about the controversy concerning the innate or evolutionary origin of the Levy foraging is given.

Timeliness of Data Delivery from Q330 Systems

Research in rapid analysis of earthquake data, broadly classified as early warning (EW) or early alarm studies, has spurred interest in the capability of digital seismic data systems to provide low‐latency data. Because of the wide usage of Quanterra Q330 data systems in operational networks, published results analyzing the latency of deployed systems have considered these devices. Several years ago, studies (e.g., Allen, 2009; Brown et al. , 2011) showed that the effectiveness of EW systems was then limited by the total delay required to deliver waveform data for analysis of 4 s or more within instrumentation systems. Recent results (Kuyuk et al. , 2014), however, following the American Recovery and Reinvestment Act (ARRA)‐funded upgrade of instrumentation in California show that instrumentation‐related delays have been reduced in some operational networks to a comparatively small component, typically <10%, of the overall time required to issue EW alerts. We present a simple, robust method to measure the latency of data delivery arising in the Q330 family of data loggers, including the typical contribution to total latency of both hardwired and cellular wireless communication paths over the public internet. These latter two categories of communication represent the majority of those used in operational seismological networks. Current EW systems are capable of issuance of an alert within 8.68±3.73 s after the first P ‐wave detection for events in California (Kuyuk et al. , 2014). We show that the actual latency introduced by Q330 data loggers—including public internet latencies—is typically <1.5 s maximum, with a mean value <1 s, when communications network bandwidth is capable of sustaining continuous data delivery. The method is applicable to any data system. Measured values of data latency are consistent with in situ measurements in EW systems now operating in California. The sub‐1 s mean latency capability of Q330 systems now deployed widely in seismically active areas …

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

Abstract In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history analysis) is an established statistical method for the study of temporal “events” or, more specifically, questions regarding the temporal distribution of the occurrence of events and their dependence on covariates of the data sources. To make this method applicable in the setting of data streams, we propose an adaptive variant of a model that is closely related to the well-known Cox proportional hazard model. Adopting a sliding window approach, our method continuously updates its parameters based on the event data in the current time window. As a proof of concept, we present two case studies in which our method is used for different types of spatio-temporal data analysis, namely, the analysis of earthquake data and Twitter data. In an attempt to explain the frequency of events by the spatial location of the data source, both studies use the location as covariates of the sources.

Modelling real earthquake activity with reverse engineering based on evolutionary computation methods

Hazard assessment of dangerous natural phenomena is critical because of their evident results concerning loss of human life and property, especially in dense populated areas. Earthquakes are probably the most devastating phenomenon since their immediate and long-term consequences are severe. This study is focused on the earthquake data analysis in different regions of Greece, characterised by different seismicity levels. In specific, a novel model is proposed based on evolutionary computation methods, such as symbolic regression by genetic programming and genetic algorithms in order to elucidate preliminary hidden mathematical relations and patterns found in the seismological signals under study. Furthermore, the model is calibrated using reverse engineering and closes the loop from the data collection to initial hypothesis. In this way, the model formation is achieved. The presented simulation results qualitatively and quantitatively reveal some of the fundamental characteristics of each studied geographical region located in Greece that stem from its geodynamic properties.