A novel tree-based algorithm to discover seismic patterns in earthquake catalogs

A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.Ministerio de Ciencia y Tecnología TIN2011-28956-C00Junta de Andalucía P12-TIC-1728Instituto Ramón y Cajal (RYC) RYC-2012-1198

Earthquake Prediction

Among the countless natural disasters, earthquakes are capable to inflict vast devastation to a large number of buildings and constructions at the blink of an eye. Lack of knowledge and awareness on earthquake as well as its comeback is conspicuous and results in disaster; leading to bitter memories. Therefore, earthquake forecast has been a polemical study theme that has defied even the most intelligent of minds. In this chapter, an attempt was made to do an extensive overview in the area of the earthquake prediction as well as classifying them into the main strategies comprising short‐, immediate‐, and long‐term prediction. An example of each strategy was carried out by mentioning their corresponding approaches/algorithms, such as ΔCFS, CN, MSc, M8, ANN, FFBPANN, KNN, GRNN, RBF, and LMBP; depending on the importance of each strategy. Based on these, it was concluded that, after the Tohoku‐Oki earthquake with M9.0, the current orientation of the Headquarters for earthquake Research Promotion of MEXT in Japan declare that, their mission would be long‐term statistical forecast of seismicity. Even, it is claimed that they do not emphasize on short‐term forecasting. Besides, intermediate‐term estimations are not capable to be used for prevention of all damages and protect all human life, but they may be utilized to undertake certain affordable activities to decrease damage, losses, and modify postdisaster relief. And, despite the long‐term prediction is more concerned by researchers, there is no certain satisfactory level to content them. De facto, the made covenant of 1970 that investigators will be capable to forecast/predict ground excitations within a decade, still remains unmet

Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field

Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquakes in Indonesia

Spatio temporal data clustering is challenge task. The result of clustering data are utilized to investigate the seismic parameters. Seismic parameters are used to describe the characteristics of earthquake behavior. One of the effective technique to study multidimensional spatio temporal data is visualization. But, visualization of multidimensional data is complicated problem. Because, this analysis consists of observed data cluster and seismic parameters. In this paper, we propose a visualization system, called as IES (Indonesia Earthquake System), for cluster analysis, spatio temporal analysis, and visualize the multidimensional data of seismic parameters. We analyze the cluster analysis by using automatic clustering, that consists of get optimal number of cluster and Hierarchical K-means clustering. We explore the visual cluster and multidimensional data in low dimensional space visualization. We made experiment with observed data, that consists of seismic data around Indonesian archipelago during 2004 to 2014.Keywords: Clustering, visualization, multidimensional data, seismic parameters

Automatic Cluster-oriented Seismicity Prediction Analysis of Earthquake Data Distribution in Indonesia

Many researchers have analyzed the earthquakes to predict the earthquake period occurrences. However, they commonly faced the difficulty to project the prediction into the region adjusted to the earthquake data distribution and to provide an interpretation of the prediction for the region. This paper presents a new system for cluster-oriented seismicity prediction analysis, and semantic interpretation of the prediction result projected to the region. The system applies our automatic clustering algorithm to detect some clusters automatically depending on the earthquake data distribution and create clusters of the earthquake data for the prediction. The semantic interpretation is presented in the system to provide easier information from the seismicity prediction analysis. The system consists of four main computational functions: (1) Data acquisition and pre-processing, (2) Automatic clustering of earthquake data distribution, (3) Seismicity prediction of earthquake time period occurrence based on cluster with confidence levels of seismic event using the Guttenberg-Richter law, and (4) Region-based seismicity prediction analysis and semantic interpretation of the prediction for each cluster. For experiments, we use earthquake data series provided by the Advanced National Seismic System (ANSS) in the year 1963-2015 with the location of Indonesia. We made a series of experiments for earthquakes in Nias (2005), Yogyakarta (2006), and Padang (2009), with respectively 6.3, 7.6 and 8.7 Richter magnitude level. Our system presented the seismicity prediction analysis from each earthquake cluster and provided an easy interpretation of the prediction probability

A combined estimator using TEC and b-value for large earthquake prediction

[EN] Ionospheric anomalies have been shown to occur a few days before several large earthquakes. The published works normally address examples limited in time (a single event or few of them) or space (a particular geographic area), so that a clear method based on these anomalies which consistently yields the place and magnitude of the forthcoming earthquake, anytime and anywhere on earth, has not been presented so far. The current research is aimed at prediction of large earthquakes, that is with magnitude M-w 7 or higher. It uses as data bank all significant earthquakes occurred worldwide in the period from January 1, 2011 to December 31, 2018. The first purpose of the research is to improve the use of ionospheric anomalies in the form of TEC grids for earthquake prediction. A space-time TEC variation estimator especially designed for earthquake prediction will show the advantages with respect to the use of simple TEC values. Further, taking advantage of the well-known predictive abilities of the Gutenberg-Richter law's b-value, a combined estimator based on both TEC anomalies and b-values will be designed and shown to improve prediction performance even more.Baselga Moreno, S. (2020). A combined estimator using TEC and b-value for large earthquake prediction. Acta Geodaetica et Geophysica Hungarica. 55(1):63-82. https://doi.org/10.1007/s40328-019-00281-5S6382551Abordán A, Szabó NP (2018) Metropolis algorithm driven factor analysis for lithological characterization of shallow marine sediments. Acta Geod Geophys 53:189–199. https://doi.org/10.1007/s40328-017-0210-zAkhoondzadeh M, Saradjian MR (2011) TEC variations analysis concerning Haiti (January 12, 2010) and Samoa (September 29, 2009) earthquakes. 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Local, Regional, and Remote Seismo‐Acoustic Observations of the April 2015 VEI 4 Eruption of Calbuco Volcano, Chile

The two major explosive phases of the 22–23 April 2015 eruption of Calbuco volcano, Chile, produced powerful seismicity and infrasound. The eruption was recorded on seismo-acoustic stations out to 1,540 km and on five stations (IS02, IS08, IS09, IS27, and IS49) of the International Monitoring System (IMS) infrasound network at distances from 1,525 to 5,122 km. The remote IMS infrasound stations provide an accurate explosion chronology consistent with the regional and local seismo-acoustic data and with previous studies of lightning and plume observations. We use the IMS network to detect and locate the eruption signals using a brute-force, grid-search, cross-bearings approach. After incorporating azimuth deviation corrections from stratospheric crosswinds using 3-D ray tracing, the estimated source location is 172 km from true. This case study highlights the significant capability of the IMS infrasound network to provide automated detection, characterization, and timing estimates of global explosive volcanic activity. Augmenting the IMS with regional seismo-acoustic networks will dramatically enhance volcanic signal detection, reduce latency, and improve discrimination capability

Earthquake Nucleation Processes Across Different Scales and Settings

Extended nucleation phases of earthquakes have been regularly observed, yet the underlying mechanisms governing the initiation phase of rupture are yet to be understood in detail. Currently two end member models exist to explain earthquake nucleation: one model claiming that the nucleation phase of a small earthquake is indistinguishable from that of a large one, while the other proposes fundamental differences in the underlying process. Previous studies have been using the same seismological observations to argue for either model, leaving the need of further investigations into the nucleation behavior of earthquakes across scales and different settings. The thesis at hand contributes to the current discussion on earthquake nucleation by providing additional observational evidence for extended nucleation phases, complex rupture interaction and growth across a number of different scales and settings. Here, earthquake nucleation is investigated for three different scenarios, each with varying degrees of complexity: 1) the controlled case of induced seismicity in hydraulic stimulations of geothermal reservoirs, where rupture growth is assumed to be primarily governed by anthropogenic activity, 2) the partly-controlled setting of a geothermal field with a long history of fluid injection and production, and 3) the uncontrolled case of natural seismicity in the central Sea of Marmara, where earthquake nucleation is purely governed by the regional tectonics. First, the temporal evolution of seismicity and the growth of observed moment magnitudes for a range of past and present hydraulic stimulation projects associated with the creation of enhanced geothermal systems are analyzed. They reveal a clear linear relation between injected fluid volume/hydraulic energy and cumulative seismic moments. For most projects studied, the observations are in good agreement with existing physical models that predict a relation between injected fluid volume and maximum seismic moment of induced events. This suggests that seismicity results from a stable, pressure controlled rupture process at least for an extended injection period. Overall evolution of seismicity is independent of tectonic stress regime and is most likely governed by reservoir specific parameters, such as the preexisting structural inventory. In contrast, a few stimulations reveal unbound increase in seismic moment suggesting that for these cases evolution of seismicity is mainly controlled by stress field, the size of tectonic faults and fault connectivity. The uncertainty over whether or not a transition between behavior is likely to occur at any point during the injection is what motivates the need for a next generation monitoring and traffic-light system accounting for the possibility of unstable rupture propagation from the very beginning of injection by observing the entire seismicity evolution at high resolution for an immediate reaction in injection strategy. Furthermore, the majority of pressure-controlled stimulations shows the potential of actively controlling the size of induced earthquakes, if an injection protocol is chosen based on continuous feedback from a near-real-time seismic monitoring system. Second, moderate sized earthquakes at The Geysers geothermal field (California), where years of injection and production across hundreds of wells have led to a unique physical environment, are studied. While overall seismicity at The Geysers is generally governed by anthropogenic activities, contributions of individual wells or injection activities are hard to distinguish, thus making detailed managing of occurring magnitudes challenging. New high-resolution seismicity catalogs framing the occurrence of 20 ML > 2.5 earthquakes were created. The seismicity catalogs were developed using a matched filter algorithm, including automatic determination of P and S phase onsets and their inversion for absolute hypocenter locations with corresponding uncertainties. The selected 20 sequences sample different hypocentral depths and hydraulic conditions within the field. Seismic activity and magnitude frequency distributions displayed by the different earthquake sequences are correlated with their location within the reservoir. Sequences located in the northwestern part of the reservoir show overall increased seismic activity and low b values, while the southeastern part is dominated by decreased seismic activity and higher b values. Periods of high injection coincide with high b values and vice versa. These observations potentially reflect varying differential and mean stresses and damage of the reservoir rocks across the field. Additionally, a systematic search for seismicity localization using a multi-step cross-correlation analysis was performed. No evidence for increased correlation between the occurring seismicity and the mainshock for any of the 20 sequences could be seen, indicating that each main nucleation spot was seismically silent prior to the main rupture. However, a number of highly inter-correlated earthquakes for sequences below the reservoir and during high injection activity is observed. Under these conditions, the seismicity surrounding the future mainshock source region is more concentrated and might be evidence for a cascading nucleation process. About 50% of analyzed sequences exhibit no change in seismicity rate in response to the large main event. However, we find complex waveforms at the onset of the main earthquake, suggesting that small ruptures spontaneously grow into or trigger larger events, consistent with a cascading type nucleation. Third, the spatiotemporal evolution of seismicity during a sequence of moderate (MW4.7 and MW5.8) earthquakes occurring in September 2019 at the transition between a creeping and a locked segment of the North Anatolian Fault in the central Sea of Marmara (Turkey) was analyzed. A matched filter technique was applied to continuous waveforms from the regional network, substantially reducing the magnitude threshold for detection. Sequences of foreshocks preceding the two mainshocks are clearly seen, exhibiting different behaviors: a migration of the seismicity along the entire fault segment on the long-term and a concentration around the epicenters of the large events on the short-term. Suggesting that both seismic and aseismic slip during the foreshock sequences change the stress state on the fault, bringing it closer to failure. Furthermore, the observations also suggest that the MW4.7 event contributed to weaken the fault as part of the preparation process of the MW5.8 earthquake. Combining the results obtained from different settings, it becomes apparent that, regardless of the tectonic setting and degree of anthropogenic control over the seismicity, there is a wide range of complex nucleation behaviours not yet explained by any of the current models of earthquake nucleation. A simplistic view of earthquake nucleation as either a deterministic or a stochastic process seems inconsistent with the obtained results and fails to account for a more complex nucleation behaviour. Observations from The Geysers and the western Sea of Marmara earthquake sequence, suggest that both cascade triggering and aseismic slip can play major roles in the nucleation of moderate sized earthquakes. Both mechanisms seem to jointly contribute to fault initiation, even within the same rock volume. A separation of the two mechanisms can potentially be thought of at The Geysers, where cascade triggering seems to dominate in highly damaged parts of the reservoir, suggesting that the anthropogenic activity can at least partially influence the nucleation behavior of the occurring seismicity. This would be in agreement with the results obtained from analysis of hydraulic stimulations, where during the pressure-controlled phase of injection rupture growth is controlled by the injected fluid