Extending the central concept of recurrence times for a point process to recurrent events in space-time allows us to characterize seismicity as a record breaking process using only spatiotemporal relations among events. Linking record breaking events with edges between nodes in a graph generates a complex dynamical network isolated from any length, time or magnitude scales set by the observer. For Southern California, the network of recurrences reveals new statistical features of seismicity with robust scaling laws. The rupture length and its scaling with magnitude emerges as a generic measure for distance between recurrent events. Further, the relative separations for subsequent records in space (or time) form a hierarchy with unexpected scaling properties.
This paper presents an explanation of a possible mechanism underlying the shape of the universal curve of Scaling Law for Earthquake Recurrence Time Distributions. The presented simple stochastic cellular automaton model is reproducing the gamma distribution fit with the proper value of the parameter $γ$ characterizing Earth's seismicity and also imitates a deviation from the fit at the short interevent times, as observed in real data. Thus the model suggests an explanation of the universal pattern of rescaled Earthquake Recurrence Time Distributions in terms of combinatorial rules for accumul…
We investigate the sequence of great earthquakes over the past century. To examine whether the earthquake record includes temporal clustering, we identify aftershocks and remove those from the record. We focus on the recurrence time, defined as the time between two consecutive earthquakes. We study the variance in the recurrence time and the maximal recurrence time. Using these quantities, we compare the earthquake record with sequences of random events, generated by numerical simulations, while systematically varying the minimal earthquake magnitude Mmin. Our analysis shows that the earthquak…
We study the statistics of the recurrence times between earthquakes above a certain magnitude M$ in California. We find that the distribution of the recurrence times strongly depends on the previous recurrence time $τ_0$. As a consequence, the conditional mean recurrence time $\hat τ(τ_0)$ between two events increases monotonically with $τ_0$. For $τ_0$ well below the average recurrence time $\ovτ, \hatτ(τ_0)$ is smaller than $\ovτ$, while for $τ_0>\ovτ$, $\hatτ(τ_0)$ is greater than $\ovτ$. Also the mean residual time until the next earthquake does not depend only on the elapsed time, but als…
We quantify the correlation between earthquakes and use the same to distinguish between relevant causally connected earthquakes. Our correlation metric is a variation on the one introduced by Baiesi and Paczuski (2004). A network of earthquakes is constructed, which is time ordered and with links between the more correlated ones. Data pertaining to the California region has been used in the study. Recurrences to earthquakes are identified employing correlation thresholds to demarcate the most meaningful ones in each cluster. The distribution of recurrence lengths and recurrence times are analy…
We quantify the correlation between earthquakes and use the same to distinguish between relevant causally connected earthquakes. Our correlation metric is a variation on the one introduced by Baiesi and Paczuski (2004). A network of earthquakes is constructed, which is time ordered and with links between the more correlated ones. Recurrences to earthquakes are identified employing correlation thresholds to demarcate the most meaningful ones in each cluster. Data pertaining to three different seismic regions, viz. California, Japan and Himalayas, are comparatively analyzed using such a network …
We develop an efficient numerical scheme to solve accurately the set of nonlinear integral equations derived previously in (Saichev and Sornette, 2007), which describes the distribution of inter-event times in the framework of a general model of earthquake clustering with long memory. Detailed comparisons between the linear and nonlinear versions of the theory and direct synthetic catalogs show that the nonlinear theory provides an excellent fit to the synthetic catalogs, while there are significant biases resulting from the use of the linear approximation. We then address the suggestions prop…
Characteristic versus critical features of earthquakes are studied on the basis of the Olami-Feder-Christensen model. It is found that the local recurrence-time distribution exhibits a sharp $δ$-function-like peak corresponding to rhythmic recurrence of events with a fixed ``period'' uniquely determined by the transmission parameter of the model, together with a power-law-like tail corresponding to scale-free recurrence of events. The model exhibits phenomena closely resembling the asperity known in seismology.
The statistics of recurrence times in broad areas have been reported to obey universal scaling laws, both for single homogeneous regions (Corral, 2003) and when averaged over multiple regions (Bak et al.,2002). These unified scaling laws are characterized by intermediate power law asymptotics. On the other hand, Molchan (2005) has presented a mathematical proof that, if such a universal law exists, it is necessarily an exponential, in obvious contradiction with the data. First, we generalize Molchan's argument to show that an approximate unified law can be found which is compatible with the em…
Non-randomness of Japan megaquakes implied by stress recovery and accumulation
University of Shizuoka（静冈大学）；Department of Physics and Astronomy, University of California, Davis（加州大学戴维斯分校物理与天文系）；Center for Integrated Research and Education of Natural Hazards, Shizuoka University（静冈大学自然灾害综合研究教育研究中心）；Institute of Statistical Mathematics（统计数学研究所）；Yokohama Institute for Earth Sciences, Japan Agency for Marine-Earth Science and Technology（日本海洋地球科学技术厅横滨地球科学研究所）
GPS and strong-motion sensors are broadly used for the monitoring of structural health and Earth surface motions, focusing on response of structures, earthquake characterization and rupture modeling. Most studies have shown differences between the two systems at very long periods (e.g. >100sec). The aim of this study is the assessment of the compatibility of GPS and strong-motion records by comparing the consistency in the frequency domain and by comparing their respective displacement waveforms for several frequency bands. For this purpose, GPS and strong-motion records of 23 collocated sites…
It has been observed that the earthquake events possess short-term memory, i.e. that events occurring in a particular location are dependent on the short history of that location. We conduct an analysis to see whether real-time earthquake data also possess long-term memory and, if so, whether such autocorrelations depend on the size of earthquakes within close spatiotemporal proximity. We analyze the seismic waveform database recorded by 64 stations in Japan, including the 2011 "Great East Japan Earthquake", one of the five most powerful earthquakes ever recorded which resulted in a tsunami an…
Long-term sedimentary earthquake records along the northern branch of the North Anatolian Fault in the Sea of Marmara (NW Türkiye)
ITÜ - Istanbul Technical University（伊斯坦布尔理工大学）；Maden Fakültesi = Faculty of mines [Istanbul]（伊斯坦布尔矿业学院）；CEREGE - Centre Européen de Recherche et d'Enseignement des Géosciences de l'Environnement（欧洲地球与环境科学研究与教学中心）；ISMAR - Istituto di Science Marine（海洋科学研究所）；ISMAR - Istituto di Scienze Marine [Bologna]（博洛尼亚海洋科学研究所）；LIENSs - LIttoral ENvironnement et Sociétés（海岸环境与社会实验室）；LDEO - Lamont-Doherty Earth Observatory（拉蒙特-多尔蒂地球观测站）
Statistical properties of earthquakes are studied both by the analysis of real earthquake catalog of Japan and by numerical computer simulations of the spring-block model in both one and two dimensions. Particular attention is paid to the spatiotemporal correlations of earthquakes, e.g., the recurrence-time distribution or the time evolution before and after the mainshock of seismic distribution functions, including the magnitude distribution and the spatial seismic distribution. Certain eminent features of the spatiotemporal correlations, e.g., foreshocks, aftershocks, swarms and doughnut-lik…
The Mw 7.8 Kahramanmaraş Earthquake was larger and more destructive than what had been expected for the tectonic setting in Southeastern Turkey. By using near-field records we provide evidence for early supershear transition on the splay fault that hosted the nucleation and early propagation of the first rupture that eventually transitioned into the East Anatolian fault. The two stations located furthest from the epicenter show a larger fault parallel particle velocity component relative to the fault normal particle velocity component; a unique signature of supershear ruptures that has been id…
We propose a new approach for generating synthetic earthquake catalogues based on the physics of soft glasses. The continuum approach produces yield-stress materials based on Lattice-Boltzmann simulations. We show that, if the material is stimulated below yield stress, plastic events occur, which have strong similarities with seismic events. Based on a suitable definition of displacement in the continuum, we show that the plastic events obey a Gutenberg-Richter law with exponents similar to those for real earthquakes. We further find that average acceleration, energy release, stress drop and r…
We study statistical properties of the number of large earthquakes over the past century. We analyze the cumulative distribution of the number of earthquakes with magnitude larger than threshold M in time interval T, and quantify the statistical significance of these results by simulating a large number of synthetic random catalogs. We find that in general, the earthquake record cannot be distinguished from a process that is random in time. This conclusion holds whether aftershocks are removed or not, except at magnitudes below M = 7.3. At long time intervals (T = 2-5 years), we find that stat…
In this paper, we analyze several instrumental records of temperatures at different locations by using new techniques originally developed for the analysis of extreme values of dynamical systems. We show that they have the same recurrence time statistics as a chaotic dynamical system perturbed with dynamical noise and by instrument errors. The technique provides a criterion to discriminate whether the recurrence of a certain temperature belongs to the normal variability or can be considered as a genuine extreme event with respect to a specific timescale fixed as parameter. The method gives a s…
This study describes a deep convolutional neural network (CNN) based technique for the prediction of intensity measurements (IMs) of ground shaking. The input data to the CNN model consists of multistation 3C broadband and accelerometric waveforms recorded during the 2016 Central Italy earthquake sequence for M $\ge$ 3.0. We find that the CNN is capable of predicting accurately the IMs at stations far from the epicenter and that have not yet recorded the maximum ground shaking when using a 10 s window starting at the earthquake origin time. The CNN IM predictions do not require previous knowle…
The effect of transformations analogous to those of the real-space renormalization group are analyzed for the temporal occurrence of earthquakes. The distribution of recurrence times turns out to be invariant under such transformations, for which the role of the correlations between the magnitudes and the recurrence times are fundamental. A general form for the distribution is derived imposing only the self-similarity of the process, which also yields a scaling relation between the Gutenberg-Richter b-value, the exponent characterizing the correlations, and the recurrence-time exponent. This a…
Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understand the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect …
The analysis of strong motion recordings in structures is crucial to understand the damaging process during earthquakes. A very precise time-frequency representation, the reassigned smoothed pseudo-Wigner-Ville method, allowed us to follow the variation of the Millikan Library (California) and the Grenoble City Hall building (France) resonance frequencies during earthquakes. Under strong motions, a quick frequency drop, attributed to damage of the soil-structure system, followed by a slower increase is found. However, in the case of weak earthquakes, we show that frequency variations come from…
Method for restoring the orientation of ocean bottom seismometers using distant earthquakes records
Lomonosov Moscow State University（莫斯科国立罗蒙诺索夫大学）；Institute of Marine Geology and Geophysics, Far Eastern Branch, Russian Academy of Science（俄罗斯科学院远东分院海洋地质与地球物理研究所）
We present a deep learning method for single-station earthquake location, which we approach as a regression problem using two separate Bayesian neural networks. We use a multi-task temporal-convolutional neural network to learn epicentral distance and P travel time from 1-minute seismograms. The network estimates epicentral distance and P travel time with absolute mean errors of 0.23 km and 0.03 s respectively, along with their epistemic and aleatory uncertainties. We design a separate multi-input network using standard convolutional layers to estimate the back-azimuth angle, and its epistemic…
We propose a method to search for signs of causal structure in spatiotemporal data making minimal a priori assumptions about the underlying dynamics. To this end, we generalize the elementary concept of recurrence for a point process in time to recurrent events in space and time. An event is defined to be a recurrence of any previous event if it is closer to it in space than all the intervening events. As such, each sequence of recurrences for a given event is a record breaking process. This definition provides a strictly data driven technique to search for structure. Defining events to be nod…
We propose a new type of earthquake precursor based on the analysis of correlation dynamics between geophysical signals of different nature. The precursor is found using a two-parameter cross-correlation function introduced within the framework of flicker-noise spectroscopy, a general statistical physics approach to the analysis of time series. We consider an example of cross-correlation analysis for water salinity time series, an integral characteristic of the chemical composition of groundwater, and geoacoustic emissions recorded at the G-1 borehole on the Kamchatka peninsula in the time fra…
In this brief report we present evidence for the dynamics of the earthquake (EQ) preparation processes that led to two strong events (Mw>6) that took place in Durrës (Albania) and Chania (West-Crete, Greece) on 26 and 27-11-2019, respectively. Specifically, MHz fracto-electromagnetic emissions (EME) recorded by our telemetric stations presented critical fluctuations prior to each one of these EQs. Subsequently, the EME possibly related to each one of these EQs evolved differently towards the occurrence of the EQ event: the ones possibly related to Chania EQ departed from critical state accordi…
Machine learning regression can predict macroscopic fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. Here we show that a similar approach is successful using event catalogs derived from the continuous data. Our methods are applicable to catalogs of arbitrary scale and magnitude of completeness. We investigate how machine learning regression from an event catalog of laboratory earthquakes performs as a function of the catalog magnitude of completeness. We find that strong model performance requires a sufficiently low…
Prediction of the Kamchatka July 29, 2025, earthquake by the evolution of low-magnitude seismicity recovered using waveform cross-correlation at IMS seismic arrays
More than 30 minutes before the earthquake event in Florina, Greece at 21:43:47(UTC) on 9th January 2022 an enhanced Eötvös torsion balance registered unidentified signals in the Jánossy Underground Research Laboratory in Budapest, Hungary. These signals were not visible on the seismograms, although seismic noises are continuously recorded by a side-by-side broadband seismometer. Moreover, seismological stations did not detect anything unusual, they presented a negative confirmation of the events. Our observation suggests that torsion balances might effectively detect precursory earthquake sig…
A megathrust subduction earthquake (Mw7.8) struck the coast of Ecuador on April 16th, 2016 at 23h58 UTC. This earthquake is one of the best-recorded megathrust events up to date. Besides the mainshock, two large aftershocks have been recorded on May 18th, 2016, at 7h57 (Mw 6.7) and 16h46 (Mw6.9). These data make a significant contribution for understanding the attenuation of ground motions in Ecuador. Peak ground accelerations and spectral accelerations are compared with four ground-motion prediction equations developed for interface earthquakes, the global Abrahamson et al. (2016) model, the …
Statistical properties of the one-dimensional spring-block (Burridge-Knopoff) model of earthquakes obeying the rate and state dependent friction law are studied by extensive computer simulations. The quantities computed include the magnitude distribution, the rupture-length distribution, the mainshock recurrence-time distribution, the seismic time correlations before and after the mainshock, the mean slip amount, and the mean stress drop at the mainshock, etc. Events of the model can be classified into two distinct categories. One tends to be unilateral with its epicenter located at the rim of…
We demonstrate the efficacy of a Bayesian statistical inversion framework for reconstructing the likely characteristics of large pre-instrumentation earthquakes from historical records of tsunami observations. Our framework is designed and implemented for the estimation of the location and magnitude of seismic events from anecdotal accounts of tsunamis including shoreline wave arrival times, heights, and inundation lengths over a variety of spatially separated observation locations. As an initial test case we use our framework to reconstruct the great 1852 earthquake and tsunami of eastern Ind…
Earthquake hypocenters form the basis for a wide array of seismological analyses. Pick-based earthquake location workflows rely on the accuracy of phase pickers and may be biased when dealing with complex earthquake sequences in heterogeneous media. Time-reversal imaging of passive seismic sources with the cross-correlation imaging condition has potential for earthquake location with high accuracy and high resolution, but carries a large computational cost. Here we present an alternative deep-learning approach for earthquake location by combining the benefits of neural operators for wave propa…
Earthquakes are major hazards to humans, buildings and infrastructure. Early warning methods aim to provide advance notice of incoming strong shaking to enable preventive action and mitigate seismic risk. Their usefulness depends on accuracy, the relation between true, missed and false alerts, and timeliness, the time between a warning and the arrival of strong shaking. Current approaches suffer from apparent aleatoric uncertainties due to simplified modelling or short warning times. Here we propose a novel early warning method, the deep-learning based transformer earthquake alerting model (TE…
Time-Resolved Surface-Fault Displacement During the 2026 Kumamoto Earthquake From Near-Fault Video
Kyoto University（京都大学）；National Institute for Earth Physics（罗马尼亚地球物理研究所）；Tohoku University（东北大学）；University of Tsukuba（筑波大学）；University of Shizuoka（静冈大学）；Kyoto Institute of Technology（京都工艺纤维大学）
Applying Detrended Fluctuation Analysis (DFA) to the geomagnetic data recorded at three measuring stations in Japan, Rong et al. in 2012 reported that anomalous magnetic field variations were identified well before the occurrence of the disastrous Tohoku $M_w$9.0 earthquake that occurred on 11 March 2011 in Japan exhibiting increased "non-uniform" scaling behavior. Here, we provide an explanation for the appearance of this increase of "non-uniform" scaling on the following grounds: These magnetic field variations are the ones that accompany the electric field variations termed Seismic Electric…
Earthquake monitoring workflows are designed to detect earthquake signals and to determine source characteristics from continuous waveform data. Recent developments in deep learning seismology have been used to improve tasks within earthquake monitoring workflows that allow the fast and accurate detection of up to orders of magnitude more small events than are present in conventional catalogs. To facilitate the application of machine-learning algorithms to large-volume seismic records, we developed a cloud-based earthquake monitoring workflow, QuakeFlow, that applies multiple processing steps …
In this study, we have used point-source approximation with the premise that it is compatible with the rupture process of the predominant faulting geometry caused by the initiation of Shonbe seismic sequence. First, all recorded earthquakes during six months after the main shock, 2013 April 9th, in the vicinity of khaki anticline near Shonbe, are relocated with the probabilistic nonlinear method based on Bayesian inference. The synthetic test is done, with the exact distribution of real stations on hand. Thus, the proper value for the parameter that can explicitly affect the error results is c…
Extraction of information in the form of oscillations from noisy data of natural phenomena such as sounds, earthquakes, ionospheric and brain activity, and various emissions from cosmic objects is extremely difficult. As a method for finding periodicity in such challenging data sets, the 2D Hybrid approach, which employs wavelets, is presented. Our technique produces a wavelet transform correlation intensity contour map for two (or one) time series on a period plane defined by two independent period axes. Notably, by spreading peaks across the second dimension, our method improves apparent res…
In Earthquake Early Warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive signals are mere nuisance signals. One of the most fundamental - and difficult - tasks in EEW is to rapidly and reliably discriminate real local earthquake signals from all other signals. This discrimination is necessarily based on very little information, typically a few seconds worth of seismic waveforms from a small number of stations. As a result, current EEW systems struggle to avoid discrimination errors, and s…
University of British Columbia（不列颠哥伦比亚大学）；GD Goenka University（GD戈恩卡大学）
Earthquake prediction has been a challenging research area for many decades, where the future occurrence of this highly uncertain calamity is predicted. In this paper, several parametric and non-parametric features were calculated, where the non-parametric features were calculated using the parametric features. $8$ seismic features were calculated using Gutenberg-Richter law, the total recurrence, and the seismic energy release. Additionally, criterions such as Maximum Relevance and Maximum Redundancy were applied to choose the pertinent features. These features along with others were used as …
After a large earthquake, the likelihood of successive strong aftershocks needs to be estimated. Exploiting similarities with critical phenomena, we introduce a scaling law for the decay in time following a main shock of the expected number of aftershocks greater than a certain magnitude. Empirical results that support our scaling hypothesis are obtained from analyzing the record of earthquakes in California. The proposed form unifies the well-known Omori and Gutenberg-Richter laws of seismicity, together with other phenomenological observations. Our results substantially modify presently empl…
We assess the level of cross correlation between P-waves generated by earthquakes in the Atlantic Ocean and measured by 22 array stations of the International Monitoring System (IMS). There are 931 events with 6,411 arrivals in 2011 and 2012. Station TORD was the most sensitive and detected 868 from 931 events. We constructed several 931 by 931 matrices of cross correlation coefficients (CCs) for individual stations and also for average and cumulative CCs. These matrices characterize the detection performance of the involved stations and the IMS. Sixty earthquakes located in the northern hemis…
The 11/5/2011 Lorca, Spain earthquake (Mw5.2) and related seismicity produced extensive damage in the town of Lorca and vicinity. During these earthquakes, evidence of rotations and permanent deformations in structures were observed. To analyze these aspects and study the source properties from the near field, the displacement time histories were obtained including the static component at Lorca station. Displacement time histories were computed by an appropriate double time integration procedure of accelerograms. Using these data, the foreshock and mainshock slip distributions were calculated …
Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. The recorded seismic signals by DAS have several distinct characteristics, such as unknown coupling effects, strong anthropogenic noise, and ultra-dense spatial sampling. These aspects differ from conventional seismic data recorded by seismic networks, making it challenging to utilize DAS at present for seismic monitoring. New data analysis algorithms are needed to extract useful information from DAS data. Previous studies on conventional seismic data demonstrated that deep learning m…
It is well accepted that, at the global scale, the Gutenberg-Richter (GR) law describing the distribution of earthquake magnitude or seismic moment has to be modified at the tail to properly account for the most extreme events. It is debated, though, how much additional time of earthquake recording will be necessary to properly constrain this tail. Using the global CMT catalog, we study how three modifications of the GR law that incorporate a corner-value parameter are compatible with the size of the largest observed earthquake in a given time window. Current data lead to a rather large range …
Effects of stochastic and natural seismic noise on the performance of waveform cross-correlation used to recover low-magnitude seismicity prior to the July 29, 2025, Kamchatka earthquake
A recently proposed method of constructing seismic networks from 'record breaking events' from the earthquake catalog of California (Phy. Rev. E, 77 6,066104, 2008) was successfull in establishing causal features to seismicity and arrive at estimates for rupture length and its scaling with magnitude. The results of our implementation of this procedure on the earthquake catalog of Japan establishes the robustness of the procedure. Additionally, we find that the temporal distributions are able to detect heterogeneties in the seismicity of the region.
