Earthquake Catalog Research Articles

An updated earthquake catalogue and seismic regimes in the northwest Himalaya: Seismic periodicity associated with strong earthquakes

An updated earthquake catalogue and seismic regimes in the northwest Himalaya: Seismic periodicity associated with strong earthquakes

A many objective chimp optimization algorithm to de-cluster earthquake catalogs in space time domain

A many objective chimp optimization algorithm to de-cluster earthquake catalogs in space time domain

A New Statistical Perspective on Båth’s Law

Abstract The empirical Båth’s law states that the average magnitude difference (ΔM) between a mainshock and its strongest aftershock is ∼1.2, independent of the size of the mainshock. Although this observation can generally be explained by a scaling of aftershock productivity with mainshock magnitude in combination with a Gutenberg–Richter frequency–magnitude distribution, estimates of ΔM may be preferable because they are directly related to the most interesting information, namely the magnitudes of the main events, without relying on assumptions. However, a major challenge in calculating this value is the bias introduced by missing data points when the strongest aftershock is below the observed cut-off magnitude. Ignoring missing values leads to a systematic error because the data points removed are those with particularly large magnitude differences ΔM. The error can be minimized by restricting the statistics to mainshocks that are at least 2 magnitude units above the cutoff, but then the sample size is strongly reduced. This work provides an innovative approach for modeling ΔM by adapting methods for time-to-event data, which often suffer from incomplete observations (censoring). In doing so, we adequately account for unobserved values and estimate a fully parametric distribution of the magnitude differences ΔM for mainshocks in a global earthquake catalog. Our results suggest that magnitude differences are best modeled by the Gompertz distribution and that larger ΔM are expected at increasing depths and higher heat flows.

Optimal Network Design for Microseismic Monitoring in Urban Areas - A Case Study in Munich, Germany

Well-designed monitoring networks are crucial for obtaining precise locations, magnitudes and source parameters, both for natural and induced microearthqakes. The performance of a seismic network depends on many factors, including network geometry, signal-to-noise ratio (SNR) at the seismic station, instrumentation and sampling rate. Therefore, designing a high-quality monitoring network in an urban environment is challenging due to the high level of anthropogenic noise and dense building infrastructure, which can impose geometrical limitations and elevated construction costs for sensor siting. To address these challenges, we apply a numerical optimization approach to design a microseismic surveillance network for induced earthquakes in the metropolitan area of Munich (Germany), where several geothermal plants exploit a deep hydrothermal reservoir. First of all, we develop a detailed noise model for the city of Munich, to capture the heterogeneous noise conditions. Then, we calculate the expected location precision for a randomly chosen network geometry from the body-wave amplitudes and travel times of a synthetic earthquake catalog considering the modeled local noise level at each network station. In the next step, to find the optimum network configuration, we use a simulated annealing approach in order to minimize the error ellipsoid volume of the linearized earthquake location problem. The results indicate that a surface station network cannot reach the required location precision (0.5 km in epicentre and 2 km in source depth) and detection capability (magnitude of completeness Mc = 1.0) due to the city´s high seismic noise level. In order to reach this goal, borehole stations need to be added to increase the SNR of the microearthquake recordings, the accuracy of their body-wave arrival times and source parameters. The findings help to better quantify the seismic monitoring requirements for a save operation of deep geothermal projects in urban areas.

Enhanced crustal and intermediate seismicity in the Hindu Kush-Pamir region revealed by attentive deep learning model

The Hindu Kush-Pamir region (HKPR) is characterized by complex ongoing deformation, unique slab geometry, and intermediate seismic activity. The availability of extensive seismological data in recent decades has prompted the use of deep learning algorithms to extract valuable insights. In this study, we present a fully automated approach for augmenting earthquake catalogue within the HKPR. Our method leverages an attention mechanism-based deep learning architecture to simultaneously detect events, perform phase picking, and estimate magnitudes. We applied this model to a ten-month dataset (January 2013–October 2013) from 83 stations in the region. Utilizing a robust criterion to evaluate the model's probabilities, we associated phases at different stations and pinpointed earthquake locations in the region. Our results demonstrate a significant enhancement, revealing nearly four and a half times more earthquakes than previously documented in the International Seismological Center (ISC) catalogue. A notable portion of these newly detected events falls within the category of very low-magnitude earthquakes (<3), which were absent in the ISC catalogue. Notably, our spatiotemporal analysis reveals a concentration of crustal seismicity along poorly mapped neotectonic north and northeast-oriented faults in the western Pamir, as well as the Vakhsh Thrust System and the Darvaz Karakul Fault. These findings underscore potential sources of future seismic hazards. Furthermore, our expanded earthquake catalogue facilitates a deeper understanding of the interplay between crustal and intermediate seismic activity in the HKPR, shedding light on the deformation and active faulting resulting from Eurasian-Indian plate interactions.

Seismotectonic mapping and energy release analysis for Ongole city of Andhra Pradesh, India

An updated homogenised earthquake catalogue comprising 753 events from 1800 AD to 2021 AD for Ongole city andhra Pradesh, was compiled for a circular buffer zone of radius 500km with collector office (Lat 15.49880 N and Long 80.04970 E) as the centre. Dependent events were declustered using the Uhrhammer algorithm and the degree of completeness was computed using CUVI and Stepp’s methods. The analysis shows that around 11% of events were foreshocks and aftershocks. The degree of completeness for the magnitude range of 3.0 ≤ Mw &lt; 3.5, 3.5 ≤ Mw &lt; 4.0, 4.0 ≤ Mw &lt; 4.5, 4.5 ≤ Mw &lt; 5.0, 5.0 ≤ Mw &lt; 5.5 and Mw ≥ 5.5 was found to be 23, 56, 56, 63, 105 and 178 years based on CUVI method and 30, 30, 60, 60, 110 and 160 years as per Stepp’s method respectively. Values of the seismicity parameters a and b based on Gutenberg-Ritcher’s recurrence relationship for the study area were 4.02 and 0.83 respectively. Based on the current study, the maximum earthquake magnitude (Mmax) computed for the Ongole city is 7±0.27 on the moment magnitude scale and cumulative seismic energy release is 2.73x1016J. Finally, a seismotectonic map has been developed in ArcGIS 10.5.1 software comprising past seismicity, regional geology and faults details.

Aftershock Forecasting

Aftershocks can compound the impacts of a major earthquake, disrupting recovery efforts and potentially further damaging weakened buildings and infrastructure. Forecasts of the probability of aftershocks can therefore aid decision-making during earthquake response and recovery. Several countries issue authoritative aftershock forecasts. Most aftershock forecasts are based on simple statistical models that were first developed in the 1980s and remain the best available models. We review these statistical models and the wide-ranging research to advance aftershock forecasting through better statistical, physical, and machine-learning methods. Physics-based forecasts based on mainshock stress changes can sometimes match the statistical models in testing but do not yet outperform them. Physical models are also hampered by unsolved problems such as the mechanics of dynamic triggering and the influence of background conditions. Initial work on machine-learning forecasts shows promise, and new machine-learning earthquake catalogs provide an opportunity to advance all types of aftershock forecasts. ▪Several countries issue real-time aftershock forecasts following significant earthquakes, providing information to aid response and recovery.▪Statistical models based on past aftershocks are used to compute aftershock probability as a function of space, time, and magnitude.▪Aftershock forecasting is advancing through better statistical models, constraints on physical triggering mechanisms, and machine learning.▪Large high-resolution earthquake catalogs provide an opportunity to advance physical, statistical, and machine-learning aftershock models.

Verifying the Magnitude Dependence in Earthquake Occurrence.

The existence of magnitude dependence in earthquake triggering has been reported. Such a correlation is linked to the issue of seismic predictability and it remains under intense debate whether it is physical or is caused by incomplete data due to the missing short-term aftershocks. Working firstly with a synthetic catalog generated by a numerical model that captures most statistical features of earthquakes and then with a high-resolution earthquake catalog for the Amatrice-Norcia (2016) sequence in Italy, where for the latter case we employ the stochastic declustering method to reconstruct the family tree among seismic events and limit our analysis to events above the magnitude of completeness, we found that the hypothesis of magnitude correlation can be rejected.

Detection of hidden earthquakes after the 2011 Tohoku earthquake by automatic hypocenter determination combined with machine learning

After the 2011 Mw 9.0 Tohoku earthquake, seismicity became extremely active throughout Japan. Despite enormous efforts to detect the large number of earthquakes, microearthquakes (M < 2 inland, M < 3 offshore) were not always cataloged and many have remained undetected, making it difficult to understand the detailed seismicity after the 2011 Tohoku earthquake. We developed an automatic hypocenter determination method combined with machine learning to detect microearthquakes. Machine learning was used for phase classification with convolutional neural networks and ensemble learning to remove false detections. We detected > 920,000 earthquakes from March 2011 to February 2012, triple the number of the conventional earthquake catalog (~ 320,000). This represents a great improvement in earthquake detection, especially in and around the Tohoku region. Detailed analysis of our merged catalog more clearly revealed features such as (1) swarm migrations, (2) small foreshock activity, and (3) increased microseismicity preceding repeating earthquakes. This microseismic catalog provides a magnifying glass for understanding detailed seismicity.Graphical

Seismic hazard assessment studies based on developed deterministic and probabilistic approaches for the central-east of Iran region

PurposeThis Study aims to present the seismic hazard assessment of the earthquake-prone eastern of Iran that has become more important due to its growing economic importance. Many cities in this region have experienced life and financial losses due to major earthquakes in recent years. Thus, in this study the seismic hazard maps and curves, and site-specific spectrums were obtained by using probabilistic approaches for the region.Design/methodology/approachThe seismotectonic information, seismicity data and earthquake catalogues were gathered, main active seismic sources were identified and seismic zones were considered to cover the potential active seismic regions. The seismic model based on logic tree method used two seismic source models, two declustered catalogues, three choices for earthquake recurrence parameters and maximum considered earthquakes and four ground motion predicting (attenuation) models (GMPE).FindingsThe results showed a wide range of seismic hazards levels in the study region. The peak ground acceleration (PGAs) for 475 years returns period ranges between 0.1 g in the north-west part of the region with low seismic activity, to 0.52 g in the south-west part with high levels of seismicity. The PGAs for a 2,475-year period, also ranged from 0.12 to 0.80 g for the same regions. The computed hazard results were compared to the acceptable level of seismic hazard in the region based on Iran seismic code.Originality/valueA new probabilistic approach has been developed for obtaining seismic hazard maps and curves; these results would help engineers in design of earthquake-resistant structures.

A new method for coseismic offset detection from GPS coordinate time series

Currently, the extraction of coseismic offset signals primarily relies on earthquake catalog data to determine the occurrence time of earthquakes. This is followed by the process of differencing the average GPS coordinate time series data, with a time interval of 3 to 5 days before and after the earthquake. In the face of the huge amount of GPS coordinate time series data today, the conventional approach of relying on earthquake catalog data to assist in obtaining coseismic offset signals has become increasingly burdensome. To address this problem, we propose a new method for automatically detecting coseismic offset signals in GPS coordinate time series without an extra earthquake catalog for reference. Firstly, we pre-process the GPS coordinate time series data for filtering out stations with significant observations missing and detecting and removing outliers. Secondly, we eliminate other signals and errors in the GPS coordinate time series, such as trend and seasonal signals, leaving the coseismic offset signals as the primary signal. The resulting coordinate time series is then modeled using the first-order difference and data stacking method. The modeling method enables automatic detection of the coseismic offset signals in the GPS coordinate time series. The aforementioned method is applied to automatically detect coseismic offset signals using simulated data and the Searles Valley GPS data in California, USA. The results demonstrate the efficacy of our proposed method, successfully detecting coseismic offsets from vast amounts of GPS coordinate time series data.

Memory guided Aquila optimization algorithm with controlled search mechanism for seismicity analysis of earthquake prone regions

De-clustering the seismic catalog is one of the crucial processes in determining the probability of exceeding ground motions at particular locations. Removing dependent events, such as foreshocks and aftershocks generated from mainshocks, from an earthquake catalog is known as seismicity de-clustering. This paper presents a new approach to classify seismicity using a swarm intelligence technique called the memory-guided Aquila optimizer (MGAOA). Aquila optimization is a recently reported yet popular optimization algorithm inspired by the hunting process of Aquila. In MGAOA, the Aquila searches for the prey based on the personal best history stored in the memory element, which helps MGAOA to converge faster and maintain the balance between exploration and exploitation during local and global search procedures. The search-controlled parameter is further integrated with Aquila optimization to enhance the exploitation phase of the algorithm. The effectiveness of the proposed MGAO is tested on twenty-three classical test functions and ten benchmark test suites of IEEE CEC 2021. The results show that the proposed MGAOA outperforms other algorithms regarding performance metrics and statistical tests. The MGAOA is applied to solve the problem of seismicity de-clustering using the nearest neighbor distance (NND). The NND is a widely used parameter for seismicity and consists of space–time-magnitude information. The MGAOA-based de-clustering model is used to identify the seismic clusters in highly active earthquake-prone Himalayas, California, Japan, and Indonesia regions. The results obtained by the proposed model are compared with state-of-the-art de-clustering techniques and other memory-guided swarm intelligence techniques. The obtained results in terms of aftershocks and backgrounds are evaluated with the help of cumulative plot, λ-plot, space–time plot, inter-event time versus inter-event distance plot, and statistical parameters like coefficient of variance, which show that the proposed model efficiently detects the seismic clusters, and outperforms other benchmark de-clustering techniques.

Trade space for time for inspecting an earthquake cycle by modern seismological observation: The central-southern part of the Sichuan-Yunnan rhombic block

The central-southern part of the eastern border of the Sichuan-Yunnan rhombic block provides the research strategy of ‘trade space for time’ with an interesting fault system, where the segments have similar focal mechanisms and cover almost continuous spectra of elapse rates. We experiment to study the seismological characteristics of different segments with different elapse rates. We employed the de-clustered earthquake catalog for the calculation of b values for each segment. The analysis revealed that different segments have similar b values, which implies that, although different segments have different periods of earthquake recurrence, the 'natural time' for the whole fault system elapses with a homogeneous pace. We extended the earthquake potential score (EPS) for nowcasting earthquakes to a quasi-EPS (qEPS). It is found that qEPS increases with the increase of elapse rates, albeit for those fault segments whose elapse rates have exceeded 1, qEPS may better reflect the seismic hazard.

Coherent Spatial Variations in the Productivity of Earthquake Sequences in California and Nevada

Abstract Earthquakes are clustered in space and time, with individual sequences composed of events linked by stress transfer and triggering mechanisms. On a global scale, variations in the productivity of earthquake sequences—a normalized measure of the number of triggered events—have been observed and associated with regional variations in tectonic setting. Here, we focus on resolving systematic variations in the productivity of crustal earthquake sequences in California and Nevada—the two most seismically active states in the western United States. We apply a well-tested nearest-neighbor algorithm to automatically extract earthquake sequence statistics from a unified 40 yr compilation of regional earthquake catalogs that is complete to M ∼ 2.5. We then compare earthquake sequence productivity to geophysical parameters that may influence earthquake processes, including heat flow, temperature at seismogenic depth, complexity of quaternary faulting, geodetic strain rates, depth to crystalline basement, and faulting style. We observe coherent spatial variations in sequence productivity, with higher values in the Walker Lane of eastern California and Nevada than along the San Andreas fault system in western California. The results illuminate significant correlations between productivity and heat flow, temperature, and faulting that contribute to the understanding and ability to forecast crustal earthquake sequences in the area.

A‐Positive: A Robust Estimator of the Earthquake Rate in Incomplete or Saturated Catalogs

AbstractDetection thresholds in earthquake catalogs frequently change in time due to station coverage improvements and network saturation effects during active periods such as mainshock‐aftershock cascades. This presents a challenge to seismicity‐rate estimation; there is a tradeoff between using as low a minimum magnitude as possible to maximize data while not undercounting the rate due to catalog incompleteness. Here we present a simple method, “a‐positive,” which makes use of differential statistics to robustly estimate the seismicity rate in catalogs with time‐varying detection thresholds. We demonstrate the effectiveness of this method for a centuries‐long, hybrid earthquake catalog with both historical and instrumentally‐detected earthquakes in the Central and Eastern U.S., as well as for the 2019 Ridgecrest aftershock sequence in California, which has rapid changes in completeness due to network saturation. We find that the a‐positive method leads to more precise and less biased estimates of seismicity rate than traditional methods. In addition, with our improved estimate of earthquake rate early in the aftershock cascade, we find no evidence of rate‐saturation at short times from the mainshock; that is, the Omori c‐value is not distinguishable from zero.

SimplETAS: A Benchmark Earthquake Forecasting Model Suitable for Operational Purposes and Seismic Hazard Analysis

Abstract The epidemic-type aftershock sequence (ETAS) model is the most effective mathematical description of the short-term space–time earthquake clustering. However, the use of such a model is sometimes hampered by the difficulty in estimating the high number of its unknown correlated parameters. Moreover, the most recent ETAS formulations introduce the space–time variability of some parameters that makes their estimation even more arduous. Here, we investigate the model in an opposite perspective, looking for the simplest ETAS parameterization that can satisfactorily describes the earthquake clustering in crustal tectonic regions; we named this model simplETAS. We show that simplETAS calibrated with the Italian seismicity of the last decades adequately describes the space–time occurrence of the out-of-sample largest earthquakes in the instrumental and historical catalog, confirming the validity of the assumptions made to build the model. Owing to its simplicity, simplETAS is easily applicable in most regions, and it has some important properties that are worth being remarked. First, simplETAS can be used as a benchmark model to assess the relative predictive skill of more complex earthquake forecasts. Second, it may be used for operational earthquake forecasting purposes in regions with limited earthquake catalogs. Third, it provides a straightforward, flexible, and effective approach to generate synthetic earthquake catalogs of variable length to be implemented in seismic hazard and risk analysis, overcoming all the declustering-related problems and the controversial Poisson assumption.

Sharpen data-driven prediction rules of individual large earthquakes with aid of Fourier and Gauss

Predicting individual large earthquakes (EQs)’ locations, magnitudes, and timing remains unreachable. The author’s prior study shows that individual large EQs have unique signatures obtained from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs (Mge 6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis.

Romanian Earthquakes Database. Part I: 984 - 1899

"The main purpose of this study is to create a database that correlates the data from the current catalogue of Romanian earthquakes (ROMPLUS) with the information from other available catalogues, research studies for the events produced between 984 and 1899, historical documents, and mentions in archives or historical papers. The database will contain, in addition to the ROMPLUS available information, the type of event (tectonic or anthropic) and the references attached to each event. In the second step of our analysis (events recorded after 1900), the database will be updated and connected with the catalogue of the earthquake mechanisms. "

Predicting the Future Performance of the Planned Seismic Network in Chinese Mainland

Abstract The China Earthquake Administration has currently launched an ambitious nationwide seismicity monitoring network project that will increase the number of stations from ∼950 to 2000 for the broadband seismic stations used to compile the earthquake catalog. The new network is planned to go online by the end of 2023. For more than half of Chinese mainland, the interstation distance of the broadband seismic network will soon be smaller than 100 km, for 27% smaller than 50 km, and for 6% smaller than 25 km. Of all possible ways to characterize the higher-resolution monitoring of the frequent smaller earthquakes expected inside Chinese mainland, the completeness magnitude (Mc) remains one of the most commonly used. Using the prior model of the Bayesian magnitude of completeness method calibrated on the Chinese earthquake catalog from 1 January 2009 to 26 June 2022, we predict the spatial distribution of Mc for the new network based on the planned network configuration. If almost the entire Chinese mainland is at present covered down to Mc=3.3, this threshold will fall to Mc=2.9 in the near future. This means approximately two times more earthquakes will be recorded in the complete catalog available for statistical analysis per year (for a = 6.77 and b = 0.80 in the Gutenberg–Richter law log10N=a−b·M, in which N represents the number of events of magnitude larger than or equal to M and M≥Mc). Based on the observation that abnormal seismicity as precursors are most likely to be observed at least at three units below the mainshock magnitude, and assuming earthquakes to be potentially damaging at M ≥ 5, the new seismic network shall achieve the goal of 76% coverage for optimal seismic-based earthquake prediction research.

Assessing earthquake rates and b-value given spatiotemporal variation in catalog completeness: Application to Atlantic Canada

Spatiotemporal variations in the magnitude of completeness Mc make it challenging to confidently assess seismic hazard or even to simply compare earthquake rates between regions. In this study, we introduce new techniques to correct for heterogeneous Mc in a treatment of the eastern and Atlantic Canada earthquake catalog (1985--2022). We first introduce new methodology to predict Mc(x,t) based on the distribution of seismometers. Second, we introduce a modified maximum-likelihood estimator (MLE) for b (the b-value) that accounts for spatiotemporal Mc variation, allowing the inclusion of more earthquakes. Third, we compute the ratio of detected/predicted M&gt;1 earthquakes as a function of Mc and apply it to create a calibrated M&gt;1 event-rate map. The resulting map has advantages over a moment-rate map, which is effectively sensitive only to the very largest earthquakes in the dataset. The new MLE results in a modestly more precise b when applied to the Charlevoix Seismic Zone, and a substantial increase in precision when applied to the full Atlantic Canada region. It may prove useful in future hazard assessments, particularly of regions with highly heterogeneous Mc and relatively sparse catalogs.