Catalog Completeness Research Articles

Catalog Replenishment Based on Statistical Method for Strong Earthquakes in the Triple Junction Area, Eastern Tibet

Abstract The triple junction area, bordered by the Longmenshan, Xianshuihe, and Anninghe faults, is located at the southeastern corner of Songpan–Ganzi block, northern Tibet. In this area, a series of strong earthquakes occurred, including the 2008 Mw 7.9 Wenchuan, 2013 Mw 6.6 Lushan, 2014 Mw 5.9 Kangding, 2022 Mw 5.8 Lushan, and 2022 Mw 6.6 Luding. In addition, multiple hot springs with different temperatures are distributed along the faults in the same area. This study tries to figure out: (1) the necessity of catalog replenishment based on statistical method; (2) whether the special tectonic environment characterized by hot springs influences the evolution of aftershock sequences. A complete aftershock catalog is a fundamental data set for exploring these issues. However, the incompleteness of the catalog limits our understanding of aftershock evolution. Here, we applied a statistical catalog replenishment method to the five aftershock sequences, with the results showing that the replenishment method is helpful to improve the catalog completeness by producing more reasonable Omori law parameters. In addition, both the catalogs obtained from matched filter technique and the replenishment method have advantages and limitations, suggesting that combining two versions of catalog in aftershock evolution study is helpful. Finally, we revealed a negative correlation between the p-value of Omori law parameters and temperatures of hot springs, which may be useful to evaluate potential aftershock risks.

Earthquake Magnitude Correlations Expose Short-Term Catalog Incompleteness

Abstract The independence of earthquake magnitudes is a fundamental assumption and limitation in earthquake forecasting. To assess its validity, we examine correlations between the magnitude of successive earthquakes. We first investigate the 2019 Ridgecrest foreshock sequence and find a significant magnitude correlation as well as an unusually low Gutenberg–Richter b-value (≈0.7 using moment magnitudes). We demonstrate that these anomalous features are not indicative of a precursory phase, but a consequence of short-term incompleteness (STI) that is not detected by conventional methods to estimate catalog completeness. Synthetic simulations of this sequence support this explanation: imposing STI leads to significant magnitude correlation and biased b-value estimates. Expanding our investigation to seismicity across southern California reveals pervasive magnitude correlation due to STI, not limited to sequences with large earthquakes. Our findings suggest that magnitude correlation is the most effective indicator of STI, rather than a characteristic of the underlying earthquake-generating process.

Forecasting future earthquakes with deep neural networks: application to California

SUMMARY We use the spatial map of the logarithm of past estimated released earthquake energies as input of fully convolutional networks (FCN) to forecast future earthquakes. This model is applied to California and compared with an elaborated version of the epidemic type aftershock sequence (ETAS) model. Our long-term earthquake forecast simulations show that the FCN model is close to the ETAS model in forecasting earthquakes with $M \ge 3.0,\,\,4.0,\,\,{\rm{and\,\,}}5.0$ according to the Molchan diagram. Moreover, training and implementing the FCN model is 2000–4000 times faster than calibrating the ETAS model and generating its probabilistic forecasts. The FCN model is straightforward in terms of its neural network structure and feature engineering. It does not require extensive knowledge of statistical seismology or the analysis of earthquake catalogue completeness. Using the earthquake catalogue with $M \ge 0$ as FCN input can enhance the model's performance in some time–magnitude forecasting windows.

Precision Seismicity of the San Andreas Fault System in Central California: Insights into Active Tectonics and Earthquake Physics

Since the late 1960s, seismicity of the San Andreas Fault system in central California has been under intense observation by U. S. Geological Survey’s Northern California Seismic Network (NCSN). Designed to provide accurate focal depth control and a low magnitude of catalog completeness, the network maps fault, both large and small across the full breadth of this 400-kilometer-long portion of the plate boundary, including many that are known only from their seismicity. Beginning in 1984, with the advent of routine digital recording, new opportunities were created to significantly enhance the spatial resolution of seismicity. This led to the co-evolution of method that take full advantage of digital waveforms and new insights into the nature of seismicity and faults. Collectively referred to as precision seismicity, discoveries included repeating earthquakes and highly organized and stable distributions of seismicity on major faults. Long term observation of seismicity before and after the six largest earthquakes over the last half-century underscore the outstanding challenges for earthquake predictability and theories of earthquake nucleation.

Revisiting Stepp’s method for the completeness of regional seismic catalogues

Revisiting Stepp’s method for the completeness of regional seismic catalogues

Automated Detection of Volcanic Seismicity Using Network Covariance and Image Processing

Abstract Seismicity at restless volcanoes commonly features a variety of signal types reflecting both volcanotectonic and fluid-driven source processes. However, traditional catalogs of seismicity are often incomplete, especially concerning events with emergent onsets such as those driven by the dynamics of magmatic and hydrothermal fluids. The detection of all discrete events and continuous seismic tremors, regardless of the underlying source processes, would therefore improve the ability of monitoring agencies to forecast eruptions and mitigate their associated hazards. We present a workflow for generalized detection of seismic events based on the network covariance matrix (Seydoux et al., 2016). Our contributions enable the method to simultaneously detect continuous and short-duration (<∼10 s) events, provide information about the frequency content of the signals, and to refine the initial detection times by an order of magnitude (from window lengths of 75 to 7.5 s). We test the workflow on a 15-month record of seismicity with 23 stations at Mammoth Mountain, California (July 2012–October 2013) and detect 62% of long-period events and 94% of volcanotectonic events in the existing Northern California Earthquake Data Center catalog. In addition, ∼3000 events are not included in the catalog, and thousands of tremor signals are found. The method is suitable for near-real-time analysis of continuous waveforms and can provide a valuable supplement to existing algorithms to improve the completeness of catalogs used for monitoring volcanoes.

SPT-SZ MCMF: an extension of the SPT-SZ catalogue over the DES region

ABSTRACT We present an extension to a Sunyaev–Zel’dovich Effect (SZE) selected cluster catalogue based on observations from the South Pole Telescope (SPT); this catalogue extends to lower signal to noise than the previous SPT–SZ catalogue and therefore includes lower mass clusters. Optically derived redshifts, centres, richnesses, and morphological parameters together with catalogue contamination and completeness statistics are extracted using the multicomponent matched filter (MCMF) algorithm applied to the S/N > 4 SPT–SZ candidate list and the Dark Energy Survey (DES) photometric galaxy catalogue. The main catalogue contains 811 sources above S/N = 4, has 91 per cent purity, and is 95 per cent complete with respect to the original SZE selection. It contains in total 50 per cent more clusters and twice as many clusters above z = 0.8 in comparison to the original SPT-SZ sample. The MCMF algorithm allows us to define subsamples of the desired purity with traceable impact on catalogue completeness. As an example, we provide two subsamples with S/N > 4.25 and S/N > 4.5 for which the sample contamination and cleaning-induced incompleteness are both as low as the expected Poisson noise for samples of their size. The subsample with S/N > 4.5 has 98 per cent purity and 96 per cent completeness and is part of our new combined SPT cluster and DES weak-lensing cosmological analysis. We measure the number of false detections in the SPT-SZ candidate list as function of S/N, finding that it follows that expected from assuming Gaussian noise, but with a lower amplitude compared to previous estimates from simulations.

Deep Neural Networks Based Denoising of Regional Seismic Waveforms and Impact on Analysis of North Korean Nuclear Tests

AbstractWe test a deep learning based denoising autoencoder algorithm on regional and teleseismic seismological and hydroacoustic datasets, which we compile from the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organisation. We focus on stations which can be relevant to investigate North Korean nuclear tests. Denoising of waveform records using autoencoder techniques potentially enables improved signal detection and processing due to lowered signal-to-noise ratios. We train and compare the performance of several different denoising autoencoder models, for short- and long waveform periods, trained on the complete station network as well as on individual stations. We investigate if the denoised waveform signals are useful for seismic source analysis and if they can still be reliably used in downstream analysis for further inferences on the seismic source type, i.e. seismic moment tensor analysis. The declared North Korean nuclear tests are a suitable benchmark test set, as they have extensively been researched and their source type and location might be assumed known. Verification of the source type is of particular interest for potential nuclear tests under international law. We find that care needs to be taken using the denoised waveform data, as a slight bias is introduced in the seismic moment tensor analysis. However we also find promising results hinting at possible future use of the technique for standard analyses, as it improves the investigation of smaller events. Autoencoder based denoising techniques could be employed in future routine frameworks to increase earthquake catalog completeness and possibly aid in detecting smaller potential treaty relevant events.

A Practical Approach to Automatic Earthquake Catalog Compilation in Local OBS Networks Using Deep-Learning and Network-Based Algorithms

Abstract In land-based seismology, modern automatic earthquake detection and phase picking algorithms have already proven to outperform classic approaches, resulting in more complete catalogs when only taking a fraction of the time needed for classic methods. For marine-based seismology, similar advances have not been made yet. For ocean-bottom seismometer (OBS) data, additional challenges arise, such as a lower signal-to-noise ratio and fewer labeled data sets available for training deep-learning models. However, the performance of available deep-learning models has not yet been extensively tested on marine-based data sets. Here, we apply three different modern event detection and phase picking approaches to an ∼12 month local OBS data set and compare the resulting earthquake catalogs and location results. In addition, we evaluate their performance by comparing different subcatalogs of manually detected events and visually revised picks to their automatic counterparts. The results show that seismicity patterns from automatically compiled catalogs are comparable to a manually revised catalog after applying strict location quality control criteria. However, the number of such well-constrained events varies between the approaches and catalog completeness cannot be reliably determined. We find that PhaseNet is more suitable for local OBS networks compared with EQTransformer and propose a pick-independent event detection approach, such as Lassie, as the preferred choice for an initial event catalog compilation. Depending on the aim of the study, different schemes of manual repicking should be applied because the automatic picks are not yet reliable enough for developing a velocity model or interpreting small-scale seismicity patterns.

PROMETHEUS: Probability in the Mediterranean of Tephra dispersal for various grain sizes. A tool for the evaluation of the completeness of the volcanic record in medial-distal archives

PROMETHEUS is a statistical tool that allows creating maps showing the probability of finding tephra deposits of different grain sizes, originating from eruptions of a specific volcanic source, at any location around the vent. It couples wind profiles at different heights in the Mediterranean area with terminal velocity of volcanic particles. The input parameters include the height of the eruption column (which characterizes the intensity of the eruption), wind statistics (directions and intensities), and tephra deposits of a selected grain size. In particular, we used the parameterizations provided by Costa et al. (2016) and performed simulations using the HAZMAP tephra dispersal model to determine the maximum reachable distances that tephra can cover under weak, medium, and strong wind conditions (e.g. 7, 30, and 70 m/s velocities at the tropopause) and with column heights of 10, 20, and 30 km, depositing of at least the loading corresponding to 0.1 mm (corresponding to cryptotephra). Three alternative configurations of the model are validated analyzing first the eruptive source of Somma Vesuvius, with the related explosive eruptions from 22 ka Pomici di Base to the 1944 eruption. A further validation is made by comparing the probabilistic maps with the tephrostratigraphy of known marine and terrestrial cores using standard test of proportions (binomial distributions) and the binary logistic regression model, statistically quantifying the effectiveness of the model against the tephrostratigraphy recorded within this time frame. Based on this validation, a preferred configuration of PROMETHEUS is selected. PROMETHEUS probability maps will guide the selection of sampling sites for specific tephra deposits and could also support the study of the completeness of overall eruption catalogs over time.

The clustering of dark sirens' invisible host galaxies

Dark sirens are a powerful way to infer cosmological and astrophysical parameters from the combination of gravitational wave sirens and galaxy catalogues. Importantly, the method relies on the completeness of the galaxy catalogues being well modelled. A magnitude-limited catalogue will always be incomplete to some extent, requiring a completion scheme to avoid biasing the parameter inference. Standard methods include homogeneous and multiplicative completion, which have the advantage of simplicity but underestimate or overestimate the amplitude of structure at low completeness, respectively. In this work, we propose a new method to complete galaxy catalogues which uses clustering information to incorporate knowledge of the large scale structure into the dark sirens method. We find that if the structure of the true number of galaxies is sufficiently well preserved in the catalogue, our estimator can perform drastically better than both homogeneous and multiplicative completion. We lay the foundations for a maximally informative dark sirens analysis and discuss its limitations.

Earthquake forecasting model for Albania: the area source model and the smoothing model

Abstract. We proposed earthquake forecasting models for Albania, one of the most seismogenic regions in Europe, to give an overview of seismic activity by implementing area source and smoothing approaches. The earthquake catalogue was first declustered to remove foreshocks and aftershocks when they are within the derived distance windows and time windows of mainshocks. Considering catalogue completeness, the events with M≥4.1 during the period of 1960–2006 were implemented for the learning forecast model. The forecasting is implemented into an area source model that includes 20 sub-regions and a smoothing model with a cell size of 0.2∘ × 0.2∘ to forecast the seismicity in Albania. Both models show high seismic rates along the western coastline and in the southern part of the study area, consistent with previous studies that discussed seismicity in the area and currently active regions. To further validate the forecast performance of the two models, we introduced the Molchan diagram to quantify the correlation between models and observations. The Molchan diagram suggests that the models are significantly better than a random distribution, confirming their forecasting abilities. Our results provide crucial information for subsequent research on seismic activity, such as probabilistic seismic hazard assessment.

Probabilistic seismic hazard analysis for Bagan (Myanmar)

Following the 2016 Chauk earthquake in Bagan (Myanmar), numerous old pagodas and temples suffered severe damage. This research presents a study on the seismic hazard analysis of the Bagan city in Myanmar, based on a probabilistic framework focussing on analysing 43 temples with their associated local soil information. To this end, two seismic source models are developed based on the tectonic setting of the region and information available. Instrumental and historical records are compiled from both literature and international earthquake catalogues while conducting catalogue completeness. This study uses state-of-the-art ground motion models to perform probabilistic seismic hazard analysis and develop seismic hazard maps for different return periods in the region. Results are also expressed for selected temples in the region in terms of site-specific uniform hazard spectra. The findings indicate significant seismic activity, with peak ground acceleration in the region ranging from 0.25 to 0.36 g for a return period of 2475 years, 0.22–0.32 g for a return period of 975 years, and 0.18–0.24 g for a return period of 475 years. The updated hazard levels indicate that the literature slightly underestimates hazard in the region under study.

Distinct Yet Adjacent Earthquake Sequences near the Mendocino Triple Junction: 20 December 2021 Mw 6.1 and 6.0 Petrolia, and 20 December 2022 Mw 6.4 Ferndale

Abstract Two earthquake sequences occurred a year apart at the Mendocino Triple Junction in northern California: first the 20 December 2021 Mw 6.1 and 6.0 Petrolia sequence, then the 20 December 2022 Mw 6.4 Ferndale sequence. To delineate active faults and understand the relationship between these sequences, we applied an automated deep-learning workflow to create enhanced and relocated earthquake catalogs for both the sequences. The enhanced catalog newly identified more than 14,000 M 0–2 earthquakes and also found 852 of 860 already cataloged events. We found that deep-learning and template-matching approaches complement each other to improve catalog completeness because deep learning finds more M 0–2 background seismicity, whereas template-matching finds the smallest M < 0 events near already known events. The enhanced catalog revealed that the 2021 Petrolia and 2022 Ferndale sequences were distinct in space and time, but adjacent in space. Though both the sequences happened in the downgoing Gorda slab, the shallower Ferndale sequence ruptured within the uppermost slab near the subduction interface, while the onshore Petrolia sequence occurred deeper in the mantle. Deep-learning-enhanced earthquake catalogs could help monitor evolving earthquake sequences, identify detailed seismogenic fault structures, and understand space–time variations in earthquake rupture and sequence behavior in a complex tectonic setting.

Пространственные вариации коэффициента группируемости землетрясений (на примере Японии)

The article is devoted to the spatial distribution of the average productivity of earthquakes in the main part of Japan for the period 2000–2020. The maps were generated with The Generic Mapping Tools using the Japan Meteorological Agency catalog for earthquakes 40 km below the surface. We are talking about «crustal» earthquakes on the island part of Japan. Maps were built for the period 2010–2020, where the radius (25 km, 50 km, 100 km), catalog completeness (1 and 1.5) and ∆ -productivity (1 and 2) were varied. For the most indicative map, the stability of the picture in time was checked. An attempt was also made to test the effect of surface heat flux on the distribution of average earthquake productivity.

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>1 earthquakes as a function of Mc and apply it to create a calibrated M>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.

False positives are common in single-station template matching

Template matching has become a cornerstone technique of observational seismology. By taking known events, and scanning them against a continuous record, new events smaller than the signal-to-noise ratio can be found, substantially improving the magnitude of completeness of earthquake catalogues. Template matching is normally used in an array setting, however as we move into the era of planetary seismology, we are likely to apply template matching for very small arrays or even single stations. Given the high impact of planetary seismology studies on our understanding of the structure and dynamics of non-Earth bodies, it is important to assess the reliability of template matching in the small-n setting. Towards this goal, we estimate a lower bound on the rate of false positives for single-station template matching by examining the behaviour of correlations of totally uncorrelated white noise. We find that, for typical processing regimes and match thresholds, false positives are likely quite common. We must therefore be exceptionally careful when considering the output of template matching in the small-n setting.

Seismic monitoring in Greece, 1899–2014: catalogue completeness 1966–2014

SUMMARY Instrumental earthquake monitoring in Greece started in 1899–1906 with the first five seismic stations of the National Observatory of Athens, Institute of Geodynamics (NOA). Subsequent upgrades and expansions led to today’s Unified National Seismic Network, which includes almost all permanent seismic stations in Greece and provides waveform and parametric data. We examine the detection capabilities of the Greek seismic networks using phase, location and magnitude data from the International Seismological Centre (ISC) and the various National Institutes. We apply two methods to measure the network performance. In one, we form a grid, and find the 50 per cent probability detection threshold for each station-grid cell pair for different times. In the other, we find the probability-based magnitude of completeness grid for every three months from 1970 to 2014. Both methods show that in 1990 the detection threshold improved significantly in the north part of Greece. A much greater improvement took place in 2010 throughout Greece, yielding a completeness magnitude of $M_p\, \sim$ 1.

GaiaData Release 3

Context.GaiaData Release 3 (DR3) provides a number of new data products that complement the early DR3 made available two years ago. Among these is the firstGaiacatalogue of eclipsing-binary candidates containing 2 184 477 sources with brightnesses from a few magnitudes to 20 mag in theGaiaG-band and covering the full sky.Aims.We present the catalogue, describe its content, provide tips for its use, estimate its quality, and show illustrative samples.Methods.Candidate selection is based on the results of variable object classification performed within theGaiaData Processing and Analysis Consortium. Candidates are then further filtered using eclipsing-binary-tailored criteria based on theG-band light curves. To find the orbital period, a large ensemble of trial periods is first acquired using three distinct period-search methods applied to the cleanedGlight curve of each source. TheGlight curve is then modelled with up to two Gaussians and a cosine for each trial period. The best combination of orbital period and geometric model is finally selected using Bayesian model comparison based on the BIC. A global ranking metric is provided to rank the quality of the chosen model between sources. The catalogue is restricted to orbital periods larger than 0.2 days.Results.Of ∼600 000 available crossmatches, about 530 000 of the candidates are classified as eclipsing binaries in the literature as well, and 93% of them have published periods compatible with theGaiaperiods. Catalogue completeness is estimated to be between 25% and 50%, depending on the sky region, relative to the OGLE4 catalogues of eclipsing binaries towards the Galactic Bulge and the Magellanic Clouds. The analysis of an illustrative sample of ∼400 000 candidates with significant parallaxes shows properties in the observational Hertzsprung-Russell diagram as expected for eclipsing binaries. The subsequent analysis of a subsample of detached bright candidates provides further hints for the exploitation of the catalogue. We also address the observed lack of short-period (less than a day) systems in the Magellanic Cloud in comparison to Galactic systems. The orbital periods, light-curve model parameters, and global rankings are all published in the catalogue with their related uncertainties where applicable.Conclusions.ThisGaiaDR3 catalogue of eclipsing-binary candidates constitutes the largest catalogue to date in terms of number of sources, sky coverage, and magnitude range.

GaiaData Release 3

Context.TheGaiaDR3 catalogue contains, for the first time, about 800 000 solutions with either orbital elements or trend parameters for astrometric, spectroscopic, and eclipsing binaries, and combinations of these three.Aims.With this paper, we aim to illustrate the huge potential of this large non-single-star catalogue.Methods.Using the orbital solutions and models of the binaries, we have built a catalogue of tens of thousands of stellar masses or lower limits thereof, some with consistent flux ratios. Properties concerning the completeness of the binary catalogues are discussed, statistical features of the orbital elements are explained, and a comparison with other catalogues is performed.Results.Illustrative applications are proposed for binaries across the Hertzsprung-Russell Diagram (HRD). Binarity is studied in the giant branch and a search for genuine spectroscopic binaries among long-period variables is performed. The discovery of new EL CVn systems illustrates the potential of combining variability and binarity catalogues. Potential compact object companions are presented, mainly white dwarf companions or double degenerates, but one candidate neutron star is also found. Towards the bottom of the main sequence, the orbits of previously suspected binary ultracool dwarfs are determined and new candidate binaries are discovered. The long awaited contribution ofGaiato the analysis of the substellar regime shows the brown dwarf desert around solar-type stars using true rather than minimum masses, and provides new important constraints on the occurrence rates of substellar companions to M dwarfs. Several dozen new exoplanets are proposed, including two with validated orbital solutions and one super-Jupiter orbiting a white dwarf, all being candidates requiring confirmation. Besides binarity, higher order multiple systems are also found.Conclusions.By increasing the number of known binary orbits by more than one order of magnitude,GaiaDR3 will provide a rich reservoir of dynamical masses and an important contribution to the analysis of stellar multiplicity.