Instead of processing raw waveform data, we compare frequency–magnitude distribution (FMD) completeness with a multi-station phase-reporting threshold (PRT) directly utilizing the earthquake catalog during 2009–2025 from China Earthquake Networks Center and China Seismic Format (CSF) 1.0 phase reports. PRT is the magnitude threshold at which an event is predicted to receive phase reports from at least four stations at the specified model probability. One-to-one matching links 1,147,184 official catalog events to unique CSF events. The official export filename identifies its magnitude field as surface-wave magnitude, M_S , although the workbook has no event-level magnitude-type field. Preferred CSF magnitudes are predominantly local magnitude, M_L (99.8 M_L≥ 0 and depth ≤ 60 km. Unique event–station outcomes, consistent distances, active-month eligibility and negative-sampling adjustment yield 2606 station models. The valid-cell median M_PRT is 2.05. In 1546 common cells, the paired median PRT-minus-goodness-of-fit completeness difference is − 0.25. The FMD-to-PRT and reverse intervals contain 3.3
Ground-Motion Prediction Equations (GMPE) are a fundamental component of Probabilistic Seismic Hazard Analysis (PSHA). In low-seismicity regions such as Brazil, the lack of locally calibrated GMPE has led to the adoption of models developed for tectonically analogous regions, particularly Central and Eastern North America (CENA). Previous Brazilian PSHA studies based on foreign GMPE produced high peak ground acceleration (PGA) estimates, raising concerns regarding their engineering applicability. In this study, a database comprising approximately 150 PGA records from 27 regional earthquakes (2.8 ≤ Mw ≤ 5.6), combined with site characterization surveys measuring V _S30 , is used to evaluate a pool of 48 candidate GMPE spanning five families: the NGA-East final and seed models, and models developed for the United Kingdom, Australia, and Canada. Events below the Mw 4.0 validity floor of most GMPE are evaluated at their true recorded magnitude, with each candidate independently extrapolated downward using the magnitude-scaling shape of Yenier Atkinson (2015). Candidates are screened in two stages and assessed at two distance ranges (R _rup ≤ 100 and ≤ 400 km) through a set of complementary metrics, including normalized residuals (Z-scores), root-mean-square error (RMS), magnitude- and distance-binned RMS, log-likelihood (LLH), the stochastic-area metric (SA), and a random-effects decomposition of residual variance into inter-event, site-to-site, and remaining components. These metrics are reported side by side and discussed qualitatively, rather than combined into a composite score. Most candidates overpredict Brazilian PGA on average at both ranges, plausibly related to differences in crustal attenuation, stress-drop, and focal-depth distributions between CENA and the Brazilian stable platform, although these hypotheses are not directly tested by the present dataset. Three models stand out on complementary grounds and are suggested as a starting point for the epistemic-uncertainty branches of future Brazilian PSHA logic trees: nga_east_usgs_13, with the lowest magnitude-binned RMS and site-to-site variability at 100 km; nga_east_Graizer, the only shortlisted model with a bias not statistically distinguishable from zero at 400 km; and Allen2012_SS14, with the lowest raw RMS at both ranges and attenuation assumptions developed independently of the NGA-East framework. Their residual trends with distance run in opposite directions, so that together they bracket the observed attenuation rather than sharing a common bias. The compiled dataset is made publicly available, and the proposed evaluation framework offers a reproducible methodology transferable to other low-seismicity intraplate settings.
We investigate the regional subsoil structure around the city of Thessaloniki, northern Greece, using Rayleigh wave group velocity dispersion curves (U(f)). Three independent datasets were used. The first two are ambient noise and earthquakes recorded at 12 different sites with broadband seismographs during a period of almost four years. The third dataset consists of small earthquakes recorded by the permanent accelerographic network that operates in Thessaloniki. Ambient noise records were used to estimate Green’s functions between station pairs from the average crosscorrelation of simultaneous noise records. Dispersion curves were estimated from Green’s functions and earthquake records using the Multiple Filter Technique. We explored the possibility of using those dispersion curves to compute tomographic models in the region, independently for each dataset. We found that the seismic noise crosscorrelations and the acceleration records produced compatible results. The seismograph array gives larger U(f) values. We show that this difference is related to the length of the path where group velocity is estimated; the incompatibility of our datasets is the result of the particular distribution with distance within each dataset. The available data imposed relatively large cell sizes for the tomography computations. Although we observe some correlation between our results with a rather complex geology, the cell size used is too large to make detailed comparisons and the restricted amount of data makes the results variable. Our results show the need to analyze the compatibility of different datasets before mixing them in tomographic computations.
Recent increases in seismicity due to anthropogenic activities in southeast New Mexico, USA, demonstrate the need for timely earthquake detection and updated seismicity catalogs. The New Mexico Tech Seismological Observatory (NMTSO) has for many years performed earthquake detection and location in New Mexico using only manual analyst review, but as the number of earthquakes has increased, the implementation of automated methods in the earthquake detection workflow can improve efficiency, accuracy, and completeness of earthquake catalogs. In this study, we applied two machine-learning-based seismic phase auto-pickers to continuous waveform data from multiple seismic networks in southeast New Mexico and west Texas for 2021 to assess the efficiency of these tools for earthquake detection in this region. Results from deep learning tools (the EQTransformer and the PhaseNet auto-pickers) for events located within the New Mexico region were benchmarked against the manually picked NMTSO earthquake catalog derived for the study period. These catalog comparisons, specifically the numbers of missed and false detections, provide a baseline for evaluating machine learning tools in the southeast New Mexico region. We find that both machine learning methods allow detection of significantly more earthquakes than our manual detection, but due to significant numbers of false positives and missed events, they are best employed in conjunction with analyst review. We now employ EQTransformer in our earthquake detection process, thus advancing our ability to provide timely earthquake detection and location in this seismically active area. Article highlights. This study evaluated two machine-learning-based phase pickers for application within the New Mexico Seismic Network. The results demonstrate the importance of parameter optimization to minimize false positive (detections identified as noise events based on manual review) and false negative (earthquakes present in manually reviewed catalog but not present in the machine learning catalog) detections and improve event identification accuracy. By comparing the performance of the different tools, we implemented the most effective workflow for network earthquake monitoring, thereby improving the catalog completeness.
Numerous earthquake cases have shown that long-period ground motions can easily cause panic in high-rise buildings, damage to non-structural components, and even structural damage. However, existing instrumental seismic intensity cannot effectively quantify their strength, and there is a lack of prediction models for far-field long-period ground motions in southeastern coastal provinces and cities of mainland China induced by earthquakes originating in the Taiwan region. Using the peak value of the Absolute Velocity Response Spectrum (Sva) as the intensity measure, this study constructs a long-period ground-motion prediction equation suitable for earthquake early warning, based on 145 earthquakes with M ≥ 5.0 that occurred in the Taiwan region of China from 2012 to 2025 and observation data from seven provinces and municipalities in southeastern mainland China. A mixed-effects model framework is adopted, combining the maximum likelihood and restricted maximum likelihood methods in an iterative stepwise manner to optimize fixed effects and random effects (introducing event-specific random slopes and station random effects). Likelihood ratio tests confirmed that each added random slope significantly improved the fit (p < 0.001). Variance decomposition showed that random effects explain 82.74
