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Distributed Acoustic Sensing (DAS) is a promising technique to improve the rapid detection and characterization of earthquakes. Previous DAS studies mainly focus on the phase information but less on the amplitude information. In this study, we compile earthquake data from two DAS arrays in California, USA, and one submarine array in Sanriku, Japan. We develop a data‐driven method to obtain the first scaling relation between DAS amplitude and earthquake magnitude. Our results reveal that the earthquake amplitudes recorded by DAS in different regions follow a similar scaling relation. The scaling relation can provide a rapid earthquake magnitude estimation and effectively avoid uncertainties caused by the conversion to ground motions. Our results show that the scaling relation appears transferable to new regions with calibrations. The scaling relation highlights the great potential of DAS in earthquake source characterization and early warning. Plain Language Summary Distributed Acoustic Sensing (DAS) is an emerging technique that can convert an optical fiber cable into a dense array to record seismic waves from earthquakes. The recorded seismic signals contain essential information about earthquakes. For example, DAS can record high‐amplitude signals from earthquakes with large magnitudes. However, the exact setting of the optical cables (i.e., installation conditions and coupling with the surrounding medium) is often unknown, thus preventing quantitative estimations of earthquake magnitudes with DAS. In this study, we analyze earthquake data recorded by different DAS arrays and develop a data‐driven method to obtain an empirical relation between the earthquake magnitude and the amplitude of DAS signals. We show that this empirical relation can accurately estimate the earthquake magnitude directly from the DAS data. Furthermore, the empirical relation we obtain from one area can also be applied to new regions with slight calibrations. Our empirical relation can significantly expand the applications of DAS in earthquake research, such as seismic hazard assessment and earthquake early warning. Key Points We present the first data‐based scaling relation between Distributed Acoustic Sensing (DAS) amplitude and earthquake magnitude Earthquake magnitude can be reliably estimated from DAS amplitude with the scaling relation The DAS scaling relation can be transferred from one region to another with minor calibrations

The Russian heatwave in 2010 killed tens of thousands of people, and was by far the worst event in Europe since at least 1950, according to recent studies and a novel universal heatwave index capturing both the duration and magnitude of heatwaves. Here, by taking an improved version of this index, namely the heat wave magnitude index daily, we rank the top ten European heatwaves that occurred in the period 1950-2014, and show the spatial distribution of the magnitude of the most recent heatwave in summer 2015. We demonstrate that all these events had a strong impact reported in historical newspapers. We further reveal that the 1972 heatwave in Finland had a comparable spatial extent and magnitude as the European heatwave of 2003, considered the second strongest heatwave of the observational era. In the next two decades (2021-2040), regional climate projections suggest that Europe experiences an enhanced probability for heatwaves comparable to or greater than the magnitude, extent and duration of the Russian heatwave in 2010. We demonstrate that the probability of experiencing a major European heatwave in the coming decades is higher in RCP8.5 than RCP4.5 even though global mean temperature projections do not differ substantially. This calls for a proactive vulnerability assessment in Europe in support of formulating heatwave adaptation strategies to reduce the adverse impacts of heatwaves.

The parametric catalogue of Italian earthquakes CPTI15 (Catalogo Parametrico dei Terremoti Italiani) represents the latest of a 45-years-long tradition of earthquake catalogues for Italy, and a significant innovation with respect to its predecessors. CPTI15 combines all known information on significant Italian earthquakes of the period 1000–2017, balancing instrumental and macroseismic data. Although the compilation criteria are the same as in the previous CPTI11 version, released in 2012, the catalogue has been revised as concerns: the time coverage, extended to 2017; the associated macroseismic data, improved in quantity and quality; the considered instrumental data, new and/or updated; the energy thresholds, lowered to maximum or epicentral intensity 5 or magnitude 4.0 (instead of 5–6 and 4.5, respectively); the determination of parameters from macroseismic data, based on a new calibration; the instrumental magnitudes, resulting from new sets of data and new conversion relationships to Mw. The catalogue considers and harmonizes data of different types and origins, both macroseismic and instrumental. For all earthquakes, the magnitude is given in terms of true or proxy moment magnitude (Mw), with the related uncertainty. The compilation procedure rigorously implements data and methods published in peer-reviewed journals. All data and methods are clearly indicated in the catalogue, in order to guarantee the maximum transparency of the compilation procedures. As compared to previous CPTI releases, the final CPTI15 catalogue shows a frequency–magnitude distribution coherent with current Italian instrumental catalogues, making it suitable for statistical analysis of the time-space property of the Italian seismicity.

Magnitude is a measure of the size of an earthquake. In practical work such as seismic activity analysis, people usually think that an earthquake has only one magnitude. Many people will have such doubts：why use multiple magnitudes? Why magnitude can’t be unified? In earthquake monitoring, why magnitudes can’t be converted to each other? Why are different magnitudes of the same earthquake different? How to use multiple magnitudes? This paper describes the principle of magnitude determination and the cleverness of magnitude, and discusses the above 11 problems so that scientific researchers and managers can correctly determine and use magnitude in practical work.

The K2 mission will make use of the Kepler spacecraft and its assets to expand upon Kepler's groundbreaking discoveries in the fields of exoplanets and astrophysics through new and exciting observations. K2 will use an innovative way of operating the spacecraft to observe target fields along the ecliptic for the next 2-3 years. Early science commissioning observations have shown an estimated photometric precision near 400 ppm in a single 30 minute observation, and a 6-hr photometric precision of 80 ppm (both at V = 12). The K2 mission offers long-term, simultaneous optical observation of thousands of objects at a precision far better than is achievable from ground-based telescopes. Ecliptic fields will be observed for approximately 75 days enabling a unique exoplanet survey which fills the gaps in duration and sensitivity between the Kepler and TESS missions, and offers pre-launch exoplanet target identification for JWST transit spectroscopy. Astrophysics observations with K2 will include studies of young open clusters, bright stars, galaxies, supernovae, and asteroseismology.

Astrometric surveys such as Gaia and LSST will measure parallaxes for hundreds of millions of stars. Yet they will not measure a single distance. Rather, a distance must be estimated from a parallax. In this didactic article, I show that doing this is not trivial once the fractional parallax error is larger than about 20%, which will be the case for about 80% of stars in the Gaia catalog. Estimating distances is an inference problem in which the use of prior assumptions is unavoidable. I investigate the properties and performance of various priors and examine their implications. A supposed uninformative uniform prior in distance is shown to give very poor distance estimates (large bias and variance). Any prior with a sharp cut-off at some distance has similar problems. The choice of prior depends on the information one has available-and is willing to use-concerning, e.g., the survey and the Galaxy. I demonstrate that a simple prior which decreases asymptotically to zero at infinite distance has good performance, accommodates nonpositive parallaxes, and does not require a bias correction.

In this paper, a scalable end-to-end tree boosting system called XGBoost has been applied for predicting the magnitude of an earthquake from the early part of earthquake waveform data. This model uses the features extracted from the early P wave phase of the records as an input. The model's effectiveness has been verified by using data on earthquakes occurring in the Eurasian plate of Japan Islands from 1996 to 2021. Feature engineering has given 29 new features identified from the early P wave phase of the record, which show a high correlation with the magnitude of an earthquake. The comparison of predicted and actual magnitude shows that a trained XGboost model, which uses a single input record for magnitude prediction, gives an average prediction error of 0.004 ± 0.57 for earthquakes in the test dataset. In contrast, the average prediction error of –1.1 ± 0.80 and –0.65 ± 0.69 has been obtained for the magnitude estimated from conventional τ c and P d methods using the same test dataset. It is further seen that the average predicted magnitude of a single earthquake of magnitude 4.5 and 6.1 (M JMA ) obtained by using multiple nearfield records using XGBoost model is 4.58 ± 0.33 and 6.32 ± 0.29, which is close to the actual magnitude of the earthquake. The results presented in this paper clearly show that the structured data can be effectively used by complex machine learning or deep learning models to predict earthquake magnitude from single or multiple records.

The Gutenberg-Richter frequency-magnitude distribution of earthquakes has become well established in seismology. The slope of the relation between frequency and magnitude (b value) is typically 1, but it often shows variations around 1. Based on an analysis of seismicity prior to the 2011 Tohoku and 2004 Sumatra earthquakes (both in magnitude (M) 9 class), we show that the pronounced decade-scale decrease in b value was a common precursor to both mega-quakes around their hypocenters. This is the first report on M9-class quakes to confirm a change in b value, which has been predicted based on the results of laboratory experiments. We propose that the b value is an important indicator of an impending great earthquake, and has great potential in terms of predicting a future large quake off the Pacific coast of Hokkaido, Japan.

The problem of evaluation of the maximum possible regional earthquake magnitude (Mmax) is reviewed and analyzed. Two aspects of this topic are specified: statistical, and historical and paleoseismic. The frequentist and the fiducial approaches used in the problem are analyzed and compared. General features of the Bayesian approach are discussed within the framework of the Mmax problem. A useful connection between quantiles of a single event and maximum event in a future time interval T is derived. Various estimators of Mmax used in seismological practice are considered and classified. Different methods of estimation are compared: the statistical moment method, the Bayesian method, the estimators based on the extreme value theory (EVT), the estimators using order statistics. A comparison of several well-known estimators of Mmax in the framework of the truncated Gutenberg–Richer law is made. As a more adequate and stable alternative to Mmax the quantiles Qq(T) of maximum earthquake considered in future time horizon T are proposed and analyzed. These quantiles permit us to select a time horizon T and quantile level q for a reliable estimation of maximum possible magnitudes. The instability of Mmax-estimates compared to Qq(T)-estimates is demonstrated. The main steps of the Qq(T)-quantile estimation procedure are highlighted. The historical and paleoseismic data are used, and an additional evidence of low robustness of Mmax-parameter is found. The evidence of possibility of earthquake magnitudes well exceeding the Mmax-value obtained for the truncated Gutenberg–Richter law is found also. The present situation in the domain of the Mmax-evaluation is discussed.

Changes in climate intensity and frequency, including extreme events, heavy and intense rainfall, have the greatest impact on water resource management and flood risk management. Significant changes in air temperature, precipitation, and humidity are expected in future due to climate change. The influence of climate change on flood hazards is subject to considerable uncertainty that comes from the climate model discrepancies, climate bias correction methods, flood frequency distribution, and hydrological model parameters. These factors play a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Inter-comparison Project Phase 6, flood managers and water resource planners are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. We examine catchment-based projected changes in flood quantiles and extreme high flow events for Awash catchments. Conceptual hydrological models (HBV, SMART, NAM and HYMOD), three downscaling techniques (EQM, DQM, and SQF), and an ensemble of hydrological parameter sets were used to examine changes in peak flood magnitude and frequency under climate change in the mid and end of the century. The result shows that projected annual extreme precipitation and flood quantiles could increase substantially in the next several decades in the selected catchments. The associated uncertainty in future flood hazards was quantified using aggregated variance decomposition and confirms that climate change is the dominant factor in Akaki (C2) and Awash Hombole (C5) catchments, whereas in Awash Bello (C4) and Kela (C3) catchments bias correction types is dominate, and Awash Kuntura (C1) both climate models and bias correction methods are essential factors. For the peak flow quantiles, climate models and hydrologic models are two main sources of uncertainty (31% and 18%, respectively). In contrast, the role of hydrological parameters to the aggregated uncertainty of changes in peak flow hazard variable is relatively small (5%), whereas the flood frequency contribution is much higher than the hydrologic model parameters. These results provide useful knowledge for policy-relevant flood indices, water resources and flood risk control and for studies related to uncertainty associated with peak flood magnitude and frequency.