Improving Coseismic Slip Measurements

A physics-based method estimates the duration of earthquakes’ coseismic phase and can help improve the precision of coseismic slip models and magnitude estimates.

FinDerS(+): Real-Time Earthquake Slip Profiles and Magnitudes Estimated from Backprojected Displacement with Consideration of Fault Source Maturity Gradient

The Finite-Fault Rupture Detector (FinDer) algorithm computes rapid line-source rupture models from high-frequency seismic acceleration amplitudes (PGA). In this paper, we propose two extensions to FinDer, called FinDerS and FinDerS+, which have the advantage of taking into account a geological property of the source fault, its structural maturity, as well as its relation to the earthquake slip distribution. These two new algorithms calculate real-time earthquake slip profiles by backprojecting seismic and/or geodetic displacement amplitudes onto the FinDer line-source. This backprojection is based on a general empirical equation established in previous work that relates dynamic peak ground displacement (PGD) at the stations to on-fault coseismic slip. While FinDerS projects PGD onto the current FinDer line-source, FinDerS+ allows the rupture to grow beyond the current model extent to predict future rupture evolution. For an informed interpolation and smoothing of the estimated slip values, FinDerS and FinDerS+ both employ a generic empirical function that has been shown to relate the along-strike gradient of structural maturity of the ruptured fault, the earthquake slip distribution, and the rupture length. Therefore, while FinDer derives magnitudes from a relatively uncertain and general empirical rupture length-magnitude relations, FinDerS and FinDerS+ provide alternate and better informed magnitude estimates using the mean slip of the profiles derived from the integration of fault source maturity. The two new algorithms can incorporate both seismic strong-motion and geodetic displacement data. In order to recover PGD from strong-motion instruments, we double-integrate and high-pass filter (> 0.075 Hz) the seismic acceleration records. Together, the three algorithms exploit the full spectrum of ground-motions, including high frequencies to derive a source fault model (FinDer) and low frequencies to determine the static offsets along this model (FinDerS and FinDerS+). We test the three algorithms for the 2019 MW 7.1 Ridgecrest (California), 2016 MW 7.0 Kumamoto (Japan), and 2008 MW 7.9 Wenchuan (China) earthquakes. Conclusively, low-frequency PGD data and integration of the fault maturity gradient do not speed-up calculations for these events, but provide additional information on slip distribution and final rupture length, as well as alternative estimates of magnitudes that can be useful to check for consistency across the algorithm suite. The FinDer algorithms systematically outperform previously established real-time PGD-based magnitude estimates in terms of speed and accuracy. The resulting slip distributions can be useful for improved ground-motion prediction given the observed relationship between seismic radiation and fault maturity.

Potential effect of seasonally varying station thresholds on joint-maximum-likelihood magnitude estimates from bulletin data

<div> <div> <div> <p>Accurate seismic body-wave magnitudes (m<sub>b</sub>) are important in nuclear test-ban treaty verification.  Network mean magnitudes are known to be biased when the effect of noise obscuring signal at some stations in the monitoring network is ignored.  To overcome this bias a joint-maximum-likelihood method is used to invert bulletin amplitude and period measurements at a network of stations from a number of closely spaced sources, to estimate unbiased network m<sub>b</sub> values and station corrections. For each station a noise threshold is determined independently using the Kelly & Lacoss (1969) method, assuming that large samples of amplitudes reported in a bulletin (in this case from the International Seismological Centre, ISC) follow a Gutenberg-Richter distribution. Where stations report arrivals sufficiently frequently, the noise threshold can be estimated separately for different seasons, to highlight variations caused by, for instance, storms or freezing of nearby ocean.  The noise thresholds at some stations differ by up to 0.4 magnitude units between seasons.  Sensitivity of maximum-likelihood magnitude estimates of a group of announced explosions at the Nevada Test Site to variations in threshold at Canadian Arctic stations (compared with using the annual mean) is generally small (<∼0.01-0.02 units), and greatest for low-magnitude events in the “noisy” season, when the station magnitudes are below the seasonal threshold but above the annual average threshold.</p> <p>UK Ministry of Defence © Crown copyright 2021/AWE</p> </div> </div> </div>

Moment Magnitude Estimates in the Marmara Sea Region (NW Turkey) Using Coda Wave Analysis

<p>Reliable magnitude estimates of the earthquakes are of utmost important for seismic hazard studies, particularly, in tectonically active areas such as the Marmara region of NW Turkey. The region is highly populated and contains a major fault associated with destructive earthquakes. In this study we apply a coda wave modelling approach based on acoustic radiative transfer theory to calculate the source displacement spectrum, and thus to obtain moment magnitudes of small earthquakes within the Marmara region. We examine three-component waveform data extracted from local earthquakes with magnitudes 2.5 ≤ M<sub>L</sub> ≤ 5.7 recorded in a radius of 150 km. For each event in the region, an inversion is performed in several different frequency bands. Our results indicate significant similarity with the local magnitude values reported by the KOERI. Consequently, we focus on establishing a novel relation between M<sub>w</sub> and M<sub>L</sub> in the Marmara region.</p>

Revisiting California’s Past Great Earthquakes and Long-Term Earthquake Rate

ABSTRACT In this study, we revisit the three largest historical earthquakes in California—the 1857 Fort Tejon, 1872 Owens Valley, and 1906 San Francisco earthquakes—to review their published moment magnitudes, and compare their estimated shaking distributions with predictions using modern ground-motion models (GMMs) and ground-motion intensity conversion equations. Currently accepted moment magnitude estimates for the three earthquakes are 7.9, 7.6, and 7.8, respectively. We first consider the extent to which the intensity distributions of all three earthquakes are consistent with a moment magnitude toward the upper end of the estimated range. We then apply a GMM-based method to estimate the magnitudes of large historical earthquakes. The intensity distribution of the 1857 earthquake is too sparse to provide a strong constraint on magnitude. For the 1872 earthquake, consideration of all available constraints suggests that it was a high stress-drop event, with a magnitude on the higher end of the range implied by scaling relationships, that is, higher than moment magnitude 7.6. For the 1906 earthquake, based on our analysis of regional intensities and the detailed intensity distribution in San Francisco, along with other available constraints, we estimate a preferred moment magnitude of 7.9, consistent with the published estimate based on geodetic and instrumental seismic data. These results suggest that, although there can be a tendency for historical earthquake magnitudes to be overestimated, the accepted catalog magnitudes of California’s largest historical earthquakes could be too low. Given the uncertainties of the magnitude estimates, the seismic moment release rate between 1850 and 2019 could have been either higher or lower than the average over millennial time scales. It is further not possible to reject the hypothesis that California seismicity is described by an untruncated Gutenberg–Richter distribution with a b-value of 1.0 for moment magnitudes up to 8.0.

How You Estimate Calories Matters: Calorie Estimation Reversals

Consumers often form calorie estimates. How consumers estimate calories can systematically bias their calorie assessments. We distinguish between magnitude estimates—when consumers judge whether something has “very few” to “many” calories—and numeric estimates—when consumers estimate a number of calories. These two estimation modes lead to calorie estimate reversals when assessing calories in stimuli that trade off type and quantity, such as when assessing calories in a smaller portion of unhealthy food versus a larger portion of healthier food. When forming a “magnitude estimate,” people judge the larger, healthier food portion as containing fewer calories than the smaller, unhealthy food portion. However, when forming a “numeric estimate,” people often come to the opposite conclusion—judging the larger, healthier food portion as having more calories. This reversal occurs because these two estimation modes are differentially sensitive to information regarding a stimulus’ type (e.g., food healthiness), which is processed first, and quantity (e.g., food portion size), which is processed secondarily. Specifically, magnitude estimates are more sensitive to type, whereas numeric estimates attend to both type and quantity. Accordingly, this divergence between calorie estimation modes attenuates when: 1) quantity information is made primary or 2) in an intuitive (vs. deliberative) mindset.

Catalogue of the central stars of planetary nebulae

Planetary nebulae represent a potential late stage of stellar evolution, however, their central stars (CSPNe) are relatively faint and, therefore, pertinent information is available for merely < 20% of the Galactic sample. Consequently, the literature was surveyed to construct a new catalogue of 620 CSPNe featuring important spectral classifications and information. The catalogue supersedes the existing iteration by 25% and includes physical parameters such as luminosity, surface gravity, temperature, magnitude estimates, and references for published spectra. The marked statistical improvement enabled the following pertinent conclusions to be determined: the H-rich/H-poor ratio is 2:1, there is a deficiency of CSPNe with types [WC 5-6], and nearly 80% of binary central stars belong to the H-rich group. The last finding suggests that evolutionary scenarios leading to the formation of binary central stars interfere with the conditions required for the formation of H-poor CSPN. Approximately 50% of the sample with derived values of log L⋆, log Teff, and log g, exhibit masses and ages consistent with single stellar evolutionary models. The implication is that single stars are indeed able to form planetary nebulae. Moreover, it is shown that H-poor CSPNe are formed by higher mass progenitors. The catalogue is available through the Vizier database.

Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile

SUMMARY Magnitude estimation is a central task in seismology needed for a wide spectrum of applications ranging from seismicity analysis to rapid assessment of earthquakes. However, magnitude estimates at individual stations show significant variability, mostly due to propagation effects, radiation pattern and ambient noise. To obtain reliable and precise magnitude estimates, measurements from multiple stations are therefore usually averaged. This strategy requires good data availability, which is not always given, for example for near real time applications or for small events. We developed a method to achieve precise magnitude estimations even in the presence of only few stations. We achieve this by reducing the variability between single station estimates through a combination of optimization and machine learning techniques on a large catalogue. We evaluate our method on the large scale IPOC catalogue with >100 000 events, covering seismicity in the northern Chile subduction zone between 2007 and 2014. Our aim is to create a method that provides low uncertainty magnitude estimates based on physically meaningful features. Therefore we combine physics based correction functions with boosting tree regression. In a first step, we extract 110 features from each waveform, including displacement, velocity, acceleration and cumulative energy features. We correct those features for source, station and path effects by imposing a linear relation between magnitude and the logarithm of the features. For the correction terms, we define a non-parametric correction function dependent on epicentral distance and event depth and a station specific, adaptive 3-D source and path correction function. In a final step, we use boosting tree regression to further reduce interstation variance by combining multiple features. Compared to a standard, non-parametric, 1-D correction function, our method reduces the standard deviation of single station estimates by up to $57\, {\rm per\, cent}$, of which $17\, {\rm per\, cent}$ can be attributed to the improved correction functions, while boosting tree regression gives a further reduction of $40\, {\rm per\, cent}$. We analyse the resulting magnitude estimates regarding their residuals and relation to each other. The definition of a physics-based correction function enables us to inspect the path corrections and compare them to structural features. By analysing feature importance, we show that envelope and P wave derived features are key parameters for reducing uncertainties. Nonetheless the variety of features is essential for the effectiveness of the boosting tree regression. To further elucidate the information extractable from a single station trace, we train another boosting tree on the uncorrected features. This regression yields magnitude estimates with uncertainties similar to the single features after correction, but without using the earthquake location as required for applying the correction terms. Finally, we use our results to provide high precision magnitudes and their uncertainties for the IPOC catalogue.