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1. Quantifying Magnitude Uncertainty of the 2019 Ridgecrest Earthquake Sequence Through a Sensitivity Study of the Relative Magnitude Method

   https://doi.org/10.1785/0120240126  

   Gable, Sydney L; Huang, Yihe (December 2024, Bulletin of the Seismological Society of America)

   ABSTRACT Precise knowledge of earthquake magnitudes is vital for accurate characterization of seismic hazards. However, the estimation of earthquake magnitude, particularly for small events, is complicated by differences in network procedures and completeness. This produces disparate magnitude estimates for the same event and emphasizes the need for a consistent and transportable magnitude estimation procedure. Here, we investigate the use of the relative magnitude method, which measures earthquake magnitude from a least-squares inversion of interlinked waveform amplitude ratios. Our results show that that the relative magnitude method can establish both local and moment magnitudes for many events in the 2019 Ridgecrest sequence. The method also provides constraints on moment magnitude estimates for M <3 events, which are not routinely available using current methods. Although the relative magnitude method is advantageous because it can be applied uniformly in various regions and does not require empirical distance or attenuation corrections, there are several parameters that require subjective decision making and may introduce bias in the resulting magnitude estimates. These include acceptable thresholds for signal-to-noise ratios and cross correlation, filtering procedures, sampling windows, and station selection. Here, we not only calculate magnitude but also investigate how the subjective decision making affects the resulting magnitudes. Based on our analysis, we present recommendations to enhance the utility of this method for future users. 

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2. Re-Estimation of Earthquake Magnitudes Using a Relative Magnitude Method and the Effects of Magnitude Uncertainty on Seismic Hazard Estimates

   Gable, Sydney L; Huang, Yihe; Shelly, David R (December 2023, AGU)

   Accurate estimates of earthquake magnitude are necessary to improve our understanding of seismic hazard. Unbiased magnitudes for small earthquakes are especially important because magnitude exceedance probabilities for large earthquakes are derived from the behavior of small earthquakes. Also, accurate characterization of small events is becoming increasingly important for ground motion models. However, catalog magnitudes may vary for the same event depending on network procedures and capabilities. In addition, different magnitude scales are often used for events of varying sizes. For example, moment magnitude (Mw) is the widely preferred estimate for earthquake size but it is often not available for small earthquakes (M < 3.5). As a result, statistical measures such as magnitude frequency distribution (MFD) and b-value can be biased depending on magnitude type and uncertainties that arise during the measurement process. In this research we demonstrate the capability of the relative magnitude method to provide a uniform and accurate estimate of earthquake magnitude in a variety of regions, while only requiring the use of waveform data. The study regions include the Permian Basin in Texas, central Oklahoma, and southern California. We present results in which only relative magnitudes are used to estimate MFD and b-value as well as relative magnitudes that are benchmarked to an absolute scale using a coda-envelope derived Mw calibration for small events. We also discuss potential sources of uncertainty in the relative magnitude method such as acceptable signal-to-noise ratios, cross-correlation thresholds, and choice of scaling constant, as well as our attempts to mitigate those uncertainties. 

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3. New Estimates of Magnitude‐Frequency Distribution and b ‐Value Using Relative Magnitudes for the 2011 Prague, Oklahoma Earthquake Sequence

   https://doi.org/10.1029/2023JB026455  

   Gable, S. L.; Huang, Y. (December 2023, Journal of Geophysical Research: Solid Earth)

   Abstract The magnitude‐frequency distribution (MFD) describes the relative proportion of earthquake magnitudes and provides vital information for seismic hazard assessment. Theb‐value, derived from the MFD, is commonly used to estimate the probability that a future earthquake will exceed a specified magnitude threshold. Improved MFD andb‐value estimates are of great importance in the central and eastern United States where high volumes of fluid injection have contributed to a significant rise in seismicity over the last decade. In this study, we recalculate the magnitudes of 8,775 events for the 2011 Prague, Oklahoma sequence using a relative magnitude approach that depends only on waveform data to calculate magnitudes. We also compare the distribution of successive magnitude differences to the MFD and show that a combination of the magnitude difference distribution (MDFD) and relative magnitudes yields a reliable estimate ofb‐value. Using the MDFD and relative magnitudes, we examine the temporal and spatial variations in theb‐value and show thatb‐value ranges between ∼0.6 and 0.85 during the aftershock sequence for at least 5 months after theM5.7 mainshock, though areas surrounding the northeast part of the sequence experience higherb‐values (0.7–0.85) than the southwestern part of the Meeker‐Prague fault whereb‐value is the lowest (0.6–0.7). We also identify a cluster of off‐fault events with the highestb‐values in the catalog (0.85). These new estimates of MFD andb‐value will contribute to understanding of the relations between induced and tectonic earthquake sequences and promote discussion regarding the use ofb‐value in induced seismic hazard estimation. 

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4. Rapid Estimation of Single-Station Earthquake Magnitudes with Machine Learning on a Global Scale

   https://doi.org/10.1785/0120230171  

   Dybing, Sydney N; Yeck, William L; Cole, Hank M; Melgar, Diego (January 2024, Bulletin of the Seismological Society of America)

   ABSTRACT The foundation of earthquake monitoring is the ability to rapidly detect, locate, and estimate the size of seismic sources. Earthquake magnitudes are particularly difficult to rapidly characterize because magnitude types are only applicable to specific magnitude ranges, and location errors propagate to substantial magnitude errors. We developed a method for rapid estimation of single-station earthquake magnitudes using raw three-component P waveforms observed at local to teleseismic distances, independent of prior size or location information. We used the MagNet regression model architecture (Mousavi and Beroza, 2020b), which combines convolutional and recurrent neural networks. We trained our model using ∼2.4 million P-phase arrivals labeled by the authoritative magnitude assigned by the U.S. Geological Survey. We tested input data parameters (e.g., window length) that could affect the performance of our model in near-real-time monitoring applications. At the longest waveform window length of 114 s, our model (Artificial Intelligence Magnitude [AIMag]) is accurate (median estimated magnitude within ±0.5 magnitude units from catalog magnitude) between M 2.3 and 7.6. However, magnitudes above M ∼7 are more underestimated as true magnitude increases. As the windows are shortened down to 1 s, the point at which higher magnitudes begin to be underestimated moves toward lower magnitudes, and the degree of underestimation increases. The over and underestimation of magnitudes for the smallest and largest earthquakes, respectively, are potentially related to the limited number of events in these ranges within the training data, as well as magnitude saturation effects related to not capturing the full source time function of large earthquakes. Importantly, AIMag can determine earthquake magnitudes with individual stations’ waveforms without instrument response correction or knowledge of an earthquake’s source-station distance. This work may enable monitoring agencies to more rapidly recognize large, potentially tsunamigenic global earthquakes from few stations, allowing for faster event processing and reporting. This is critical for timely warnings for seismic-related hazards. 

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5. Improving Stress Drop Estimation through Point-Wise Spectral Ratio Stacking

   Liu, Meichen; Huang, Yihe; Ritsema, Jeroen (December 2023, AGU)

   Stress drop, a crucial source parameter in earthquake studies, significantly influences ground motion prediction and seismic hazard assessment. Despite several existing methods to estimate stress drops, the resulting stress drop estimates often exhibit a wide variation of up to 3-4 orders of magnitude. In this study, we address the robustness of stress drop estimation by introducing a point-wise spectral ratio stacking approach based on empirical Green’s functions (eGfs). Conventional trace-wise stacking can lead to data exclusion due to high signal-to-noise ratio requirements across a wide range of frequency. By adopting point-wise stacking, we maximize the utilization of useful recording information, leading to more accurate stress drop estimates. We applied the point-wise spectral ratio stacking method to a comprehensive dataset comprising global earthquakes from 1990 to 2020 with magnitude larger than Mw5.5 and depth shallower than 50 km. We first verified the moment magnitudes of earthquakes estimated from the resulting seismic moment ratios. We found that the moment magnitude of master events best consistent with catalog magnitudes when the magnitude difference between master and their eGfs differs by about 0.5. Our analysis indicates that stress drop of shallow earthquakes exhibits no depth dependence, while showing a slight increase with magnitude. The results obtained through our optimized stacking process shed new light on stress drop estimate of shallow earthquakes and have the potential to enhance the understanding of earthquake mechanics. 

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