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1. Characterizing sediment sources by non-negative matrix factorization of detrital geochronological data

   https://doi.org/https://doi.org/10.1016/j.epsl.2019.01.044  

   Saylor, Joel E. Sundell (April 2019, Earth and planetary science letters)

   This paper explores an inverse approach to the problem of characterizing sediment sources' (“source” samples) age distributions based on samples from a particular depocenter (“sink” samples) using non-negative matrix factorization (NMF). It also outlines a method to determine the optimal number of sources to factorize from a set of sink samples (i.e., the optimum factorization rank). We demonstrate the power of this method by generating sink samples as random mixtures of known sources, factorizing them, and recovering the number of known sources, their age distributions, and the weighting functions used to generate the sink samples. Sensitivity testing indicates that similarity between factorized and known sources is positively correlated to 1) the number of sink samples, 2) the dissimilarity among sink samples, and 3) sink sample size. Specifically, the algorithm yields consistent, close similarity between factorized and known sources when the number of sink samples is more than ∼3 times the number of source samples, sink data sets are internally dissimilar (cross-correlation coefficient range >0.3, Kuiper V value range >0.35), and sink samples are well-characterized (>150–225 data points). However, similarity between known and factorized sources can be maintained while decreasing some of these variables if other variables are increased. Factorization of three empirical detrital zircon U–Pb data sets from the Book Cliffs, the Grand Canyon, and the Gulf of Mexico yields plausible source age distributions and weights. Factorization of the Book Cliffs data set yields five sources very similar to those recently independently proposed as the primary sources for Book Cliffs strata; confirming the utility of the NMF approach. The Grand Canyon data set exemplifies two general considerations when applying the NMF algorithm. First, although the NMF algorithm is able to identify source age distribution, additional geological details are required to discriminate between primary or recycled sources. Second, the NMF algorithm will identify the most basic elements of the mixed sink samples and so may subdivide sources that are themselves heterogeneous mixtures of more basic elements into those basic elements. Finally, application to a large Gulf of Mexico data set highlights the increased contribution from Appalachian sources during Cretaceous and Holocene time, potentially attributable to drainage reorganization. Although the algorithm reproduces known sources and yields reasonable sources for empirical data sets, inversions are inherently non-unique. Consequently, the results of NMF and their interpretations should be evaluated in light of independent geological evidence. The NMF algorithm is provided both as MATLAB code and a stand-alone graphical user interface for Windows and macOS (.exe and .app) along with all data sets discussed in this contribution. 

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2. Automated High‐Frequency Geomagnetic Disturbance Classifier: A Machine Learning Approach to Identifying Noise While Retaining High‐Frequency Components of the Geomagnetic Field

   https://doi.org/10.1029/2022JA030842  

   McCuen, Brett A.; Moldwin, Mark B.; Steinmetz, Erik S.; Engebretson, Mark J. (February 2023, Journal of Geophysical Research: Space Physics)

   Abstract We present an automated method to identify high‐frequency geomagnetic disturbances in ground magnetometer data and classify the events by the source of the perturbations. We developed an algorithm to search for and identify changes in the surface magnetic field, dB/dt, with user‐specified amplitude and timescale. We used this algorithm to identify transient‐large‐amplitude (TLA) dB/dtevents that have timescale less than 60 s and amplitude >6 nT/s. Because these magnetic variations have similar amplitude and time characteristics to instrumental or man‐made noise, the algorithm identified a large number of noise‐type signatures as well as geophysical signatures. We manually classified these events by their sources (noise‐type or geophysical) and statistically characterized each type of event; the insights gained were used to more specifically define a TLA geophysical event and greatly reduce the number of noise‐type dB/dtidentified. Next, we implemented a support vector machine classification algorithm to classify the remaining events in order to further reduce the number of noise‐type dB/dtin the final data set. We examine the performance of our complete dB/dtsearch algorithm in widely used magnetometer databases and the effect of a common data processing technique on the results. The automated algorithm is a new technique to identify geomagnetic disturbances and instrumental or man‐made noise, enabling systematic identification and analysis of space weather related dB/dtevents and automated detection of magnetometer noise intervals in magnetic field databases. 

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3. Characterizing Multisubevent Earthquakes Using the Brune Source Model

   https://doi.org/10.1785/0120220192  

   Liu, Meichen; Huang, Yihe; Ritsema, Jeroen (January 2023, Bulletin of the Seismological Society of America)

   ABSTRACT Although the Brune source model describes earthquake moment release as a single pulse, it is widely used in studies of complex earthquakes with multiple episodes of high moment release (i.e., multiple subevents). In this study, we investigate how corner frequency estimates of earthquakes with multiple subevents are biased if they are based on the Brune source model. By assuming complex sources as a sum of multiple Brune sources, we analyze 1640 source time functions of Mw 5.5–8.0 earthquakes in the seismic source characteristic retrieved from deconvolving teleseismic body waves catalog to estimate the corner frequencies, onset times, and seismic moments of subevents. We identify more subevents for strike-slip earthquakes than dip-slip earthquakes, and the number of resolvable subevents increases with magnitude. We find that earthquake corner frequency correlates best with the corner frequency of the subevent with the highest moment release (i.e., the largest subsevent). This suggests that, when the Brune model is used, the estimated corner frequency and, therefore, the stress drop of a complex earthquake is determined primarily by the largest subevent rather than the total rupture area. Our results imply that, in addition to the simplified assumption of a radial rupture area with a constant rupture velocity, the stress variation of asperities, rather than the average stress change of the whole fault, contributes to the large variance of stress-drop estimates. 

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4. Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning

   Zhang, Yubo; Botelho, Pedro; Gordon, Trevor; Zussman, Gil; Kadota, Igor (March 2025, arXiv)

   —We consider a decentralized wireless network with several source-destination pairs sharing a limited number of orthogonal frequency bands. Sources learn to adapt their transmissions (specifically, their band selection strategy) over time, in a decentralized manner, without sharing information with each other. Sources can only observe the outcome of their own transmissions (i.e., success or collision), having no prior knowledge of the network size or of the transmission strategy of other sources. The goal of each source is to maximize their own throughput while striving for network-wide fairness. We propose a novel fully decentralized Reinforcement Learning (RL)-based solution that achieves fairness without coordination. The proposed Fair Share RL(FSRL)solution combines: (i) state augmentation with a semiadaptive time reference; (ii) an architecture that leverages risk control and time difference likelihood; and (iii) a fairness-driven reward structure. We evaluate FSRL in more than 50 network settings with different number of agents, different amounts of available spectrum, in the presence of jammers, and in an ad-hoc setting. Simulation results suggest that, when we compare FSRL with a common baseline RL algorithm from the literature, FSRL can be up to 89.0% fairer (as measured by Jain’s fairness index) in stringent settings with several sources and a single frequency band, and 48.1% fairer on average. 

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5. Geochemical Composition of Aeolian Dust and Surface Deposits from the Qatar Peninsula

   Yigiterhan, Oguz; Turner, Jesse; Al-Ansari, Ebrahim; Murray, James (January 2018, Chemical geology)

   The trace metal geochemistry of atmospheric dust and terrestrial surface particles were studied on the Qatar Peninsula from February 2014 to November 2015. We included samples of the mega dust-storm event on 01–02 April 2015. Atmospheric dust samples were collected using passive dust traps. Terrestrial surface deposits of recent dust accumulation and traffic particulate from roads were also sampled. All samples were total acid digested and analyzed for major and trace elements using ICP-OES analyzer. The concentration of thirteen elements (Ca, Mg, Ag, As, Cd, Cr, Cu, Mo, Ni, Se, Sn, Sr, Zn) were enriched in atmospheric dust samples, relative to upper continental crust (UCC). Calcium was especially enriched by up to 435% relative to UCC. About 33% of the total sample mass was CaCO3, reflecting the composition of surface rocks and soils in the source areas. Of the elements typically associated with anthropogenic activity, Ag, Ni, and Zn were most enriched relative to UCC, with enrichment factors (EF) of 182%, 233%, and 209%, respectively. Other metals, which normally reflect anthropogenic sources, including Pb and V, were not significantly enriched, with enrichment factors of 25% and 3%, respectively. Major elements (Al, Mn, Fe) were depleted (− 58%, − 35%, and − 5%, respectively) relative to UCC due to the large dilution effect of the enrichment of CaCO3. Back trajectories were determined at the date of sampling for each sample using the NOAA HYSPLIT model. These showed that the source of the dust particles was almost equally divided between northerly and southerly sources, except one sample, which appeared to originate from the west. More variability in particle source locations were observed during the winter months (October to March). Samples from the mega-dust storm were solubilized using an acetic acid-hydroxylamine hydrochloride leach procedure to obtain an upper estimate of the potential contribution of bioactive elements to surface seawater. The leach procedure solubilized a significant fraction of almost all elements. Ca was the element most affected (81% removed) because of the carbonate minerals present. Bioactive elements like Fe (25%) and P (58%) were also significantly solubilized. Because river input is so small to the Arabian Gulf, this solubilized fraction of dust is likely a major source of nutrients to surface seawater. Enrichment factors were also calculated with respect to the average composition of terrestrial surface deposits (TSD). Samples are not enriched significantly with respect to major components (EF < 2), with a depletion in Ca, K, Na in dust storm samples, reflecting a different origin. A significant enrichment of the same trace metals is evident in dust deposits and in traffic samples, but not in dust storms: Cu, Mo, Ni, Zn, possibly deriving from local atmospheric sources (traffic, industries). Samples with northern and southern origins were compared to see if the composition could be used to identify source. Only three elements were observed to be statistically different. Pb and Na were higher in samples from the south, while Cr was higher in those from the north. 

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