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Ocean swell activities excite body‐wave microseisms that contain information on the Earth's internal structure. Although seismic interferometry is feasible for exploring structures, it faces the problem of spurious phases stemming from an inhomogeneous source distribution. This paper proposes a new method for inferring seismic discontinuity structures beneath receivers using body‐wave microseisms. This method considers the excitation sources of body‐wave microseisms to be spatially localized and persistent over time. To detect the P‐s conversion beneath the receivers, we generalize the receiver function analysis for earthquakes to body‐wave microseisms. The resultant receiver functions are migrated to the depth section. The detected 410‐ and 660‐km mantle discontinuities are consistent with the results obtained using earthquakes, thereby demonstrating the feasibility of our method for exploring deep‐earth interiors. This study is a significant step toward body‐wave exploration considering the sources of P‐wave microseisms to be isolated events. Plain Language Summary The ocean waves excite persistent and random ground motions called microseisms. Since this excitation is independent of seismic activities, this wavefield has information about seismic structures that earthquakes never have. For the deep structure, such as the mantle and core, body‐wave microseisms are more suitable than surface‐wave microseisms because body‐wave microseisms have better sensitivity. Previous studies using body‐wave microseisms mainly adopted the cross‐correlation analysis known as seismic interferometry. This method assumes that the microseisms are excited everywhere. However, the inhomogeneous source distribution of body‐wave microseisms causes artifacts for exploration by seismic interferometry. We developed a new method which circumvents this problem. Assuming that the body‐wave microseisms are spatially isolated, this method extracted the P‐s converted waves beneath receivers from body‐wave microseisms. The 3‐Dimensional imaging result of extracted P‐s converted waves shows both 410‐ and 660‐km mantle discontinuities, consistent with results using earthquakes. This study shows the potential of body‐wave microseisms for exploring the deep earth structure. Key Points The P‐S waves at mantle discontinuities were extracted from the ambient noise excited by the ocean swells We developed the source deconvolution method to generalize a receiver function method to P‐wave microseisms The migration result of P‐S waves was consistent with previous studies, showing the potential of P‐wave microseisms to seismic structures

The Himalayan collision zone, where the Indian Plate subducts beneath the Eurasian Plate at a low angle, has caused many devastating earthquakes. The Kathmandu basin, situated in this region, is surrounded by mountains on all sides and is filled with distinct soft lake sediments with a highly undulating bedrock topography. The basin has been experiencing rapid urbanization, and the growing population in its major cities has increased the vulnerability to seismic risk during future earthquakes. Several strong-motion stations have recently been deployed in the Kathmandu basin. It is expected that the data captured by this strong-motion station array will further enhance our understanding of site amplification in sedimentary basins. Clear P-to-S converted waves have been observed in the strong-motion records. In this study, we investigate the medium boundary that generated these converted waves. First, we estimate the shallow velocity structures, which correspond to the topographic slopes or surface geology, beneath the strong-motion stations. We then apply a receiver function analysis to the strong-motion records. The receiver function indicates that the interface between the soft sediment and seismic bedrock serves as a boundary that generates converted waves. The obtained results can be used for tuning three-dimensional velocity structures. Graphical Abstract

In this study, we probe the misalignment of 200 temporary broadband seismometers based on the polarization of P waves from regional and teleseismic earthquakes. The seismometers were deployed in the epicentral region of 2016 ML 5.8 Gyeongju earthquake, South Korea, and this unprecedented dense array provided a unique opportunity for investigating fault structures from microseismicity. For the full use of three-component seismic records, we estimate and provide time-dependent misorientation angles of the 200 seismometers from June 2018 to March 2021 with uncertainty assessments. Two methods based on the principal component analysis and the minimization of transverse P-wave energy are applied. Our estimates are characterized by small uncertainty (average median absolute deviation of 3.14°). Moreover, periods of suspected temporal changes in misorientation angles mostly coincide with periods of reported technical operations, which demonstrates reliability of our methods to precisely detect the temporal variation of misorientation angles. We expect our misorientation angles to serve as an essential metadata for further seismological researches utilizing the dense array data.

The location and origin of Neoproterozoic‐Cambrian sutures provide keys to understand the formation and evolution of the supercontinent Gondwana. The Larsemann Hills is located near a major Neoproterozoic‐Cambrian suture zone in the Prydz Belt, but has not been examined locally by comprehensive geophysical studies. In this study, we analyzed data collected from a one‐dimensional (1D) joint seismic‐MT array deployed during the 36th Chinese National Antarctic Research Expedition. We found that a sharp Moho discontinuity offset of 6–8 km shows up in the stacked image of teleseismic P‐wave receiver function analysis; coinciding with the abrupt Moho offset, a near‐vertical channel with (a) low resistivity extending to the uppermost mantle depths, and (b) high crustal Poisson's ratio in the crust is identified. These findings provide evidence for the determination of the location and collisional nature of the Prydz belt or a portion of it. Plain Language Summary Our study seeks to unravel the history of a supercontinent called Gondwana. We do this by exploring ancient geological features known as sutures. These sutures are like stitches that hold the Earth's crust together, and they're crucial in understanding how continents were once connected. We specifically focused on a place in Antarctica called the Larsemann Hills, which is located near an important suture zone. This region hasn't received much attention from scientists until now. During the 36th Chinese National Antarctic Research Expedition, we made some exciting discoveries. We found a clear boundary in the Earth's crust, a bit like a seam in a piece of clothing. At the same time, we noticed a unique underground pathway. This pathway had special properties, suggesting that it reaches deep into the Earth's mantle. It's a bit like finding a hidden treasure beneath the Earth's surface. Our findings strongly suggest a connection between these underground discoveries and the ancient sutures in the Earth's crust. In other words, we're piecing together a puzzle that can help us learn more about the Earth's past and how continents have moved over millions of years. Key Points A distinct Moho discontinuity offset of 6–8 km is found in the stacked image obtained from P‐wave receiver function In conjunction with the abrupt Moho offset, a nearly vertical conduit with low resistivity and high Poisson's ratio is identified These geophysical results provide crucial evidence for determining the collisional nature and location of the Prydz orogenic belt

The selected rotation angle and deconvolution time window during S‐wave receiver function (SRF) calculations, and the final SRF quality control may introduce artificial interference. Here we overcome these problems by proposing a new strategy named GC_SRF for obtaining the lithospheric thickness from S‐wave receiver functions, which employs grid search and correlation analysis to obtain reliable SRFs. Extensive tests using synthetic and real data suggest that the GC_SRF strategy is a robust and reproducible approach for estimating lithospheric thickness. Specifically, this GC_SRF strategy can restore the weak Sp phases from full wavefield synthetic seismograms. Clear and distinct discontinuity patterns that do not involve artificial interference compared with those obtained in previous studies of southeastern Tibet are produced here. The post‐stack migrated SRFs reveal distinct lateral variations of lithospheric thickness in southeastern Tibet: (a) Tengchong volcano has a thin crust and thin lithosphere–asthenosphere boundary (LAB) (∼90 km); (b) the Chuandian region has a thicker crust and either a poorly defined or unclear LAB. The absence of a continuous LAB in the Chuandian region may suggest lithospheric regrowth due to the recovery processes of the mantle plume; (c) a thinner crust and clear LAB of ∼160 km depth is presented beneath the Sichuan Basin. Plain Language Summary The outermost shell of the solid Earth, the lithosphere, is the “Birthplace” of numerous natural hazards, such as volcanic eruptions or destructive earthquakes. The thickness and property of the lithosphere are crucial for understanding the evolutionary processes of the Earth, yet potential artificial interferences are difficult to avoid in conventional seismological techniques. In this study, we introduce a new strategy (GC_SRF) to obtain the lithospheric thickness while avoiding potential artificial interference based on the teleseismic technique. This newly developed strategy can obtain robust and reliable lithospheric thickness from synthetic seismograms. The GC_SRF is then applied to image the lithospheric structure beneath the southeastern Tibetan Plateau, where distinct lateral variations of lithospheric thickness are revealed. Further application of this strategy to other complex geological environments will help to advance the understanding of geodynamic processes. Key Points A generalized strategy (GC_SRF) for obtaining the lithospheric thickness from S‐wave receiver functions is proposed This GC_SRF strategy can rebuild the Sp converted phase from either full wavefield synthetic seismograms or field data without artifacts Distinct lateral variations in the lithospheric thickness from the Sichuan Basin to Tengchong Volcano in southeastern Tibet are revealed

Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start \"speaking the same language\" in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multi-metabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www. roccet. ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies. © 2012 The Author(s).

Using P and S‐wave receiver function analysis we investigate the lithospheric structure in the Gulf of Cádiz through a combination of ocean‐bottom seismometers and onshore seismic stations located in southwest Iberia and northwest Africa. By applying common conversion point migration with custom earth models accounting for the marine sediment layer, we imaged the Gibraltar–Alboran slab in a low‐dip subduction geometry. Both the oceanic Moho and lithosphere‐asthenosphere boundary were identified, picturing an oceanic lithosphere approximately 125 km thick. The onset of subduction occurs near the Horseshoe fault, at approximately 10°^{\\circ}$ W, where the slab begins to descend. At ∼${\\sim} $ 4.5°^{\\circ}$ W, the slab bends into an almost vertical direction, coincident with the location of intermediate‐depth seismicity. Beneath the western Gibraltar Arc the underthrusted paleomargins of both Iberia and Nubia appear to be fully preserved and still connected to a narrow section (∼${\\sim} $ 150 km) of oceanic crust.

We present the phase-connected timing solutions of all five pulsars in globular cluster M3 (NGC 5272), namely PSRs M3A to F (PSRs J1342+2822A to F), with the exception of PSR M3C, from FAST archival data. In these timing solutions, those of PSRs M3E and F are obtained for the first time. We find that PSRs M3E and F have low-mass companions and are in circular orbits with periods of 7.1 and 3.0 days, respectively. For PSR M3C, we have not detected its signal in all 41 observations. We found no X-ray counterparts for these pulsars in archival Chandra images in the band of 0.2–20 keV. From the autocorrelation function analysis of M3A and M3B’s dynamic spectra, the scintillation timescale ranges from 7.0 ± 0.3 to 60.0 ± 0.6 minutes, and the scintillation bandwidth ranges from 4.6 ± 0.2 to 57.1 ± 1.1 MHz. The measured scintillation bandwidths from the dynamic spectra indicate strong scintillation, and the scattering medium is anisotropic. From the secondary spectra, we captured a scintillation arc only for PSR M3B with a curvature of 649 ± 23 m−1 mHz−2.

We present a novel method for joint inversion of receiver functions and surface wave dispersion data, using a transdimensional Bayesian formulation. This class of algorithm treats the number of model parameters (e.g. number of layers) as an unknown in the problem. The dimension of the model space is variable and a Markov chain Monte Carlo (McMC) scheme is used to provide a parsimonious solution that fully quantifies the degree of knowledge one has about seismic structure (i.e constraints on the model, resolution, and trade‐offs). The level of data noise (i.e. the covariance matrix of data errors) effectively controls the information recoverable from the data and here it naturally determines the complexity of the model (i.e. the number of model parameters). However, it is often difficult to quantify the data noise appropriately, particularly in the case of seismic waveform inversion where data errors are correlated. Here we address the issue of noise estimation using an extended Hierarchical Bayesian formulation, which allows both the variance and covariance of data noise to be treated as unknowns in the inversion. In this way it is possible to let the data infer the appropriate level of data fit. In the context of joint inversions, assessment of uncertainty for different data types becomes crucial in the evaluation of the misfit function. We show that the Hierarchical Bayes procedure is a powerful tool in this situation, because it is able to evaluate the level of information brought by different data types in the misfit, thus removing the arbitrary choice of weighting factors. After illustrating the method with synthetic tests, a real data application is shown where teleseismic receiver functions and ambient noise surface wave dispersion measurements from the WOMBAT array (South‐East Australia) are jointly inverted to provide a probabilistic 1D model of shear‐wave velocity beneath a given station. Key Points Novel scheme for joint inversion of receiver function Transdimensional algorithm where the number of layers is an unknown Bayesian formulation correctly accounts for data and model uncertainties

Background: An increasing number of diagnostic tests and biomarkers have been validated during the last decades, and this will still be a prominent field of research in the future because of the need for personalized medicine. Strict evaluation is needed whenever we aim at validating any potential diagnostic tool, and the first requirement a new testing procedure must fulfill is diagnostic accuracy. Summary: Diagnostic accuracy measures tell us about the ability of a test to discriminate between and/or predict disease and health. This discriminative and predictive potential can be quantified by measures of diagnostic accuracy such as sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operating characteristic curve, overall accuracy and diagnostic odds ratio. Some measures are useful for discriminative purposes, while others serve as a predictive tool. Measures of diagnostic accuracy vary in the way they depend on the prevalence, spectrum and definition of the disease. In general, measures of diagnostic accuracy are extremely sensitive to the design of the study. Studies not meeting strict methodological standards usually over- or underestimate the indicators of test performance and limit the applicability of the results of the study. Key Messages: The testing procedure should be verified on a reasonable population, including people with mild and severe disease, thus providing a comparable spectrum. Sensitivities and specificities are not predictive measures. Predictive values depend on disease prevalence, and their conclusions can be transposed to other settings only for studies which are based on a suitable population (e.g. screening studies). Likelihood ratios should be an optimal choice for reporting diagnostic accuracy. Diagnostic accuracy measures must be reported with their confidence intervals. We always have to report paired measures (sensitivity and specificity, predictive values or likelihood ratios) for clinically meaningful thresholds. How much discriminative or predictive power we need depends on the clinical diagnostic pathway and on misclassification (false positives/negatives) costs.