Body Wave Phases Research Articles

Ambient Noise‐Derived SmS Splitting: A New Approach to Constraining Crustal Radial Anisotropy

AbstractRecent studies have shown that crustal body wave phases, such as PmP or SmS, can be effectively retrieved from ambient noise cross‐correlations. However, few studies have used these phases to constrain crustal structures. In this study, we successfully retrieve SmS signals from ambient noise data and observe SmS splitting caused by crustal radial anisotropy. Furthermore, through simulations of synthetic tests and application to field data, we demonstrate that these SmS signals can be used to constrain crustal radial anisotropy structures through joint inversion with surface waves. Our findings suggest that SmS signals obtained from noise data can significantly enhance the understanding of fine crustal radial anisotropic structures.

Full-waveform inversion reveals diverse origins of lower mantle positive wave speed anomalies

Determining Earth’s structure is paramount to unravel its interior dynamics. Seismic tomography reveals positive wave speed anomalies throughout the mantle that spatially correlate with the expected locations of subducted slabs. This correlation has been widely applied in plate reconstructions and geodynamic modelling. However, global travel-time tomography typically incorporates only a limited number of easily identifiable body wave phases and is therefore strongly dependent on the source-receiver geometry. Here, we show how global full-waveform inversion is less sensitive to source-receiver geometry and reveals numerous previously undetected positive wave speed anomalies in the lower mantle. Many of these previously undetected anomalies are situated below major oceans and continental interiors, with no geologic record of subduction, such as beneath the western Pacific Ocean. Moreover, we find no statistically significant correlation positive anomalies as imaged using full-waveform inversion and past subduction. These findings suggest more diverse origins for these anomalies in Earth’s lower mantle, unlocking full-waveform inversion as an indispensable tool for mantle exploration.

Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence

Abstract. Seismic phase picking and magnitude estimation are fundamental aspects of earthquake monitoring and seismic event analysis. Accurate phase picking allows for precise characterization of seismic wave arrivals, contributing to a better understanding of earthquake events. Likewise, accurate magnitude estimation provides crucial information about an earthquake's size and potential impact. Together, these components enhance our ability to monitor seismic activity effectively. In this study, we explore the application of deep-learning techniques for earthquake detection and magnitude estimation using continuous seismic recordings. Our approach introduces DynaPicker, which leverages dynamic convolutional neural networks to detect seismic body-wave phases in continuous seismic data. We demonstrate the effectiveness of DynaPicker using various open-source seismic datasets, including both window-format and continuous recordings. We evaluate its performance in seismic phase identification and arrival-time picking, as well as its robustness in classifying seismic phases using low-magnitude seismic data in the presence of noise. Furthermore, we integrate the phase arrival-time information into a previously published deep-learning model for magnitude estimation. We apply this workflow to continuous recordings of aftershock sequences following the Turkey earthquake. The results of this case study showcase the reliability of our approach in earthquake detection, phase picking, and magnitude estimation, contributing valuable insights to seismic event analysis.

Assessing large-scale mantle compositional heterogeneity from machine learning analysis of 28 global P- and S-wave tomography models

SUMMARY Differences between P- and S-wave models have been frequently used as evidence for the presence of large-scale compositional heterogeneity in the Earth's mantle. Our two-step machine learning (ML) analysis of 28 P- and S-wave global tomographic models reveals that, on a global scale, such differences are for the most part not intrinsic and could be reduced by changing the models in their respective null spaces. In other words, P- and S-wave images of mantle structure are not necessarily distinct from each other. Thus, a purely thermal explanation for large-scale seismic structure is sufficient at present; significant mantle compositional heterogeneities do not need to be invoked. We analyse 28 widely used tomographic models based on various theoretical approximations ranging from ray theory (e.g. UU-P07 and MIT-P08), Born scattering (e.g. DETOX) and full-waveform techniques (e.g. CSEM and GLAD). We apply Varimax principal component analysis to reduce tomography model dimensionality by 83 percent, while preserving relevant information (94 percent of the original variance), followed by hierarchical clustering (HC) analysis using Ward's method to quantitatively categorize all models into hierarchical groups based on similarities. We found two main tomography model clusters: Cluster 1, which we called ‘Pure P wave’, is composed of six P-wave models that only use longitudinal body wave phases (e.g. P, PP and Pdiff); and Cluster 2, which we called ‘Mixed’, includes both P- and S-wave models. P-wave models in the ‘Mixed’ cluster use inversion methods that include inputs from other geophysical and geological data sources, and this causes them to be more similar to S-wave models than Pure P-wave models without significant loss of fitness to P-wave data. Given that inclusion of new data classes and seismic phases in more recent tomographic models significantly changes imaged seismic structure, our ML assessment of global tomography model similarity may improve selection of appropriate P- and S-wave models for future global tomography comparative studies.

Imaging with seismic noise: improving extraction of body wave phases from the deep Earth through selective stacking based on H/V ratios

SUMMARY Generating high-resolution images of the deep Earth remains a challenge. Body waves extracted from noise correlations hold high promise to complement earthquake-based studies, but data processing and interpretation are still under development. We develop a methodology to improve signal-to-noise ratio (SNR) of P410P and P660P, waves reflected at the top and bottom of the mantle transition zone, using data from the greater Alpine area and focussing on the second microseismic peak (2.5–10 s period). Rather than stacking all available data, we only stack correlations for days with a low ratio of amplitudes between the horizontal plane and vertical direction (H/V). Due to an improved SNR we can stack over fewer correlation pairs, with the result that horizontal resolution is significantly improved. We propose a systematic approach to determine at each study point the optimal combination of station pairs and the H/V threshold. We observe that the optimal choice of parameters is location dependent and that it is generally different for P410P and P660P. Additionally, we show that in our study area the maximum interstation distance needs to be reduced to ∼150 km for P410P to avoid that this arrival is contaminated by surface waves. Applied to the greater Alpine area we demonstrate a significant improvement of signal extraction: while P410P and P660P were only sporadically identified in standard stacks, with the new processing scheme these arrivals are clearly identified with coherent phases across large distances. We also show that amplitudes of P660P decrease drastically around longitude ∼11°E to ∼12°E, indicating that the lower discontinuity of the transition zone in that area is too broad to have a significant reflexion coefficient for P waves in the second microseismic peak.

The marsquake catalogue from InSight, sols 0–1011

The InSight mission (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) has been collecting high-quality seismic data from Mars since February 2019, shortly after its landing. The Marsquake Service (MQS) is the team responsible for the prompt review of all seismic data recorded by the InSight's seismometer (SEIS), marsquake event detection, and curating seismicity catalogues. Until sol 1011 (end of September 2021), MQS have identified 951 marsquakes that we interpret to occur at regional and teleseismic distances, and 1062 very short duration events that are most likely generated by local thermal stresses nearby the SEIS package. Here, we summarize the seismic data collected until sol 1011, version 9 of the InSight seismicity catalogue. We focus on the significant seismicity that occurred after sol 478, the end date of version 3, the last catalogue described in a dedicated paper. In this new period, almost a full Martian year of new data has been collected, allowing us to observe seasonal variations in seismicity that are largely driven by strong changes in atmospheric noise that couples into the seismic signal. Further, the largest, closest and most distant events have been identified, and the number of fully located events has increased from 3 to 7. In addition to the new seismicity, we document improvements in the catalogue that include the adoption of InSight-calibrated Martian models and magnitude scales, the inclusion of additional seismic body-wave phases, and first focal mechanism solutions for three of the regional marsquakes at distances ∼30°.

Sensitivity of SK(K)S and ScS phases to heterogeneous anisotropy in the lowermost mantle from global wavefield simulations

SUMMARY Observations of seismic anisotropy at the base of the mantle are abundant. Given recent progress in understanding how deformation relates to anisotropy in lowermost mantle minerals at the relevant pressure and temperature conditions, these observations can be used to test specific geodynamic scenarios, and have the potential to reveal patterns of flow at the base of the mantle. For example, several recent studies have sought to reproduce measurements of shear wave splitting due to D″ anisotropy using models that invoke specific flow and texture development geometries. A major limitation in such studies, however, is that the forward modelling is nearly always carried out using a ray theoretical framework, and finite-frequency wave propagation effects are not considered. Here we present a series of numerical wave propagation simulation experiments that explore the finite-frequency sensitivity of SKS, SKKS and ScS phases to laterally varying anisotropy at the base of the mantle. We build on previous work that developed forward modelling capabilities for anisotropic lowermost mantle models using the AxiSEM3D spectral element solver, which can handle arbitrary anisotropic geometries. This approach enables us to compute seismograms for relatively short periods (∼4 s) for models that include fully 3-D anisotropy at moderate computational cost. We generate synthetic waveforms for a suite of anisotropic models with increasing complexity. We first test a variety of candidate elastic tensors in laterally homogeneous models to understand how different lowermost mantle elasticity scenarios express themselves in shear wave splitting measurements. We then consider a series of laterally heterogeneous models of increasing complexity, exploring how splitting behaviour varies across the edges of anisotropic blocks and investigating the minimum sizes of anisotropic heterogeneities that can be reliably detected using SKS, SKKS and ScS splitting. Finally, we apply our modelling strategy to a previously published observational study of anisotropy at the base of the mantle beneath Iceland. Our results show that while ray theory is often a suitable approximation for predicting splitting, particularly for SK(K)S phases, full-wave effects on splitting due to lowermost mantle anisotropy can be considerable in some circumstances. Our simulations illuminate some of the challenges inherent in reliably detecting deep mantle anisotropy using body wave phases, and point to new strategies for interpreting SKS, SKKS and ScS waveforms that take full advantage of newly available computational techniques in seismology.

Structure and QP–QS Relations in the Seattle and Tualatin Basins from Converted Seismic Phases

ABSTRACTWe use converted body-wave phases from local earthquakes to constrain depth to basement and average attenuation relations for the Seattle basin in Washington and the Tualatin basin in Oregon. P-, P-to-S-(Ps), S-to-P-(Sp), and S-wave arrivals are present in three-component recordings of magnitude 2.5–4.0 earthquakes at seismic stations located in these basins. Based on their relative travel times, these phases are attributed to body-wave conversions at the basement-to-basin contact or to high-impedance interfaces within the basins. Depth to basement values are calculated using the differential travel times between direct and converted phases, as well as average P- and S-wave velocity values. We also identify a high-impedance layer in the Tualatin basin that likely represents a laterally extensive deposit of volcanic materials embedded between the basement contact and the Columbia River Basalt Group. In addition, the average QP–QS attenuation relation is calculated for each station by taking the spectral ratio of converted phases to their parent body-wave arrivals. For the Seattle basin, our analysis yields an average QP value of 73 and an average QS value of 60 for seismic waves with frequencies between 2 and 25 Hz. In the Tualatin basin, a much reduced QP–QS relation suggests that average body-wave attenuation is likely higher than in the Seattle basin. The converted phase techniques presented here provide a reliable way to develop estimates of basin depth and attenuation structure for undercharacterized regions using simple passive source seismic records.

The signal of outermost-core stratification in body-wave and normal-mode data

SummarySeismological models of the outer core’s radial velocity structure show that the outermost core is slower than PREM. For models derived from body-wave data these low velocities are confined to the top of the outer core, while normal-mode data prefer a velocity gradient that deviates from PREM throughout the entire outer core. These different models have led to conflicting interpretations regarding the presence of stratification at the top of the outer core. While body-wave based models have been shown to require a compositionally stratified outermost core, the velocity and density profiles obtained from normal-mode data correspond to a homogeneous outer core. In addition, the observed low velocities in the outermost core are difficult to reconcile with compositional models of stratification, as the required enrichment in light elements would generally increase seismic velocities. Here, we investigate how well-suited both seismic body-wave and normal-mode data are to constrain the velocity and density structure of the outer core. To this end, we model and compare the effects of outer-core structure and D″ structure on the differential traveltimes of body-wave phases SmKS and on the centre frequencies of normal modes. We find that a trade-off between outer-core structure and D″ structure exists for both data types, but neither data can be readily explained by reasonable D″ velocities and densities. Low outermost-core velocities are therefore still required by seismological data. Using additional information from the centre frequencies of Stoneley modes—normal modes that are particularly sensitive to variations in velocity and density at the top of the outer core—we confirm that normal-mode data indeed require low velocities with respect to PREM in the outermost core, similar to a recent normal-mode model, and an overall higher outer-core density. The presence of buoyant stratification in the outermost core is therefore not immediately supported by the centre frequencies of Stoneley modes. Stratification with high seismic velocity, as one would expect from most straightforward stratification-forming processes, is directly contradicted by our results.

An array-assisted deep learning approach to seismic phase-picking

Rapid increase of seismic data poses challenges to seismic data processing, in which phase-picking is a crucial procedure and accordingly attracts attention from seismologists. Among the breakthroughs in deep learning methods for seismic data processing, seismologists have especially proposed many seismic phase-picking methods based on deep learning, which have shown much better accuracy than traditional automatic phase-picking methods. However, further improvements are needed to meet the requirements for processing continuous seismic records. Here we propose a new approach combining seismic array scheme and U-Net, an end-to-end neural network developed in image segmentation. Previous studies have shown that U-Net can classify waveforms and determine the onset of seismic phases accurately with the help of contracting and expanding paths. Based on U-Net, we first design a deep learning phase picker (PP) model with a long (40 s) time window. To avoid difficulties in time window selection and interference between P- and S-phase identifications, the model involves two networks with the same structure, which pick the P and S phases separately. To improve model training, we employ data transformation to expand the sample set, add typical noises to the sample set for better resemblance of real data, and slightly increase the weight of the seismic signal containing seismic records for a better balance between precision and recall. We also adopt a more effective array-assisted phase picker (APP) model which correlates the picking results from different stations to minimize misidentification. Using data from Hi-net and Alibaba Aftershock AI Capture Contest, we design training and test sets of different sizes to examine the performance and generalization ability of the model. Results show that the APP can effectively pick the body-wave phases on continuous records with a good generalization ability. The relationship between the model performance and training set is such that the model performance is directly related to the data quality rather than the size of the training set, and there is no serious overfitting even for a small training set. Statistics further show that the model has no special requirements for the locations of time windows and can handle seismic records with large epicentral distances. By comparison with other models (model with short time windows or the one of simultaneous picking of P- and S-phases), we find that APP can successfully process event-crowded waveform data and effectively reduce the influence of noise. Finally, we apply the trained model to a seismic network in the Inner Mongolia. Results show that the number of earthquakes detected by the APP is 30 times the manually-detected one, and that 98.1% of the events in the catalog are detected by the APP. The successful application to a seismic network different from the training set suggests that the APP is robust and has strong generalization ability.

A potential post-perovskite province in D″ beneath the Eastern Pacific: evidence from new analysis of discrepant SKS–SKKS shear-wave splitting

SUMMARY Observations of seismic anisotropy in the lowermost mantle—D″—are abundant. As seismic anisotropy is known to develop as a response to plastic flow in the mantle, constraining lowermost mantle anisotropy allows us to better understand mantle dynamics. Measuring shear-wave splitting in body wave phases which traverse the lowermost mantle is a powerful tool to constrain this anisotropy. Isolating a signal from lowermost mantle anisotropy requires the use of multiple shear-wave phases, such as SKS and SKKS. These phases can also be used to constrain azimuthal anisotropy in D″: the ray paths of SKS and SKKS are nearly coincident in the upper mantle but diverge significantly at the core–mantle boundary. Any significant discrepancy in the shear-wave splitting measured for each phase can be ascribed to anisotropy in D″. We search for statistically significant discrepancies in shear-wave splitting measured for a data set of 420 SKS–SKKS event–station pairs that sample D″ beneath the Eastern Pacific. To ensure robust results, we develop a new multiparameter approach which combines a measure derived from the eigenvalue minimization approach for measuring shear-wave splitting with an existing splitting intensity method. This combined approach allows for easier automation of discrepant shear-wave splitting analysis. Using this approach we identify 30 SKS–SKKS event–station pairs as discrepant. These predominantly sit along a backazimuth range of 260°–290°. From our results we interpret a region of azimuthal anisotropy in D″ beneath the Eastern Pacific, characterized by null SKS splitting, and mean delay time of $1.15 \, \mathrm{ s}$ in SKKS. These measurements corroborate and expand upon previous observations made using SKS–SKKS and S–ScS phases in this region. Our preferred explanation for this anisotropy is the lattice-preferred orientation of post-perovskite. A plausible mechanism for the deformation causing this anisotropy is the impingement of subducted material from the Farallon slab at the core–mantle boundary.

New normal mode constraints on bulk inner core velocities and density

Inner core elastic parameters Vp, Vs and density (ρ) inform both experimental and theoretical studies of inner core composition and potential light element candidates. Meanwhile, constraints on the density jump at the inner core boundary offer much-needed observational insight into the processes powering the geodynamo. Thus, obtaining accurate observational constraints on the elastic structure of the inner core is key to gaining better understanding of its composition and dynamics.While body wave phases provide information on inner core P velocity and anisotropy structure, constraining shear velocity and density currently relies predominantly on core-sensitive normal mode observations. Since the construction of the Preliminary Reference Earth Model (Dziewonski and Anderson, 1981), which was largely based on such data, the database of observed normal modes has become both larger and more accurate, and new model exploration methodologies have become computationally feasible.Here we use recent normal mode center-frequency catalogues to simultaneously explore average shear (Vs) and compressional (Vp) velocities, as well as density (ρ), in the inner core via a Monte Carlo parameter-space search.We find best-fitting Vp values (11,160–11,180 m/s) in good agreement with PREM, while Vs values (3560–3590 m/s) favor a minor reduction (<1% relative to PREM). We show that a recently proposed reduction of 2.5% in Vs in the inner core, while keeping the other two parameters constant, is incompatible with normal mode data, and demonstrate the importance of simultaneously exploring the parameters to address trade-off effects. Meanwhile, we show that ρ is the most poorly-constrained parameter, though mode data without any additional constraints favor a density reduction of ~0.7–1.9% with respect to PREM. We also show that the need for this reduction is largely independent of various assumed parameters, including: the choice of reference mantle and outer core model, observational catalog, and the depth of the ICB.The presented results may be of interest for laboratory and ab-initio studies aiming at constraining IC composition and for studies that consider the energy available to power the geodynamo via compositional convection.

Asymptotic analysis for dispersion relations and travel times in noise cross-correlations: spherically symmetric case

The cross-correlations of ambient noise or earthquake codas are massively used in seismic tomography to measure the dispersion curves of surface waves and the travel times of body waves. Such measurements are based on the assumption that these kinematic parameters in the cross-correlations of noise coincide with those in Green's functions. However, the relation between the cross-correlations of noise and Green's functions deserves to be studied more precisely. In this paper, we use the asymptotic analysis to study the dispersion relations of surface waves and the travel times of body waves, and come to the conclusion that for the spherically symmetric Earth model, when the distribution of noise sources is laterally uniform, the dispersion relations of surface waves and the travel times of SH body-wave phases in noise correlations should be exactly the same as those in Green's functions.

Generalized Seismic Phase Detection with Deep Learning

To optimally monitor earthquake-generating processes, seismologists have sought to lower detection sensitivities ever since instrumental seismic networks were started about a century ago. Recently, it has become possible to search continuous waveform archives for replicas of previously recorded events (template matching), which has led to at least an order of magnitude increase in the number of detected earthquakes and greatly sharpened our view of geological structures. Earthquake catalogs produced in this fashion, however, are heavily biased in that they are completely blind to events for which no templates are available, such as in previously quiet regions or for very large magnitude events. Here we show that with deep learning we can overcome such biases without sacrificing detection sensitivity. We trained a convolutional neural network (ConvNet) on the vast hand-labeled data archives of the Southern California Seismic Network to detect seismic body wave phases. We show that the ConvNet is extremely sensitive and robust in detecting phases, even when masked by high background noise, and when the ConvNet is applied to new data that is not represented in the training set (in particular, very large magnitude events). This generalized phase detection (GPD) framework will significantly improve earthquake monitoring and catalogs, which form the underlying basis for a wide range of basic and applied seismological research.

A three-dimensional oblique incidence method based on stress time-history functions of seismic body waves

This paper presents a three-dimensional oblique incidence method based on stress time-history functions of seismic body waves. This method realizes the three-dimensional oblique incidence of a single body wave phase or superposed multiple phases, which considers the earthquake action in the analysis of the slope seismic response. To implement this method, first, the spatial relationship between slope and incident seismic wave rays is described, and the direction of incident seismic waves is defined with two parameters: the azimuth angle $$\alpha$$, which reflects the azimuth of epicenter; and the incident angle $$\theta$$, which is a concentrated expression of the hypocentral depth and epicentral distance. Second, at the bottom boundary of the slope numerical model, the vibration starting time of each node excited by an oblique incident seismic wave is calculated. By considering vibration time sequence, the successive disturbance of rock and soil medium at the bottom of the slope is achieved. Then the stress time-history functions of six body wave phases are derived. Numerical simulation results of the x demonstrate the effectiveness, applicability and necessity of the proposed method.

Planned Products of the Mars Structure Service for the InSight Mission to Mars

The InSight lander will deliver geophysical instruments to Mars in 2018, including seismometers installed directly on the surface (Seismic Experiment for Interior Structure, SEIS). Routine operations will be split into two services, the Mars Structure Service (MSS) and Marsquake Service (MQS), which will be responsible, respectively, for defining the structure models and seismicity catalogs from the mission. The MSS will deliver a series of products before the landing, during the operations, and finally to the Planetary Data System (PDS) archive. Prior to the mission, we assembled a suite of a priori models of Mars, based on estimates of bulk composition and thermal profiles. Initial models during the mission will rely on modeling surface waves and impact-generated body waves independent of prior knowledge of structure. Later modeling will include simultaneous inversion of seismic observations for source and structural parameters. We use Bayesian inversion techniques to obtain robust probability distribution functions of interior structure parameters. Shallow structure will be characterized using the hammering of the heatflow probe mole, as well as measurements of surface wave ellipticity. Crustal scale structure will be constrained by measurements of receiver function and broadband Rayleigh wave ellipticity measurements. Core interacting body wave phases should be observable above modeled martian noise levels, allowing us to constrain deep structure. Normal modes of Mars should also be observable and can be used to estimate the globally averaged 1D structure, while combination with results from the InSight radio science mission and orbital observations will allow for constraint of deeper structure.

Hydration of marginal basins and compositional variations within the continental lithospheric mantle inferred from a new global model of shear and compressional velocity

AbstractWe present a new global model of shear and compressional wave speeds for the entire mantle, partly based on the data set employed for the shear velocity model savani. We invert Rayleigh and Love surface waves up to the sixth overtone in combination with major P and S body wave phases. Mineral physics data on the isotropic δlnVS/δlnVP ratio are taken into account in the form of a regularization constraint. The relationship between VP and VS that we observe in the top 300 km of the mantle has important thermochemical implications. Back‐arc basins in the Western Pacific are characterized by large VP/VS and not extremely low VS at ∼150 km depth, consistently with presence of water. Most pronounced anomalies are located in the Sea of Japan, in the back‐arc region of the Philippine Sea, and in the South China Sea. Our results indicate the effectiveness of slab‐related processes to hydrate the mantle and suggest an important role of Pacific plate subduction also for the evolution of the South China Sea. We detect lateral variations in composition within the continental lithospheric mantle. Regions that have been subjected to rifting, collisions, and flood basalt events are underlain by relatively large VP/VS ratio compared to undeformed Precambrian regions, consistently with a lower degree of chemical depletion. Compositional variations are also observed in deep lithosphere. At ∼200 km depth, mantle beneath Australia and African cratons has comparable positive VS anomalies with other continental regions, but VP is ∼1% higher.

A previously unreported type of seismic source in the firn layer of the East Antarctic Ice Sheet

AbstractWe identify a unique type of seismic source in the uppermost part of the East Antarctic Ice Sheet recorded by temporary broadband seismic arrays in East Antarctica. These sources, termed “firnquakes,” are characterized by dispersed surface wave trains with frequencies of 1–10 Hz detectable at distances up to 1000 km. Events show strong dispersed Rayleigh wave trains and an absence of observable body wave arrivals; most events also show weaker Love waves. Initial events were discovered by standard detection schemes; additional events were then detected with a correlation scanner using the initial arrivals as templates. We locate sources by determining the L2 misfit for a grid of potential source locations using Rayleigh wave arrival times and polarization directions. We then perform a multiple‐filter analysis to calculate the Rayleigh wave group velocity dispersion and invert the group velocity for shear velocity structure. The resulting velocity structure is used as an input model to calculate synthetic seismograms. Inverting the dispersion curves yields ice velocity structures consistent with a low‐velocity firn layer ~100 m thick and show that velocity structure is laterally variable. The absence of observable body wave phases and the relative amplitudes of Rayleigh waves and noise constrain the source depth to be less than 20 m. The presence of Love waves for most events suggests the source is not isotropic. We propose the events are linked to the formation of small crevasses in the firn, and several events correlate with shallow crevasse fields mapped in satellite imagery.

Extraction of weak PcP phases using the slant-stacklet transform — I: method and examples

SUMMARY In order to study fine scale structure of the Earth’s deep interior, it is necessary to extract generally weak body wave phases from seismograms that interact with various discontinuities and heterogeneities. The recent deployment of large-scale dense arrays providing high-quality data, in combination with efficient seismic data processing techniques, may provide important and accurate observations over large portions of the globe poorly sampled until now. Major challenges are low signal-to-noise ratios (SNR) and interference with unwanted neighbouring phases. We address these problems by introducing scale-dependent slowness filters that preservetime-spaceresolution.Wecombinecomplexwaveletandslant-stacktransformstoobtain the slant-stacklet transform. This is a redundant high-resolution directional wavelet transform with a direction (here slowness) resolution that can be adapted to the signal requirements. To illustrate this approach, we use this expansion to design coherence-driven filters that allow us to obtain clean PcP observations (a weak phase often hidden in the coda of the P wave), for events with magnitude Mw > 5.4 and distances up to 80 ◦ . In this context, we then minimize a linear misfit between P and PcP waveforms to improve the quality of PcP–P traveltime measurements as compared to a standard cross-correlation method. This significantly increases both the quantity and the quality of PcP–P differential traveltime measurements available for the modelling of structure near the core–mantle boundary. The accuracy of our measurements is limited mainly by the highest frequencies of the signals used and the level of noise. We apply this methodology to two examples of high-quality data from dense arrays located in north America. While focusing here on body-wave separation, the tools we propose are more general and may contribute to enhancing seismic signal observations in global seismology in situations of low SNR and high signal interference.

Body waves revealed by spatial stacking on long-term cross-correlation of ambient noise

Theoretical and experimental studies indicate that complete Green’s Function can be retrieved from cross-correlation in a diffuse field. High SNR (signal-to-noise ratio) surface waves have been extracted from cross-correlations of long-duration ambient noise across the globe. Body waves, not extracted in most of ambient noise studies, are thought to be more difficult to retrieve from regular ambient noise data processing. By stacking cross-correlations of ambient noise in 50 km inter-station distance bins in China, western United States and Europe, we observed coherent 20–100 s core phases (ScS, PKIKPPKIKP, PcPPKPPKP) and crustal-mantle phases (Pn, P, PL, Sn, S, SPL, SnSn, SS, SSPL) at distances ranging from 0 to 4 000 km. Our results show that these crustal-mantle phases show diverse characteristics due to different substructure and sources of body waves beneath different regions while the core phases are relatively robust and can be retrieved as long as stations are available. Further analysis indicates that the SNR of these body-wave phases depends on a compromise between stacking fold in spatial domain and the coherence of pre-stacked cross-correlations. Spatially stacked cross-correlations of seismic noise can provide new virtual seismograms for paths that complement earthquake data and that contain valuable information on the structure of the Earth. The extracted crustal-mantle phases can be used to study lithospheric heterogeneities and the robust core phases are significantly useful to study the deep structure of the Earth, such as detecting fine heterogeneities of the core-mantle boundary and constraining differential rotation of the inner core.