Common Reflection Point Research Articles

Characterization of seismic-scale petrofacies variability in the Arbuckle Group using supervised machine learning: Wellington Field, Kansas

The Arbuckle Group in southern Kansas has been investigated for carbon geosequestration-related studies. In this study, we evaluated the seismic-scale petrophysically defined facies variability of the Arbuckle Group at the Wellington Field, Kansas, using quantitative seismic interpretation and a supervised Random Forest classification approach. We first defined three petrophysics-based rock types (petrofacies) from core-derived porosity and permeability measurements using the flow-zone indicator approach. Then, using the artificial neural network, we classified these petrofacies in noncored intervals. We observed that petrofacies 1 corresponds to medium and coarse-grained dolomitic packstone, wackestone, and dolomitic breccia with up to 8% porosity and D-scale permeability values. Whereas petrofacies 2 and 3 correspond to argillaceous and fine-grained micritic dolomites and dolomitic mudstones with lower permeability values for a given porosity, with respect to petrofacies 1. Using the common reflection-point gathers, we performed prestack seismic inversion and calculated various amplitude-variation-with-offset (AVO) attribute volumes. We used these elastic properties and AVO attribute volumes as input for estimating supervised seismic-scale 3D petrofacies and petrofacies probability volumes using the Random Forest algorithm. Results reveal the complex distribution of the petrofacies in the candidate injection and baffle zones in the study area, where petrofacies 1 is mainly prevalent within the lower and upper portions of the Arbuckle group, whereas petrofacies 2 and 3 are mainly present in the middle Arbuckle interval. The workflow we present through this study provides the spatial variability of facies distribution that is reflective of the actual lithology and petrophysical properties of the Arbuckle group in the study area with limited well control.

Seismic deconvolution based on SNR-weighted stacking

We propose a seismic deconvolution algorithm based on signal-to-noise ratio (SNR)-weighted stacking. First, we applied the time-frequency analysis to decompose the common midpoint (CMP) or common reflection point (CRP) gather. It is considered that there are differences in noise between different traces in the frequency domain profile. Then, we employ an SNR-weighted stacking strategy in each frequency-domain profile. Finally, we construct the resolution-enhancement amplitude spectrum, apply a linear inversion approach to calculate the overall weights of each frequency-domain profile, perform weighted stacking between the frequency-domain profiles, and obtain the resolution-enhancement post-stack data. The frequency decomposition of the pre-stack gather is efficiently utilized for the primary data of the proposed method. The SNR-weighted stacking adaptively selects the components with high SNR at each frequency. In addition, the resolution-oriented stacking weights are determined by the linear inversion approach. This algorithm can be adaptively weighted by considering the SNR of each frequency component, which can effectively overcome the problem of low SNR in spectral simulation and obtain high-resolution seismic data. The algorithm does not include wavelet extraction and regularization; thus, the resolution-enhancement data is more reliable. Applications to synthetic and field seismic datasets demonstrate that the proposed resolution-enhancement methodology is of great benefit to the succeeding structural interpretation and thin-bed identification.

An integrated workflow for pre-stack gather optimization

Abstract High-quality seismic data contribute to improving the prediction accuracy of complex reservoirs. Currently, there are several methods for enhancing pre-stack gather resolution and signal-to-noise ratio (SNR). Meanwhile, to avoid incorrect seismic interpretation, we must evaluate whether the processed data maintains its original characteristics and reliability. This paper establishes an integrated workflow for optimizing common-reflection-point (CRP) gather that is divided into processing and quality control. Furthermore, we summarize the comprehensive quality control means and explain their specific significance in each step. Data processing includes four parts: gather flattening, SNR enhancement, energy compensation, and resolution improvement. Simultaneously, we use well log data, forward simulation, stack data, and inversion to guarantee the processed data is optimized and keeps its original characteristics. Applications in carbonate reservoir CRP gathers demonstrate that this workflow can comprehensively optimize pre-stack seismic data and improve SNR and resolution. Importantly, the quality controls guarantee results have improved accuracy in reservoir prediction and stronger correlation coefficients with well data.

Efficient prestack time migration velocity analysis: A correlation‐based global optimization approach

AbstractAn accurate migration velocity model is vital to seismic imaging. Conventional time‐domain migration velocity analysis techniques commonly obtain the velocity model by iteratively picking the velocity spectra of common reflection point gathers or scanning selection, which is usually laborious and computationally expensive. To solve these issues, we propose a more effective migration velocity analysis method for prestack time migration. This method is based on cross‐correlation stacked time shift functions of local migrated gathers and uses the very fast simulated annealing algorithm to semi‐automatically invert the migration velocity parameters. This new correlation‐based objective function can catch subtle bending features of the reflector in the local migrated gather. The proposed approach applies an efficient global optimization algorithm that does not depend on the initial parameter value and is easy to extend to the multiparameter complex case. Moreover, the strategy of using local migrated gathers can incorporate prior information in the inversion to avoid the effects of misidentifying reflection events and the interference of multiples. Furthermore, applying a dynamic parameter constraint strategy for the very fast simulated annealing algorithm improves the convergence speed. Both synthetic and real data examples demonstrate that the proposed migration velocity analysis method can efficiently build an accurate time migration velocity model, which can also provide a good initial velocity model for depth migration.

3D Near-surface Velocity Model Building by Integrating Surface, Borehole Seismic, and Well-logging Data in a Small-scale Testbed

This study aims to build a 3D velocity model for investigating and evaluating underground contaminants flowing through groundwater pathways. To accomplish this goal, we performed seismic exploration at a test site with fractured rock layers within a depth of 100 m. The acquired seismic data was then used to construct a three-dimensional (3D) P-wave velocity model of the test site. Although comprehensive geophysical exploration may be useful for investigating the near-surface structure, this paper focuses only on constructing a P-wave velocity model using the seismic method. The primary information of the model consists of extracted velocities from a two-dimensional surface, borehole first-arrival traveltime tomography results, and full waveform sonic log data. Since the test site has the spatial restriction of the survey line, we intended to improve the geological structure analysis results using various quantitative and qualitative analysis methods. First, to increase the reliability of velocity information, we performed a traveltime analysis on the zero vertical interval (ZVI) gather from the borehole seismic data. Then, we identified the qualitative information of the fracture zone's location by analyzing the amplitude variation of the ZVI gather. Moreover, we extracted structural information using the common reflection point gather from the borehole seismic data to supplement the obtained lithological information. However, the information for constructing a 3D P-wave velocity model was still insufficient due to the spatial constraints of the survey line and the limited depth of the borehole seismic survey. We filled this gap using the radial basis function interpolation method. We could verify the completed 3D P-wave velocity model by comparing it with core log data. Overall, the integrated interpretation of the final velocity model and the analysis results could provide a probable pathway that indicates hydraulic connectivity.

Seismic profile denoising based on common-reflection-point gathers using convolution neural networks

Abstract With the development of seismic surveys and the decline of shallow petroleum resources, high resolution and high signal-to-noise ratio have become more important in seismic processing. To improve the quality of seismic data, stationary-phase migration based on dip-angle gathers can be used to separate the reflected waves and noise. However, this method is very computationally intensive and heavily dependent on expert experience. Neural networks currently have powerful adaptive capabilities and great potential to replace artificial processing. Certain applications of convolution neural networks (CNNs) on stack profiles lead to a loss of amplitude information. Therefore, we have developed CNNs for noise reduction based on common-reflection-point (CRP) gathers. We used CRP gathers of stationary-phase migration as labels and CRP gathers of conventional prestack time migration as inputs. In addition, we analyzed the seismic amplitude properties and demonstrated the neural network optimization process and results. The results showed that our methods can achieve fast and reliable denoising and produce high-quality stack profiles that contain true amplitude information. Furthermore, the predicted high-quality CRP gathers can be used for further processing steps, such as normal moveout correction and amplitude variation with offset.

A Self-Supervised Method Using Noise2Noise Strategy for Denoising CRP Gathers

With the improvement of computing power and the rapid development of deep learning, deep-learning-based methods are widely used in the field of seismic data noise suppression. Supervised learning has proven to be effective but its performance largely relies on noise-free data labeling, which is often unavailable or an expensive process. Therefore, as a form of unsupervised learning, self-supervised learning emerged to overcome this difficulty, with its labels coming from the training dataset itself. In this letter, we propose a self-supervised learning method that requires only raw seismic data to train the model by using the Noise2Noise strategy, which takes advantage of the unpredictability of noises to regress from noisy data to clean data. Our method aims at improving the noise suppression effect for common-reflection-point (CRP) gathers. By comparing with conventional methods, both synthetic and field data show that the proposed framework is not only effective in suppressing random noise, but also remains effective for coherent noise.

Influence of shear wave velocity heterogeneity on SH-wave reverberation imaging of the mantle transition zone

SUMMARY Long-period (T > 10 s) shear wave reflections between the surface and reflecting boundaries below seismic stations are useful for studying phase transitions in the mantle transition zone (MTZ) but shear-velocity heterogeneity and finite-frequency effects complicate the interpretation of waveform stacks. We follow up on a recent study by Shearer & Buehler (hereafter SB19) of the top-side shear wave reflection Ssds as a probe for mapping the depths of the 410-km and 660-km discontinuities beneath the USArray. Like SB19, we observe that the recorded Ss410s-S and Ss660s-S traveltime differences are longer at stations in the western United States than in the central-eastern United States. The 410-km and 660-km discontinuities are about 40–50 km deeper beneath the western United States than the central-eastern United States if Ss410s-S and Ss660s-S traveltime differences are transformed to depth using a common-reflection point (CRP) mapping approach based on a 1-D seismic model (PREM in our case). However, the east-to-west deepening of the MTZ disappears in the CRP image if we account for 3-D shear wave velocity variations in the mantle according to global tomography. In addition, from spectral-element method synthetics, we find that ray theory overpredicts the traveltime delays of the reverberations. Undulations of the 410-km and 660-km discontinuities are underestimated when their wavelengths are smaller than the Fresnel zones of the wave reverberations in the MTZ. Therefore, modelling of layering in the upper mantle must be based on 3-D reference structures and accurate calculations of reverberation traveltimes.

The Inverse Fresnel Beam XSP-CDP Stack Imaging in Crosswell Seismic

In order to overcome the shortcomings of serious arc drawing and low computational efficiency in the crosswell seismic migration method and the problems of the inaccurate velocity model and sparse distribution of reflection points in the traditional stack imaging method, the article proposes an inverse Fresnel beam XSP-CDP stack imaging method based on first-arrival wave velocity tomography combined with the characteristics of crosswell seismic wave field. Firstly, an accurate crosswell velocity model is established by the first-arrival wave tomography inversion method based on the characteristics of high energy and easy pick-up of the first-arrival wave in crosswell seismic. Secondly, the velocity model is optimized, and the energy contribution weights of effective rays to the receiver point are calculated through the crosswell seismic Fresnel beam wave field forward numerical simulation method. Then, the reflected wave field is dynamically migrated to the reflection points within the first Fresnel zone according to the weight function, and the intensive common reflection point (CRP) gather after normal moveout (NMO) correction is generated. Finally, an appropriate bin is selected for stacking. In this article, the inverse Fresnel beam method is used to decompose the single-channel seismic wave field into the effective reflection points in the Fresnel zone, which makes the fold of the reflection point more uniform and improves the imaging accuracy. The model test and actual data processing results proved the validity and robustness of this method.

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

Common-reflection-point (CRP) gather is one extensively used prestack seismic data type. However, CRP suffers more noise than a poststack seismic data set. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain, but also in the time-frequency (TF) domain. Therefore, we first use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the TF domain because the SSWT has better TF resolution and reconstruction properties. Then, we use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the TF domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples indicate that our method can effectively attenuate random noise with a better attenuation performance than the commonly used principal component analysis, [Formula: see text] filter, and the continuous wavelet transform method.

An Unsupervised Deep Learning Method for Denoising Prestack Random Noise

Deep-learning-based methods have been successfully applied to seismic data random noise attenuation. Among them, the supervised deep-learning-based methods dominate the unsupervised ones. The supervised methods need accurate noise-free data as training labels. However, the field seismic data cannot meet this requirement. To circumvent it, some researchers utilized realistic-looking synthetic data or denoised results via conventional methods as labels. The former ones encounter the problem of weak generalization ability because it requires the same distribution of test and training data. The latter ones encounter the issue of insufficient denoising ability because its denoising ability is difficult to significantly exceed the conventional methods which were used to generate labels. To avoid preparing noise-free labels, we propose a novel deep learning framework for attenuating random noise of prestack seismic data in an unsupervised manner. The prestack seismic data, such as common-reflection-point (CRP) gathers and common-midpoint (CMP) gathers after normal moveout (NMO) correction, have high self-similarity. It is because their events are coherent in the time–space domain and approximately horizontal from shallow to deep layers. The generator convolutional neural network (GCN) first learns self-similar features before any learning. The useful signals are more self-similar than random noise, which is incoherent and randomly distributed. Therefore, the GCN extracts features of useful signals before random noise. We select the specific training iteration and adopt the early stopping strategy to suppress random noise. Both synthetic and field prestack seismic data examples demonstrate the validity of our methods.

Identification of pore-filling gas hydrate deposits in marine sediments based on amplitude-versus-angle study

Gas hydrate is regarded as an alternative energy source to fossil fuels in the future. In particular, the pore-filling gas hydrate has the advantages of wide distribution and large reserves, which is of great significance for commercial exploitation. We focus on the amplitude-versus-angle (AVA) patterns of reflection interfaces overlying by different pore-filling minerals deposits in order to identify gas hydrate from marine sediments. The AVA responses of different reflection models are obtained based on rock physics model and approximated Zoeppritz equation. The theoretical results show class III AVA patterns for pore-filling gas hydrate deposits and class IV AVA patterns for other pore-filling minerals deposits. In addition, we analyze the effect of gas saturation and different minerals saturation on the AVA patterns. The prestack seismic data of sites GMGS2-08 and GMGS2-16 in South China Sea is processed and the common reflection point (CRP) gathers are applied to verify our theoretical results. The AVA patterns of actual reflection interface overlying by pore-filling gas hydrate deposits and other pore-filling minerals deposits are class III and IV, respectively, in agreement with theoretical results. And the class IV AVA responses for reflection interfaces overlying by fracture-filling gas hydrate studied by predecessors are also tested in our study area. We then propose a new approach to identify pore-filling gas hydrate from marine sediments by AVA analysis.

Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing

A seismic survey identified a basalt flow that could consist of cap rock of CO2 storage beneath saline aquifer sediment in the Southern Continental Shelf of Korea. To determine the precise depth of the basalt flow, specific depth-domain data processing of migration velocity analysis (MVA) was applied to the seismic survey data. The accurate depth measurement of a target structure provides crucial information when storing and stabilizing injected CO2 beneath basalt cap rock. Strong reflections of seismic amplitude at the volcanic mounds were adjusted from the time domain to the exact depth domain by the iterated velocity using MVA. The confidence of the updated velocity was verified by the horizontal alignment of seismic events sorted according to their common reflection point (CRP). The depth difference in volcanic mounds before and after MVA application ranged from 32.5 to 60 m along the vertical axis, showing the eruption shape on the strong-amplitude contour map in detail. The eruption shape of the top of volcanic mounds was verified with spatial continuity in 3D geological interpretation. The presented results provide suitable information that can be used to locate drilling sites and to prepare CO2 injection. The geological model obtained from both time- and depth-domain processing can significantly influence the calculation of the storage volume and can be useful for history matching studies.

Technology and specificity of surface seismic in the Upper Kama Potash Salt Deposit

Exploration surveys at the Upper Kama Potash Salt Deposit widely use the surface seismic method by the common reflection point at depth. Based on the implemented research, a technology is developed for shallow seismic using an explosion source of elastic vibrations for the purposes of geological exploration. The research involved the comparative analysis of the main elastic wave sources used in the shallow seismic. It is highlighted that it is important to consider carefully the near-surface section structure and the surface relief. The accuracy of the velocity analysis procedure in the high-velocity section of salt strata is analyzed. The specificity of acquisition in the shallow seismic with an explosion source is discussed. The actual test data show a considerable increment in the energy of reflections from the roof and floor of the salt strata, which, in the absence of a priori geological information and geophysical logging data (acoustic logging and vertical seismic profiling), affects the velocity analysis precision and, as a consequence, the accuracy of reflection identification at depth. It is found that the explosion source has a much higher signal/noise ratio as against a pulse cartridge, which greatly improves neutrality of interpretation results. The use of a pulse cartridge in the surveys in the depth interval of 200–400 m is only justified when the surface conditions are perfect and the low velocity layer is not thick.

Potentially large subsurface gas hydrate bodies in the Mexican Ridges, Southwestern Gulf of Mexico

Because rising ocean temperatures can destabilize gas hydrate, identifying and characterizing large shallow hydrate bodies is increasingly important to understanding their hazard potential. In the southwestern Gulf of Mexico, reanalysis of 3D seismic reflection data reveals evidence for the presence of six potentially large gas hydrate bodies located at shallow depths below the seafloor. We originally interpreted these bodies as salt because they share common visual characteristics on seismic data with shallow allochthonous salt bodies, including high-impedance boundaries and homogeneous interiors with very little acoustic reflectivity. However, when seismic images are constructed using acoustic velocities associated with salt, the resulting images were of poor quality containing excessive moveout in common-reflection point offset image gathers. Further investigation reveals that using lower valued acoustic velocities results in higher quality images with little or no moveout. We believe that these lower acoustic values are representative of gas hydrate and not of salt. Directly underneath these bodies lies a zone of poor reflectivity, which is typical and expected under hydrate. Observations of gas in a nearby well, other indicators of hydrate in the vicinity, and the regional geologic context support the interpretation that these large bodies are composed of hydrate. The total equivalent volume of gas within these bodies is estimated to potentially be as large as 1.5 gigatons or 10.5 trillion cubic feet, considering uncertainty for estimates of porosity and saturation, comparable with the entire proven natural gas reserves of Trinidad and Tobago in 2019.

Passive seismic imaging of a craton edge – Central Australia

Passive seismic recordings of teleseismic P wave arrivals and their immediate coda along a dense profile of stations can be used to image the reflection structure beneath the profile. The process exploits the autocorrelation of the seismic signals that extracts the reflection response from the transmitted signals recorded at the surface, which can be migrated to provide a depth image. This common reflection point (CRP) imaging exploits the same portion of the seismic record used in receiver function studies, but is much less influenced by multiple problems in the presence of sediments. This style of passive seismic imaging is applied to a dense line of 68 stations at 3.5 km spacing at the northern edge of the Gawler Craton in South Australia in the MAL experiment. With nine months of passive seismic monitoring it has been possible to image fine-scale structural variations throughout the lithosphere. There is a strong variation in crustal thickness as the MAL profile crosses the inferred location of the craton edge under sedimentary cover. A transitional zone between crust and mantle can be identified that extends beneath the receiver function Moho, pinching out gently at the craton edge. The CRP imaging reveals distinct reflectivity in the lithospheric mantle with horizontal scales of 10–30 km, and vertical scales of no more than a few kilometres. The character of this reflectivity appears to change across the craton edge. There are indications of a semi-continuous mid-lithosphere discontinuity at around 90 km depth.

Common-Reflection-Point-Based Prestack Depth Migration for Imaging Lithosphere in Python: Application to the Dense Warramunga Array in Northern Australia

Abstract We exploit estimates of P-wave reflectivity from autocorrelation of transmitted teleseismic P arrivals and their coda in a common reflection point (CRP) migration technique. The approach employs the same portion of the vertical-component seismogram, as in standard Ps receiver function analysis. This CRP prestack depth migration approach has the potential to image lithospheric structures on scales as fine as 4 km or less. The P-wave autocorrelation process and migration are implemented in open-source software—the autocorrelogram calculation (ACC) package, which builds on the widely used the seismological Obspy toolbox. The ACC package is written in the open-source and free Python programming language (3.0 or newer) and has been extensively tested in an Anaconda Python environment. The package is simple and friendly to use and runs on all major operating systems (e.g., Windows, macOS, and Linux). We utilize Python multiprocessing parallelism to speed up the ACC on a personal computer system, or servers, with multiple cores and threads. The application of the ACC package is illustrated with application to the closely spaced Warramunga array in northern Australia. The results show how fine-scale structures in the lithospheric can be effectively imaged at relatively high frequencies. The Moho ties well with conventional H−κ receiver analysis and deeper structure inferred from stacked autocorrelograms for continuous data. CRP prestack depth migration provides an important complement to common conversion point receiver function stacks, since it is less affected by surface multiples at lithospheric depths.

Seafloor elastic-parameter estimation based on a regularized AVO inversion method

Seafloor properties play important roles in both the civilian and the military domains. In this paper, we propose to use a regularization-based AVO (amplitude variation with offset) inversion method to estimate the elastic parameters of seafloor sediment along a seismic survey line. Conventional AVO research focuses mainly on the strata beneath seafloor sediment; accordingly, the AVO response of the interface between the seawater and seafloor sediment is typically ignored. Several studies on the estimation of seafloor elastic parameters have been published based on AVO theory. However, these earlier studies only consider one CRP (common reflection point) gather in the inversion process. When dealing with a long seismic survey with many CRPs, earlier methods involve clear random inversion errors along the survey line. To suppress the inversion errors, we try to regularize each of the elastic parameters along the seismic survey by a total-variation method. Synthetic tests demonstrate that the regularization-based method can effectively suppress the inversion errors along the seismic survey and yield much cleaner inversion results. Finally, we apply the regularization-based method to a real two-dimensional marine seismic survey and obtain satisfactory results, which further proves the efficacy of the method.

Inverse gaussian beam stack imaging in 3D crosswell seismic exploration of deviated wells and Its application

In crosswell seismic, imaging section produced by migration employed with wave equation has a serious arc phenomenon at its edge and small effective range due to the restriction of geometry. Another imaging section produced by VSP-CDP stack imaging employed with ray-tracing theory is amplitude-preserved but has difficulty in imaging 3D complex lithological structure accurately. Therefore, the paper proposes inverse Gaussian beam stack imaging in 3D crosswell seismic of deviated well based on the Gaussian beam ray-tracing theory. Through employing Gaussian beam ray-tracing theory into 3D crosswell seismic, the energy relationship between the seismic wave field and its effective rays is analyzed. Then the author converts the single channel seismic wave field data in the common shot point (CSP) gather into multiple effective wave fields in common reflection point (CRP) gather by inverse Gaussian beam when imaging and thus produces more intensive reflection points fold number. Finally, the wave fields of the effective reflection points in the same stack bin, selected from horizontal and vertical direction of the 2D measuring line, are projected onto the 2D measuring line, chosen according to the distribution characteristics of the reflection points, and stacked into an imaging section. The method is applied in X oilfield to identify the internal structure of offshore gas cloud area. The result provides positive support for inverse Gaussian beam stack imaging in producing accurate imaging in 3D complex lithological structure and make it a powerful imaging tool for 3D crosswell seismic data processing.

Common reflection point mapping of the mantle transition zone using recorded and 3-D synthetic ScS reverberations

SUMMARY The method of ScS reverberation migration is based on a ‘common reflection point’ analysis of multiple ScS reflections in the mantle transition zone (MTZ). We examine whether ray-theoretical traveltimes, slownesses and reflection points are sufficiently accurate for estimating the thickness H of the MTZ, defined by the distance between the 410- and 660-km phase transitions. First, we analyse ScS reverberations generated by 35 earthquakes and recorded at hundreds of seismic stations from the combined Arrays in China, Hi-NET in Japan and the Global Seismic Network. This analysis suggests that H varies by about 30 km and therefore that dynamic processes have modified the large-scale structure of the MTZ in eastern Asia and the western Pacific region. Second, we apply the same procedure to spectral-element synthetics for PREM and two 3-D models. One 3-D model incorporates degree-20 topography on the 410 and 660 discontinuities, otherwise preserving the PREM velocity model. The other model incorporates the degree-20 velocity heterogeneity of S20RTS and leaves the 410 and 660 flat. To optimize reflection point coverage, our synthetics were computed assuming a homogeneous grid of stations using 16 events, four of which are fictional. The resolved image using PREM synthetics resembles the PREM structure and indicates that the migration approach is correct. However, ScS reverberations are not as strongly sensitive to H as predicted ray-theoretically because the migration of synthetics for a model with degree-20 topography on the 410 and 660: H varies by less than 5 km in the resolved image but 10 km in the original model. In addition, the relatively strong influence of whole-mantle shear-velocity heterogeneity is evident from the migration of synthetics for the S20RTS velocity model and the broad sensitivity kernels of ScS reverberations at a period of 15 s. A ray-theoretical approach to modelling long-period ScS traveltimes appears inaccurate, at least for continental-scale regions with relatively sparse earthquake coverage. Additional modelling and comparisons with SS precursor and receiver function results should rely on 3-D waveform simulations for a variety of structures and ultimately the implementation of full wave theory.