Synthetic Tests Research Articles

Application of ICEEMDAN to noise reduction of near-seafloor geomagnetic field survey data

Submarine geomagnetic field survey is a crucial means of geophysical exploration in the oceans. According to the potential field attenuation, it is preferable to carry out the near-seafloor magnetic survey close to the magnetic sources, for improving the resolution and accuracy of magnetic anomalies. However, due to the complicated marine environments and other factors, the real submarine magnetic data is often distorted by serious noises, which will result in the difficulty and low reliability of subsequent inversion and geological interpretation. Since the excellent adaptability and binary filtering characteristics, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), revised from Empirical Mode Decomposition (EMD), has become a suitable choice for processing the nonlinear and non-stationary submarine magnetic data. Moreover, to effectively reconstruct the useful signal from the Intrinsic Mode Functions (IMFs) of ICEEMDAN, an improved adaptive shrinkage scheme related to interval extremum based on interval thresholding (IT) function is proposed. In this paper, the ICEEMDAN and adaptive IT techniques are combined and applied to denoise the near-seafloor geomagnetic field survey data. The synthetic tests show that, the proposed method has a better application effect for noise reduction than other methods. Meanwhile, the random noises, ocean wave interferences and gross errors can be effectively removed. The application to the really repeated marine magnetic survey profiles illustrates that the inner coincidence accuracy of the total magnetic intensity anomaly datasets along these two profiles, has been improved from ±16.750 nT to ±1.417 nT via data denoising. Both synthetic test and real data study suggest that proposed method has a good performance for denoising the near-seafloor geomagnetic field survey data.

An alternative method for estimating effective elastic thickness by the Vening Meinesz regional isostatic theory with application to continents

SUMMARYEffective elastic thickness, ${T_\mathrm{ e}}$, is a measure of the lithosphere's mechanical strength, and describes the flexural response of the lithosphere to applied loads in the same way as a thin elastic plate. In this study, a new method for estimating ${T_\mathrm{ e}}$ in the spatial domain is presented based on the Veining Meinesz regional isostatic theory. By comparing the absolute values of the correlation coefficients between the observed Moho flexure model and different Veining Meinesz Moho flexure models, the optimal ${T_\mathrm{ e}}$ is determined. Also, the estimated correlation coefficients can be used to examine the effect of the unknown subsurface loads, which are usually difficult to evaluate in the spatial domain. This method is verified to be capable of recovering ${T_\mathrm{ e}}$ variations through synthetic tests for the models with predefined ${T_\mathrm{ e}}$ variations. Finally, the effective elastic thickness is globally determined for the continents using the topography data and recent seismically-derived Moho model. These results are compared with two published ${T_\mathrm{ e}}$ models obtained with different methods. For the areas with relatively small Moho uncertainties and high correlation coefficients, the estimated ${T_\mathrm{ e}}$ variations generally agree with previous results. The differences between three ${T_\mathrm{ e}}$ estimates could characterize the advantages of different methods in specific cases.

A Self-Supervised Denoising Method Based on Deep Noise Estimation

Seismic random noise attenuation is an essential procedure when processing seismic data. Due to various acquisition environments and complex geological conditions, random noise in seismic field data exhibits spatiotemporal levels, significantly increasing the difficulty of extracting seismic signals. The deep learning (DL) methods have shown excellent performances for seismic denoising. However, most existing discriminative DL methods cannot fit field data with noise levels and signal structures that differ from the training set. To tackle the unmatching challenge, we propose a self-supervised deep denoising model named noise estimation-based convolution neural network (NE-CNN), which contains a multiscale denoising module as a pretrained model and a dual-path noise estimation module that estimates the noise level of each data patch with a generalized Gaussian distribution and gray-level co-occurrence matrix (GLCM). With Stein’s unbiased risk estimate (SURE), we can fine-tune the pretrained denoising module according to the estimated noise levels in a self-supervised style solely on data to be processed, leading to a boost of robustness to nonstationary seismic noise. In synthetic and field data tests, NE-CNN performed satisfactorily compared with discriminative DL methods in denoising effects on seismic data with nonstationary noise.

Adaptive Mesh-Free Approach for Gravity Inversion Using Modified Radial Basis Function

This paper proposes a method of gravity inversion based on an adaptive mesh-free approach by using a modified radial basis function (RBF). We parametrize the density distribution by using a mesh-free approach. Scattered points are introduced in most mesh-free methods to discretize the given equations. The subsurface space is generally discretized into regular grid cells, while mesh-free methods can avoid the expensive mesh generation and manipulation required in traditional approaches. To deal with the problem of unstructured nodal discretization, we use a mesh-free discretization strategy to establish a mapping of subsurface grid cells to a cloud of discrete points. The nodes are adaptively refined during the inversion process to better recover abnormal bodies. In addition, the hybrid basis function and the modified radial basis function are used to improve the accuracy and stability of the solution. We verify the effectiveness of the proposed method by using several synthetic and real tests.

Multiple-Space Deep Learning Schemes for Inverse Scattering Problems

Recently, deep learning methods have made significant success in inverse scattering problems (ISPs). However, learning approaches work in different spaces, such as frequency and real space, are seldom explored in solving ISPs. In this work, multiple-space deep learning schemes (MSDLSs) incorporating frequency space and real space processing are studied. Specifically, a network works in low-frequency subspace is first introduced. Then, serial-MSDLS are introduced by combining frequency space and real space networks in serial way to enable networks in different spaces work complementarily. Lastly, to further enable dynamic interaction between multiple-space information during both training and testing stages, a parallel-MSDLS is proposed. Proposed MSDLSs are presented under the framework of back-propagation scheme (BPS). It is shown by synthetic and experimental tests that MSDLSs have a consistent improvement over BPS. It is expected that the proposed schemes will find applications on other inverse problems where an incorporation of multiple-space information is needed.

Elimination of orthophosphate from synthetic leachate using adsorption on bentonite clay

The composition of leachates is very variable and rich in toxic compounds, which requires treatment before discharge into the natural environment in order to avoid their impact on the environment and/or human health and to comply with the requirements of regulatory discharge standards in the natural environment. Among the parameters indicating pollution by leachates, we have COD, BOD5, conductivity, pH, the presence of ammonium ions, nitrite, nitrate, chloride, orthophosphate, etc. This study presents the results of synthetic leachate orthophosphate adsorption tests on bentonite clays. For the determination of the orthophosphate concentration in the synthetic leachate, before and after adsorption, we applied the method of Ultra-Violet adsorption spectroscopy. The adsorption capacity of bentonite, the amount needed and the removal efficiency were determined. The orthophosphate adsorption yield on bentonite reaches up to 32.6 % for 5 g/L of bentonite stirred for 4 hours and exceeds 39,19 % after 24 hours of stirring.

Structurally Constrained Initial Impedance Modeling for Poststack Seismic Inversion

The establishment of initial subsurface model is a crucial step for seismic inversion. An accurate and reasonable initial model can mitigate the ill-posedness of seismic inversion and improve the quality of inversion result. A common method for building initial model is the well-log data interpolation. However, the traditional well-log interpolation method ignores the structural information of the subsurface, resulting in the constructed initial model lacking geological meaning. We propose a novel structurally constrained modeling method (SCMM) to obtain a geologically reasonable initial impedance model for poststack seismic inversion. Well-log interpolation can be represented as an inverse problem. SCMM constrains the inversion process by using a regularization operator that forces the well-log data to be extended to the entire seismic working area along the subsurface local structural direction. First, we calculate the seismic dip from the poststack seismic profile. Then, we design the structural operator based on the estimated seismic dip information to constrain the interpolation process. Under the framework of inversion, the interpolation objective function can be established by combining the structural operator with the well-log data misfit term, and it can be solved efficiently by the conjugate gradient algorithm. Synthetic and field data tests show that the initial model built by SCMM is more consistent with the geological rules than that built by traditional method, and the poststack impedance inversion using SCMM is better than that using traditional modeling method in terms of convergence property and accuracy of inversion result.

Deep Learning Vertical Resolution Enhancement Considering Features of Seismic Data

The resolution of seismic data determines the ability to characterize individual geological structures in a seismic image. Sparse spike inversion (SSI) is an effective approach for improving the resolution of seismic data. However, the basic assumption of SSI is that the strong reflectivity of the formation is sparse, which may not be a reasonable fit for weak thin-layer reflections. In this study, we propose a deep learning-based method to reconstruct high-resolution seismic data by combining information from the longitudinal reflectivity distribution and lateral geological structure features in the field data. A U-shaped network that fuses residual block and attention mechanism is used to learn the relationship between low resolution and high resolution. In addition, we use a hybrid loss function that combines <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\ell _{1}}$ </tex-math></inline-formula> loss and structural similarity (SSIM) loss to optimize the network parameters for better distinguishing the geometrical features characterized by structural amplitude changes. To train the network, we adopt a workflow to automatically generate numerous 2-D low-resolution data and their corresponding high-resolution data. In this workflow, the prior information, such as statistical reflectivity distribution of well logs and structural features of the data are considered. The synthetic data and field data tests show that our method can work well compared to the traditional method even though only a few well logs are available. Especially in the field data example, our method recovers thin layers better and yields laterally more consistent high-resolution results.

Synergistic Hankel Structured Low-Rank Approximation With Total Variation Regularization for Complex Magnetic Anomaly Detection

In the field of magnetic anomaly detection (MAD), the anomaly signal is easy to be submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low-rank (SLR) and total variation (TV) regularization constraints is proposed in this paper. To be specific, a new framework SLR-TV, which mainly contains abnormal signal acquisition, objective function solution, and inverse transform operation is constructed. The noise suppression performance is improved by leveraging the structured low-rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV with five state-of-the-art methods with extensive synthetic and field tests, the results demonstrate that the proposed SLR-TV method achieves the highest SNR improvement by about 15.62% and the best structural similarity (SSIM) improvement by about 62.95% over other methods in the range from -40 dB to 0 dB, showing the utility and high-fidelity of the proposed framework in low SNR.

Fast Bayesian Inversion of Airborne Electromagnetic Data Based on the Invertible Neural Network

The inversion of airborne electromagnetic (AEM) data suffers from severe non-uniqueness of the solution. Bayesian inference provides the means to estimate structural uncertainty with a rich suite of statistical information. However, conventional Bayesian methods are computationally demanding in nonlinear inversions, especially considering the huge volumes of observational data, and thus are not feasible in practice. In this study, we develop a fast Bayesian inversion operator based on the invertible neural network (INN) to fully explore the posterior distribution and quantitatively evaluate the model uncertainty. The INN uses a latent variable to capture the information loss during measurement and constructs bijective mappings between AEM data and the resistivity model. We also introduce another noise variable into the INN to account for data uncertainties. Synthetic tests demonstrate that the INN can effectively recover the posterior distribution by a relatively small ensemble of predicted resistivity models whose AEM responses show a significant agreement with the true signal. We also apply the INN inversion operator to a field data set and obtain results consistent with previous studies. The INN shows considerable adaptability to field observations and strong noise robustness. Meanwhile, the INN delivers the inversion result with posterior model distribution for 23366 AEM time series in 20 seconds on a common PC. The inversion efficiency can be further improved for large data set due to its natural parallelizability. The proposed INN method can support fast Bayesian inversion of AEM data and offer tremendous potential for near real-time uncertainty evaluation of underground structures.

Hierarchical transfer learning for deep learning velocity model building

Deep learning is a promising approach to velocity model building because it has the potential of processing large seismic surveys with minimal resources. By leveraging large quantities of model-gather pairs, neural networks (NNs) can automatically map data to the model space, directly providing a solution to the inverse problem. Such mapping requires big data, which proves prohibitive for 2D and 3D surveys of realistic size. We have developed a transfer learning (TL) strategy. A network is first trained on a smaller subproblem, which then becomes the starting solution to a larger, more difficult data set, akin to the hierarchical multiscale strategy for full-waveform inversion. We perform TL by having subobjectives that escalate in complexity and by first training an NN at estimating horizontally layered velocity models and then proceeding to train an augmented network at estimating 2D dipping layered models. TL improves convergence and allows using a lesser quantity of 2D models for training. For synthetic tests, the structural similarity index measure of 2D interval velocity models in the time domain is [Formula: see text] and the root-mean-square (rms) error is [Formula: see text] m/s. We benchmark our algorithm on the Marmousi2 model and observe that our method can apply to velocity models with continuous deformed layers with dips up to 35°. We benchmark our algorithm on 2D marine field data and produce an rms velocity model that leads to coherent stacking and a time interval velocity model that reproduces salient features of the stacked section. TL expedites and regularizes training and data-driven techniques may be applied to field data with minimal preprocessing even though we lack real target velocity models.

Extraction of Multiple Electrical Parameters From IP-Affected Transient Electromagnetic Data Based on LSTM-ResNet

The induced polarization (IP) effect due to a polarizable body distorts transient electromagnetic (TEM) data, thereby potentially triggering sign reversal phenomena in the measured response. The measured horizontal electric field associated with a grounded-wire TEM is more strongly affected by IP effects than the measured vertical field, meaning that data inversion is more problematic for its component. The traditional inversion method, which assumes a frequency independent resistivity, is complex to extract the chargeability. Yet, the chargeability provides critical information, so it is important to extract the chargeability in addition to the resistivity from IP-affected TEM data. Thus, we proposed a data-driven method based on deep learning to recover the resistivity and chargeability of IP-affected horizontal electric fields. This method, named LSTM-ResNet, combines long short-term memory (LSTM) and a residual network (ResNet) to estimate subsurface electrical properties. Synthetic tests showed that LSTM-ResNet is computationally efficient and accurate for inversion problems. Based on the inverse results with data added noise, we found that a well-trained neural network was not sensitive to noise. A case study was performed by applying LSTM-ResNet to field data collected by a grounded-wire TEM survey at the Kalatongke copper-nickel ore deposit. LSTM-ResNet recovered the simultaneous resistivity and chargeability distributions of subsurface structures from the IP-affected horizontal electric TEM field. The results show a high-chargeability and low-resistivity layer, which was consistent with the lithologic profiles based on drilling cores, indicating the accuracy and robustness of the proposed framework for multi-parameter inversion.

Shear-Wave Splitting Analysis Using Optimization Algorithms

Abstract Shear-wave splitting (SWS) analysis is used to predict fractures in subsurface media. Specifically, two parameters relevant to SWS analysis (the azimuth of the fast shear wave and the time delay between the fast and slow shear waves) are used to quantify the main azimuth and degree of the fracture development, respectively. However, the algorithms of SWS analysis using a grid search have relatively low computational efficiency, as they need to calculate the objective function values of all grid points. To improve the efficiency of SWS analysis, we proposed new algorithms using the gradient descent, Newton, and advance-retreat methods. The new methods use the direction of the fastest gradient descent, the intersection points of the tangent plane of the first-order objective function with the zero plane, and narrowing the range of extremum points to determine the search path. Therefore, this removes the necessity to compare all grid points in the value region. We compared the three methods and the rotation-correlation method, and both synthetic and field data tests indicated that all three methods had higher computational efficiency than the traditional grid search method. Among the proposed methods, the gradient-descent method obtained the most accurate results for both synthetic and field data. Our study shows that SWS analysis combined with the gradient-descent method can accurately and efficiently obtain SWS parameters for fracture prediction.

Common‐midpoint cross‐correlation stacking tomography: A 3D approach for frequency‐dependent mapping of Rayleigh waves, group and phase velocity throughout an active seismic network

AbstractShear‐wave velocity constitutes an important characterization parameter in terms of engineering properties. Several techniques of surface wave analysis have been widely adopted for building near‐surface 1D, or even 2D, S‐wave velocity models, but comparable 3D approaches, such as the surface wave tomography (SWT) method, are usually limited to global or regional seismological studies. Moreover, a reliable 3D subsurface imaging in a complex and noisy near‐surface environment becomes difficult when using the fully automated processing techniques that are commonly suggested for the SWT method. Here, a new strategy that is well adapted to geotechnical investigations is proposed to accomplish automatically the frequency‐dependent mapping of Rayleigh waves, both for group and phase velocity, throughout an active seismic network. The specific methodology can be implemented in both 2D and 3D active seismic data acquisition schemes involving both uniform and non‐uniform layouts of receivers. It combines the advantages of a newly created common‐midpoint (CMP) cross‐correlation (CC) analysis technique, with the precision of the travel‐time tomography method that is usually used in regional seismological studies. The main analysis is based on partitioning the different wave propagation directions, weighting the CC frequency–velocity analysis around one‐wavelength and stacking the CMP dispersion images. The tomographic inversion is applied to frequency‐dependent virtual travel‐times, produced from the azimuth‐dependent results of the specific analysis, in order to generate, as output, the local dispersion curves. The output of the proposed processing method can be interpreted with any preferred inversion algorithm for group and phase velocity, either individually or jointly. Simple synthetic tests together with a 3D seismic experiment in well‐known conditions, using standard refraction and multichannel analysis of surface waves (MASW) equipment, confirmed the effectiveness of the proposed methodology in detecting both lateral and vertical S‐wave velocity variations. A ‘construction site’ case study finally highlighted the potential of the new tool in a very difficult and noisy environment.

A two-step inversion for fault frictional properties using a temporally varying afterslip model and its application to the 2019 Ridgecrest earthquake

The stress status and frictional parameters of a fault system control its slip behaviors over earthquake cycles. These parameters generally exhibit large spatial variations in a real fault system, and resolving such variations is of critical importance for realistic assessments of the seismic hazard. Slowly evolving afterslip reflects the slip response of a fault system to the coseismic stress loading, which is commonly observed following large earthquakes, thus providing information for constraining the fault stress and frictional parameters. In this study, we demonstrate that two independent and determinable frictional properties (i.e., σ(a−b) and Rinit=log⁡(vinit/v0), where σ, (a−b), vinit and v0 are the effective normal stress, velocity-dependence frictional parameters, initial slip velocity, and reference velocity, respectively) can be obtained from an afterslip evolution process. We propose a two-step strategy that uses the temporally segmented afterslip models to invert for the independent parameters. After validating the performance of our inversion procedure by synthetic tests, we apply it to the 2019 Ridgecrest earthquake afterslip process. Our results show that σ(a−b) in the main afterslip area is 0.2∼0.5 MPa. The spatial distribution of this parameter suggests a significant difference of pore pressure at the two ends of the coseismic rupture. The depth profile of the effective normal stress in the afterslip concentration area reveals that the pore fluid pressure is hydrostatic above 5 km depth and increases to lithostatic from 5 to 20 km, indicating a gradual permeability decrease in this depth range. Our analysis also shows that the afterslip models which assume a uniform slip evolution function cannot be directly used for the estimation of the frictional properties.

Weighted-average time-lapse seismic full-waveform inversion

As seismic data can contain information over a large spatial area and are sensitive to changes in the properties of the subsurface, seismic imaging has become the standard geophysical monitoring method for many applications such as carbon capture and storage and reservoir monitoring. The availability of practical tools such as full-waveform inversion (FWI) makes time-lapse seismic FWI a promising method for monitoring subsurface changes. However, FWI is a highly ill-posed problem that can generate artifacts. Because the changes in the earth’s properties are typically small in terms of magnitude and spatial extent, discriminating the true time-lapse signature from noise can be challenging. Different strategies have been proposed to address these difficulties. In this study, we propose a weighted-average (WA) inversion to better control the effects of artifacts and differentiate them from the true 4D changes. We further compare five related strategies with synthetic tests on clean and noisy data. The effects of seawater velocity variation on different strategies also are studied as one of the main sources of nonrepeatability. We tested different strategies of time-lapse FWI (TL-FWI) using the Marmousi and the SEG Advanced Modeling time-lapse models. The results indicate that the WA method can provide the best compromise between accuracy and computation time. This method also provides a range of possible answers of other TL-FWI strategies.

Seismic data interpolation using deeply supervised U‐Net++ with natural seismic training sets

ABSTRACTInterpolation techniques provide an effective method for recovery of missing traces. In recent years, many researchers have applied deep learning methods to seismic data interpolation. Generally, one can choose synthetic data as a training set; however, the features of synthetic data are always inconsistent with those of field data, which may lead to inaccurate interpolation. Meanwhile, U‐Net is a common network structure used in seismic data interpolation; however, the four downsampling and upsampling structures of U‐Net have limited adaptability for different data. In this study, the deep learning method based on U‐Net++ was proposed for seismic data interpolation, which contains U‐Net with different depths. The different depths were connected by skip pathways, and the best depth of the network was chosen for different seismic data by deep supervision. Furthermore, a new strategy for training sets was designed: frequency‐wavenumber (f‐k) bandpass filters were used to convert natural images into a natural seismic training set, which has a stronger generalization capability than synthetic data as the training set. The characteristics of the new training set can effectively improve the accuracy of missing data reconstruction. Compared with the conventional U‐Net and traditional interpolation techniques, for example, the Fourier Bregman method, the proposed method produces more accurate and reasonable interpolation results. Further, it can reconstruct both irregular and regular missing seismic data, even in the presence of strong random noise and aliasing. Synthetic and field data tests showed the effectiveness, robustness and generalization of the proposed method.

Simultaneous source separation by shot collocation and strength variation

Simultaneous shooting offers opportunities for significant cost savings in seismic data acquisitions. The most common strategy uses random delay shots where source separation is achieved during the processing stage, thereby doubling source densities. We have determined that the creation of a collocated source survey, where shots are repeated simultaneously at multiple positions, is a viable alternative strategy, with the additional benefit that it may increase source density even further while keeping the acquisition duration unchanged. Source separation is achieved using overcomplete independent component analysis by first by applying a directional wavelet transform to separate source signals with different slowness, then estimating the mixing matrix, followed by solving an optimization problem with an energy constraint combined with a sparseness inducing prior and obtaining the required waveforms. Synthetic tests find average reconstruction quality on the order of 22.1, 15.4, and 8.4 dB if, respectively, three, four, or five shots are acquired in two mixtures. Examination of the true versus obtained zero-offset sections also demonstrates the robustness of our signal recovery strategy. The advantage of shot collocation over conventional acquisitions is that it may triple or even quadruple the source density for unchanged acquisition durations with superior reconstruction results compared with dithered acquisitions using similar blending factors.

Adjoint Attenuation Tomography of Sichuan–Yunnan Region

AbstractWe use seismic waveform adjoint tomography to constrain the shear-wave attenuation models of the crust and upper mantle in Sichuan–Yunnan region of China. On the base of 3D velocity model with high accuracy by the previous work, we use the adjoint approach to efficiently construct the anelastic structures. Spectral element method with graphic processing unit acceleration is implemented in our work. We use an envelope-based misfit function and develop a mini-batch gradient descent algorithm for model update. We have applied the adjoint tomography algorithms to 41 seismic events, including 1911 high-quality three-component displacement seismic records from 2009 and 2017 in Sichuan–Yunnan region. Synthetic tests show that the attenuation model is well resolved. The generated shear-wave attenuation model reveals detailed structural characteristics of the upper mantle in Sichuan–Yunnan region. Some notable features are observed, such as an obvious strong-attenuation zone in northern Yunnan, which provides evidence for the existence of high-attenuation middle and lower crustal channels.

Seismic source mapping by surface wave time reversal: application to the great 2004 Sumatra earthquake

SUMMARY Different approaches to map seismic rupture in space and time often lead to incoherent results for the same event. Building on earlier work by our team, we ‘time-reverse’ and ‘backpropagate’ seismic surface wave recordings to study the focusing of the time-reversed field at the seismic source. Currently used source-imaging methods relying on seismic recordings neglect the information carried by surface waves, and mostly focus on the P-wave arrival alone. Our new method combines seismic time reversal approach with a surface wave ray-tracing algorithm based on a generalized spherical-harmonic parametrization of surface wave phase velocity, accounting for azimuthal anisotropy. It is applied to surface wave signal filtered within narrow-frequency bands, so that the inherently 3-D problem of simulating surface wave propagation is separated into a suite of 2-D problems, each of relatively limited computational cost. We validate our method through a number of synthetic tests, then apply it to the great 2004 Sumatra–Andaman earthquake, characterized by the extremely large extent of the ruptured fault. Many studies have estimated its rupture characteristics from seismological data (e.g. Lomax, Ni et al., Guilbert et al., Ishii et al., Krüger &amp; Ohrnberger, Jaffe et al.) and geodetic data (e.g. Banerjee et al., Catherine et al., Vigny et al., Hashimoto et al., Bletery et al.). Applying our technique to recordings from only 89 stations of the Global Seismographic Network (GSN) and bandpass filtering the corresponding surface wave signal around 80-to-120, 50-to-110 and 40-to-90 s, we reproduce the findings of earlier studies, including in particular the northward direction of rupture propagation, its approximate spatial extent and duration, and the locations of the areas where most energy appears to be released.