We have successfully applied full-3-D tomography (F3DT) based on a combination of the scattering-integral method (SI-F3DT) and the adjoint-wavefield method (AW-F3DT) to iteratively improve a 3-D starting model, the Southern California Earthquake Center (SCEC) Community Velocity Model version 4.0 (CVM-S4). In F3DT, the sensitivity (Fréchet) kernels are computed using numerical solutions of the 3-D elastodynamic equation and the nonlinearity of the structural inversion problem is accounted for through an iterative tomographic navigation process. More than half-a-million misfit measurements made on about 38,000 earthquake seismograms and 12,000 ambient-noise correlagrams have been assimilated into our inversion. After 26 F3DT iterations, synthetic seismograms computed using our latest model, CVM-S4.26, show substantially better fit to observed seismograms at frequencies below 0.2 Hz than those computed using our 3-D starting model CVM-S4 and the other SCEC CVM, CVM-H11.9, which was improved through 16 iterations of AW-F3DT. CVM-S4.26 has revealed strong crustal heterogeneities throughout Southern California, some of which are completely missing in CVM-S4 and CVM-H11.9 but exist in models obtained from previous crustal-scale 2-D active-source refraction tomography models. At shallow depths, our model shows strong correlation with sedimentary basins and reveals velocity contrasts across major mapped strike-slip and dip-slip faults. At middle to lower crustal depths, structural features in our model may provide new insights into regional tectonics. When combined with physics-based seismic hazard analysis tools, we expect our model to provide more accurate estimates of seismic hazards in Southern California.
S U M M A R YA central problem of seismology is the inversion of regional waveform data for models of earthquake sources. In regions such as Southern California, preliminary 3-D earth structure models are already available, and efficient numerical methods have been developed for 3-D anelastic wave-propagation simulations. We describe an automated procedure that utilizes these capabilities to derive centroid moment tensors (CMTs). The procedure relies on the use of receiver-side Green's tensors (RGTs), which comprise the spatial-temporal displacements produced by the three orthogonal unit impulsive point forces acting at the receivers. We have constructed a RGT database for 219 broad-band stations in Southern California using a tomographically improved version of the 3-D SCEC Community Velocity Model Version 4.0 (CVM4) and a staggered-grid finite-difference code. Finite-difference synthetic seismograms for any earthquake in our modelling volume can be simply calculated by extracting a small, source-centred volume from the RGT database and applying the reciprocity principle. The partial derivatives needed for the CMT inversion can be generated in the same way. We have developed an automated algorithm that combines a grid-search for suitable focal mechanisms and hypocentre locations with a Gauss-Newton optimization that further refines the grid-search results. Using this algorithm, we have determined CMT solutions for 165 small to medium-sized earthquakes in Southern California. Preliminary comparison with the CMT solutions provided by the Southern California Seismic Network (SCSN) shows that our solutions generally provide better fit to the observed waveforms. When applied to a large number of earthquakes, our algorithm may provide a more robust CMT catalogue for earthquakes in Southern California.
Seismograms are convolution results between seismic sources and the media that seismic waves propagate through, and, therefore, the primary observations for studying seismic source parameters and the Earth interior. The routine earthquake location and travel-time tomography rely on accurate seismic phase picks (e.g., P and S arrivals). As data increase, reliable automated seismic phase-picking methods are needed to analyze data and provide timely earthquake information. However, most traditional autopickers suffer from low signal-to-noise ratio and usually require additional efforts to tune hyperparameters for each case. In this study, we proposed a deep-learning approach that adapted soft attention gates (AGs) and recurrent-residual convolution units (RRCUs) into the backbone U-Net for seismic phase picking. The attention mechanism was implemented to suppress responses from waveforms irrelevant to seismic phases, and the cooperating RRCUs further enhanced temporal connections of seismograms at multiple scales. We used numerous earthquake recordings in Taiwan with diverse focal mechanisms, wide depth, and magnitude distributions, to train and test our model. Setting the picking errors within 0.1 s and predicted probability over 0.5, the AG with recurrent-residual convolution unit (ARRU) phase picker achieved the F1 score of 98.62% for P arrivals and 95.16% for S arrivals, and picking rates were 96.72% for P waves and 90.07% for S waves. The ARRU phase picker also shown a great generalization capability, when handling unseen data. When applied the model trained with Taiwan data to the southern California data, the ARRU phase picker shown no cognitive downgrade. Comparing with manual picks, the arrival times determined by the ARRU phase picker shown a higher consistency, which had been evaluated by a set of repeating earthquakes. The arrival picks with less human error could benefit studies, such as earthquake location and seismic tomography.
S U M M A R YIn seismic waveform analysis and inversion, data functionals can be used to quantify the misfit between observed and model-predicted (synthetic) seismograms. The generalized seismological data functionals (GSDF) of Gee & Jordan quantify waveform differences using frequency-dependent phase-delay times and amplitude-reduction times measured on timelocalized arrivals and have been successfully applied to tomographic inversions at different geographic scales as well as to inversions for earthquake source parameters. The seismogram perturbation kernel is defined as the Fréchet kernel of the data functional with respect to the seismic waveform from which the data functional is derived. The data sensitivity kernel, which is the Fréchet kernel of the data functional with respect to structural model parameters, can be obtained by composing the seismogram perturbation kernel with the Born kernel through the chain rule. In this paper, we extend GSDF analysis to broad-band waveforms by removing constraints on two control parameters defined in Gee & Jordan and derive the seismogram perturbation kernels for the modified GSDF analysis. The modifications given in this paper are consistent with the original GSDF theory in Gee & Jordan around the centre frequency and improve the stability of GSDF analysis towards the edges of the passband. We also present numerical examples of perturbation kernels for the modified GSDF analysis and their data sensitivity kernels using a homogenous half-space structure model and a complex 3-D structure model.Advances in parallel computing technology and numerical methods have made large-scale, 3-D numerical simulations of seismic wavefields much more affordable and they open up the possibility of 'full 3-D tomography' (F3DT), in which the starting model as well as the derived model perturbation is 3-D in space and the Fréchet kernel is computed using the full physics of 3-D wave propagation. Two physically equivalent but computationally distinct approaches to F3DT (Chen et al. 2007a) have been developed, the scattering-integral (SI) method, which sets up the inverse problem by calculating and storing the Fréchet kernels for individual misfit measurements ) and the adjoint-wavefield method, which constructs the gradient of the objective function through correlating the forward wavefield from the source and the adjoint-wavefield from the receivers (Tarantola 1986;Tromp et al. 2005). The first successful application of F3DT using real data from natural earthquakes was conducted in Chen et al. (2007b) to improve the 3-D crustal structure in the Los Angeles Basin region using the SI method. Recently, Tape et al. (2009) has adapted the adjoint-wavefield method to image the crustal structure in Southern California using waveform data from local earthquakes and Fichtner et al. (2009) has adapted the adjoint-wavefield method to continental-scale tomography and inverted for upper-mantle structure in the Australasian region. In these successful F3DT applications, time-and frequencydependent phas...
1 2 Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography 3 technique that can assimilate broadband, multi-component seismic waveform 4 observations into high-resolution 3D subsurface seismic structure models. The main 5 drawback in the current F3DT implementation, in particular the scattering-integral 6 implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead 7 of archiving the 4D space-time wavefields of the receiver-or source-side strain tensors. 8The strain tensor fields are needed for computing the data sensitivity kernels, which are 9 used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In 10 this study, we have successfully integrated a lossy compression algorithm into our F3DT-11 SI workflow to significantly reduce the disk space for storing the strain tensor fields. The 12 compressor supports a user-specified tolerance for bounding the error, and can be 13 integrated into our finite-difference wave-propagation simulation code used for 14 computing the strain fields. The decompressor can be integrated into the kernel 15 calculation code that reads the strain fields from the disk and compute the data sensitivity 16 kernels. During the wave-propagation simulations, we compress the strain fields before 17 writing them to the disk. To compute the data sensitivity kernels, we read the compressed 18 strain fields from the disk and decompress them before using them in kernel calculations. 19Experiments using a realistic dataset in our California statewide F3DT project have 20shown that we can reduce the strain-field disk storage by at least an order of magnitude 21 with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI 22 workflow significantly. The integration of the lossy online compressor may potentially 23 3 open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography 1 practices in the near future. 2 3 4 dimensions are not multiples of four.) The following 6 steps are then conceptually 1 applied to each block independently
We examine the plausibility of using an Artificial Neural Network (ANN) and an Importance-Aided Neural Network (IANN) for the refinement of the structural model used to create full-wave tomography images. Specifically, we apply the machine learning techniques to classifying segments of observed data wave seismograms and synthetic data wave seismograms as either usable for iteratively refining the structural model or not usable for refinement. Segments of observed and synthetic seismograms are considered usable if they are not too different, a heuristic observation made by a human expert, which is considered a match. The use of the ANN and the IANN for classification of the data wave segments removes the human computational cost of the classification process and removes the need for an expert to oversee all such classifications. Our experiments on the seismic data for Southern California have shown this technique to be promising for both classification accuracy and the reduction of the time required to compute the classification of observed data wave segment and synthetic data wave segment matches.
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