Subduction, terrane accretion, and continental rifting are fundamental plate tectonic processes. Geologic features such as igneous rocks produced during arc magmatism, terrane boundaries separating regions with different origins, and rift basins filled with sedimentary units reflect such tectonic processes. It is likely

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Summary For over forty years, the Global Centroid-Moment Tensor (GCMT) project has determined location and source parameters for globally recorded earthquakes larger than magnitude 5.0. The GCMT database remains a trusted staple for the geophysical community. Its point-source moment-tensor solutions are the result of inversions that model long-period observed seismic waveforms via normal-mode summation for a one-dimensional (1-D) reference earth model, augmented by path corrections to capture three-dimensional (3-D) variations in surface wave phase speeds, and to account for crustal structure. While this methodology remains essentially unchanged for the ongoing GCMT catalog, source inversions based on waveform modeling in low-resolution three-dimensional (3-D) earth models have revealed small but persistent biases in the standard modeling approach. Keeping pace with the increased capacity and demands of global tomography requires a revised catalog of centroid-moment tensors, automatically and reproducibly computed using Green functions from a state-of-the-art 3-D earth model. In this paper, we modify the current procedure for the full-waveform inversion of seismic traces for the six moment-tensor parameters, centroid latitude, longitude, depth, and centroid time of global earthquakes. We take the GCMT solutions as a point of departure but update them to account for the effects of a heterogeneous earth, using the global three-dimensional wavespeed model GLAD-M25. We generate synthetic seismograms from Green functions computed by the spectral-element method in the 3-D model, select observed seismic data and remove their instrument response, process synthetic and observed data, select segments of observed and synthetic data based on similarity, and invert for new model parameters of the earthquake’s centroid location, time, and moment tensor. The events in our new, preliminary database containing 9,382 global event solutions, called CMT3D for “3-D centroid-moment tensors”, are on average 4 km shallower, about 1 s earlier, about 5 percent larger in scalar moment, and more double-couple in nature than in the GCMT catalog. We discuss in detail the geographical and statistical distributions of the updated solutions, and place them in the context of earlier work. We plan to disseminate our CMT3D solutions via the online ShakeMovie platform.

Receiver functions, an important tool in understanding sub-surface interfaces, can be analysed through carefully implemented neural networks. We demonstrate this approach. Previously, we introduced our receiver function tool set, Pythonic Global Lithospheric Imaging using Earthquake Recordings (PyGLImER). PyGLImER enables us to: [1] create a database of teleseismic event displacement records at worldwide seismic stations, [2] compute receiver functions from these records, and [3] compute volumetric common conversion point (CCP) stacks from the receiver functions and their conversion points. CCP stacking is a standard tool to image the subsurface using receiver functions. The CCP stacks represent rich but large, three-dimensional volumes of data that contain information about discontinuities in Earth's crust and upper mantle. One goal of the interpretation of CCPs is the identification of such discontinuities. Automated picking routines reduce discontinuities to singular peaks and troughs, thus discarding the wealth of information available over the whole depth range, such as integrated discontinuity impedance and regional geometry. However, the obvious alternative, manual picking, is not feasible for large data volumes. Here, we explore the possibility of fully-automated segmentation of 3D CCP volumes through the application of image processing routines and machine learning to successive volume cross-sections. With our picking tool, we manually label discontinuities in CCP slices to serve as training and validation sets.We use these labeled datasets as input to train a convolutional neural network (CNN) to perform pixel-wise identifications in subsurface images. When applied to all slices of the CCP stack, the CNN outputs a fully-segmented 3D model, which furnishes quantitative exploration of subsurface discontinuity morphology. Specifically, we can investigate the thickness/width, intensity, and topography of discontinuities across continents. This information has the potential to improve our understanding of, e.g., mantle transition zone phase transitions and, therefore, mantle dynamics.