The location and origin of Neoproterozoic‐Cambrian sutures provide keys to understand the formation and evolution of the supercontinent Gondwana. The Larsemann Hills is located near a major Neoproterozoic‐Cambrian suture zone in the Prydz Belt, but has not been examined locally by comprehensive geophysical studies. In this study, we analyzed data collected from a one‐dimensional (1D) joint seismic‐MT array deployed during the 36th Chinese National Antarctic Research Expedition. We found that a sharp Moho discontinuity offset of 6–8 km shows up in the stacked image of teleseismic P‐wave receiver function analysis; coinciding with the abrupt Moho offset, a near‐vertical channel with (a) low resistivity extending to the uppermost mantle depths, and (b) high crustal Poisson's ratio in the crust is identified. These findings provide evidence for the determination of the location and collisional nature of the Prydz belt or a portion of it.

Ambient noise recorded by a dense seismic array provides an opportunity to resolve detailed 3D shear wave velocity (Vs) structures. We utilize the frequency‐Bessel transform (F‐J) method to compute the dispersion spectrogram for ambient noise recorded by the Long Beach seismic array, extract multimodal dispersion curves with a deep learning approach, and invert multimodal dispersion curves with a gradient algorithm. The addition of higher‐mode dispersion curves reduces nonuniqueness and provides an improved high‐resolution 3D Vs model that shows good correlations with the known stratigraphic sequence and geologic features in the study area. Furthermore, the Richard fault between the Pacific Coast Highway and Newport‐Inglewood faults, which have never been discussed in previous studies, is resolved. Our study demonstrates the potential of using the F‐J method to extract multimodal dispersion curves and consequently retrieve an improved high‐resolution 3D Vs model from a dense seismic array.

The ice–bedrock interfaces at the bottoms of ice sheets and the internal ice layers record the historical evolution of the ice sheets and are important indicators for inferring glacier dynamics and explaining subglacial topographies. Radar-detected bedrock and internal ice layers can be used to observe conditions in the interiors and bottoms of ice sheets. The low contrast of the internal layers’ feature in radar images, as well as the effects of ice flow, makes it challenging to automatically extract and trace the layers and interfaces of ice sheets. Manual or semiautomatic methods are extensively applied in bedrock and internal ice layers’ extractions. However, the conventional methods require large amounts of time. Especially when processing a large number of radar observation data, the accuracy and efficiency of conventional methods are untoward. Therefore, we propose EisNet, a fusion system consisting of deep neural networks, to extract the multiple types of internal layers. Because of the rareness of high-precision extraction of ice layers, it is virtually impossible to train EisNet in observational data with the corresponded label of ice layers. To train EisNet, we design a novel synthetic method for radar images based on artifact noise and characteristic textures that are similar to those in the observation data. The validation in synthetic data proves EisNet’s capacity for layer extraction and noise removal. The field data application shows the generalization and adaptation abilities of EisNet and indicates that EisNet can significantly improve the efficiency of the automatic processing of ice sheet radiostratigraphy.

We present a scheme for multiscale phase inversion (MPI) of seismic data that is less sensitive than full-waveform inversion (FWI) to the unmodeled physics of wave propagation and to a poor starting model. To avoid cycle skipping, the multiscale strategy temporally integrates the traces several times, i.e., high-order integration, to produce low-boost seismograms that are used as input data for the initial iterations of MPI. As the iterations proceed, lower frequencies in the data are boosted by using integrated traces of lower order as the input data. The input data are also filtered into different narrow frequency bands for the MPI implementation. Numerical results with synthetic acoustic data indicate that, for the Marmousi model, MPI is more robust than conventional multiscale FWI when the initial model is moderately far from the true model. Results from synthetic viscoacoustic and elastic data indicate that MPI is less sensitive than FWI to some of the unmodeled physics. Inversion of marine data indicates that MPI is more robust and produces modestly more accurate results than FWI for this data set.

Full-waveform inversion of seismic data is often plagued by cycle-skipping problems such that an iterative optimization method often gets stuck in a local minimum. To avoid this problem, we simplify the objective function so that the iterative solution can quickly converge to a solution in the vicinity of the global minimum. The objective function is simplified by only using parsimonious and important portions of the data, which are defined as skeletonized data. We have developed a mostly nonmathematical tutorial that explains the theory of wave-equation inversion of skeletonized data. We also demonstrate its effectiveness with examples.