Feed aggregator

An iterative weighted least square fitting method for crustal anisotropy using receiver functions

Geophysical Journal International - Fri, 06/14/2024 - 00:00

SummaryThe harmonic variation of the P-to-S converted phases (i.e. Pms) observed from receiver functions (RFs) includes information on crustal azimuthal anisotropy. However, this harmonic analysis is easily influenced by low-quality RF traces, and the measurements may be misleading. Here, we propose an improved method, named the Iterative Weighted Least-Square method (IWLS), to extract the splitting parameters of the crust and simultaneously retrieve the two-lobed and four-lobed components of back-azimuthal variation. The quality and weights of different RF traces are estimated properly in the IWLS method. The weight function is related to the sharpness of the Pms phase and the smearing of other signals. We conduct many synthetic tests, and the IWLS method provides stable measurements for poor back-azimuthal coverage, strong noise, weak P-wave azimuthal anisotropy, and multiple anisotropic layers. We apply the IWLS method to observational data from two temporary stations on the southeastern Tibetan Plateau and North China Craton, respectively. The measurements are comparable to previous results and provide insight into crustal deformation.

Graph-learning approach to combine multiresolution seismic velocity models

Geophysical Journal International - Fri, 06/14/2024 - 00:00

SummaryThe resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations (e.g., Yeh and Olsen, 2023). Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray-path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated travel times along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Simulation of gravity field estimation of Phobos for Martian Moon eXploration (MMX)

Earth,Planets and Space - Fri, 06/14/2024 - 00:00

We study the dynamic orbit about Phobos of the Martian Moon eXploration (MMX) spacecraft and simulate gravity field estimation using Doppler, image landmarks, and LIght Detection And Ranging (LIDAR) data based...

High-Precision Microseismic Source Localization Using a Fusion Network Combining Convolutional Neural Network and Transformer

Surveys in Geophysics - Fri, 06/14/2024 - 00:00

Abstract

Microseismic source localization methods with deep learning can directly predict the source location from recorded microseismic data, showing remarkably high accuracy and efficiency. Two main categories of deep learning-based localization methods are coordinate prediction methods and heatmap prediction methods. Coordinate prediction methods provide only a source coordinate and generally do not provide a measure of confidence in the source location. Heatmap prediction methods require the assumption that the microseismic source is located on a grid point. Thus, they tend to provide lower resolution information and localization results may lose precision. This study reviews and compares previous methods for locating the source based on deep learning. To address the limitations of existing methods, we devise a network fusing a convolutional neural network and a Transformer to locate microseismic sources. We first introduce the multi-modal heatmap combining the Gaussian heatmap and the offset coefficient map to represent the source location. The offset coefficients are utilized to correct the source locations predicted by the Gaussian heatmap so that the source is no longer confined to the grid point. We then propose a fusion network to accurately estimate the source location. A gated multi-scale feature fusion module is developed to efficiently fuse features from different branches. Experiments on synthetic and field data demonstrate that the proposed method yields highly accurate localization results. A comprehensive comparison of coordinate prediction method and heatmap prediction methods with our proposed method demonstrates that the proposed method outperforms the other methods.

Categories:

NASA satellite returns image of sea ice swirls

Phys.org: Earth science - Thu, 06/13/2024 - 20:44

NASA's Terra satellite captured floating fragments of sea ice as ocean currents carried them south along Greenland's east coast on June 4, 2024.