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Introduction to the Special Section on Seismoacoustics and Seismoacoustic Data Fusion

Bulletin of the Seismological Society of America

Dannemann Dugick, Fransiska K.; Bishop, Jordan W.; Martire, Leo; Iezzi, Alexandra M.; Assink, Jelle D.; Brissaud, Quentin; Arrowsmith, Stephen

This special section of the Bulletin of the Seismological Society of America provides a broad overview on recent advances to the understanding of the seismoacoustic wavefield through 19 articles. Leveraging multiphenomenology datasets is instrumental for the continued success of future planetary missions, nuclear test ban treaty verification, and natural hazard monitoring. Progress in our theoretical understanding of mechanical coupling, advancements in coupled-media wave modeling, and developments of efficient multitechnology inversion procedures are key to fully exploiting geophysical datasets on Earth and beyond. We begin by highlighting papers describing experimental setups and instrumentation, followed by characterization of natural and anthropogenic sources of interest, and ending in new open-access datasets. Finally, we conclude with an overview of challenges that remain as well as some potential directions for future investigation within the growing multidisciplinary field of seismoacoustics.

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TYPE Journal Article YEAR 2023
DOIOSTIScopus

Mallat Scattering Transformation based surrogate for Magnetohydrodynamics

Computational Mechanics

Glinsky, Michael E.; Maupin, Kathryn A.

A Machine and Deep Learning (MLDL) methodology is developed and applied to give a high fidelity, fast surrogate for 2D resistive MagnetoHydroDynamic (MHD) simulations of Magnetic Liner Inertial Fusion (MagLIF) implosions. The resistive MHD code GORGON is used to generate an ensemble of implosions with different liner aspect ratios, initial gas preheat temperatures (that is, different adiabats), and different liner perturbations. The liner density and magnetic field as functions of x, y, and z were generated. The Mallat Scattering Transformation (MST) is taken of the logarithm of both fields and a Principal Components Analysis (PCA) is done on the logarithm of the MST of both fields. The fields are projected onto the PCA vectors and a small number of these PCA vector components are kept. Singular Value Decompositions of the cross correlation of the input parameters to the output logarithm of the MST of the fields, and of the cross correlation of the SVD vector components to the PCA vector components are done. This allows the identification of the PCA vectors vis-a-vis the input parameters. Finally, a Multi Layer Perceptron (MLP) neural network with ReLU activation and a simple three layer encoder/decoder architecture is trained on this dataset to predict the PCA vector components of the fields as a function of time. Details of the implosion, stagnation, and the disassembly are well captured. Examination of the PCA vectors and a permutation importance analysis of the MLP show definitive evidence of an inverse turbulent cascade into a dipole emergent behavior. The orientation of the dipole is set by the initial liner perturbation. The analysis is repeated with a version of the MST which includes phase, called Wavelet Phase Harmonics (WPH). While WPH do not give the physical insight of the MST, they can and are inverted to give field configurations as a function of time, including field-to-field correlations.

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TYPE Journal Article YEAR 2023
DOIOSTIScopus
Results 701–725 of 96,771
Results 701–725 of 96,771