Geology topics

Mengyang Gu

Publications and source records attributed to Mengyang Gu.

3 recordsLinked to original sources

USGS2024

Computationally efficient emulation of spheroidal elastic deformation sources using machine learning models: a Gaussian-process-based approach

Elastic continuum mechanical models are widely used to compute deformations due to pressure changes in buried cavities, such as magma reservoirs. In general, analytical models are fast but can be inaccurate as they do not correctly satisfy boundary conditions for many geometries, while numerical models are slow and may require specialized expertise and software. To overcome these limitations, we trained supervised machine learning emulators (model surrogates) based on parallel partial Gaussian processes which predict the output of a finite element numerical model with high fidelity but >1,000× greater computational efficiency. The emulators are based on generalized nondimensional forms of governing equations for finite non‐dipping spheroidal cavities in elastic halfspaces. Either cavity volume change or uniform pressure change boundary conditions can be specified, and the models predict both surface displacements and cavity (pore) compressibility. Because of their computational efficiency, using the emulators as numerical model surrogates can greatly accelerate data inversion algorithms such as those employing Bayesian Markov chain Monte Carlo sampling. The emulators also permit a comprehensive evaluation of how displacements and cavity compressibility vary with geometry and material properties, revealing the limitations of analytical models. Our open‐source emulator code can be utilized without finite element software, is suitable for a wide range of cavity geometries and depths, includes an estimate of uncertainties associated with emulation, and can be used to train new emulators for different source geometries.

JGR Machine Learning and Computation↗

USGS2023

Calibration of imperfect geophysical models by multiple satellite interferograms with measurement bias

Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in the data complicates this task. Satellite interferograms, for instance, are widely used for calibrating geophysical models in geological hazard quantification. In this work, we used satellite interferograms to relate ground deformation observations to the properties of the magma chamber at K i¯lauea Volcano in Hawai‘i. We derived closed-form marginal likelihoods and implemented posterior sampling procedures that simultaneously estimate the model discrepancy of physical models, and the measurement bias from the atmospheric error in satellite interferograms. We found that model calibration by aggregating multiple interferograms and downsampling the pixels in the interferograms can reduce the computation complexity compared to calibration approaches based on multiple data sets. The conditions that lead to no loss of information from data aggregation and downsampling are studied. Simulation illustrates that both discrepancy and measurement bias can be estimated, and real applications demonstrate that modeling both effects helps obtain a reliable estimation of a physical model’s unobserved parameters and enhance its predictive accuracy. We implement the computational tools in the RobustCalibration package available on CRAN.

Technometrics↗

USGS2019

Magma reservoir failure and the onset of caldera collapse at Kīlauea volcano in 2018

Caldera-forming eruptions are among Earths most hazardous natural phenomena, yet the architecture of subcaldera magma reservoirs and the conditions that trigger collapse are poorly understood. Observations from the formation of a 0.8cubic kilometer basaltic caldera at Klauea Volcano in 2018 included the draining of an active lava lake, which provided a window into pressure decrease in the reservoir. We show that failure began after <4% of magma was withdrawn from a shallow reservoir beneath the volcanos summit, reducing its internal pressure by ~17 megapascals. Several cubic kilometers of magma were stored in the reservoir, and only a fraction was withdrawn before the end of the eruption. Thus, caldera formation may begin after withdrawal of only small amounts of magma and may end before source reservoirs are completely evacuated.

Hawaii↗