Jiajia Sun

Associate Professor

University of Houston

United States

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About

Dr. Jiajia Sun is an Associate Professor at the University of Houston, USA. His research primarily focuses on geophysical measurements, Bayesian inversion techniques, and machine learning applications in geosciences. Recent publications include work on natural hydrogen exploration, integration of neural networks with gravity data, and characterizing marine magnetic anomalies.

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2025

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GERMANY

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Jiajia Sun

Research Interests

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About

Articles (18)

Characterizing Marine Magnetic Anomalies: A Machine Learning Approach to Advancing the Understanding of Oceanic Crust Formation

Natural hydrogen exploration by joint sparse inversion of geophysical measurements and integrated geological interpretation

Generalized Transitional Markov Chain Monte Carlo Sampling Technique for Bayesian Inversion of Electromagnetic Data

Variational inference for geophysical Bayesian inverse problems using normalizing flows: an unsupervised approach to electromagnetic data inversion

Integration of PSPU-Net gravity inversion neural network with gravity data for enhanced 3D basement relief estimation

Three-dimensional cooperative inversion of airborne magnetic and gravity gradient data using deep-learning techniques

3D Monte Carlo geometry inversion using gravity data

Reconstruction of Multiple Target Bodies Using Trans-Dimensional Bayesian Inversion With Different Constraints

A deep learning-enhanced framework for multiphysics joint inversion

Understanding total variation regularization: Why can it recover dipping structures?

Mapping critical mineral resources using airborne geophysics, 3D joint inversion and geology differentiation: A case study of a buried niobium deposit in the Elk Creek carbonatite, Nebraska, USA

Quantifying uncertainty of salt body shapes recovered from gravity data using trans-dimensional Markov chain Monte Carlo sampling

3D probabilistic geology differentiation based on airborne geophysics, mixed Lp norm joint inversion, and physical property measurements

Towards a better understanding of the recoverability of physical property relationships from geophysical inversions of multiple potential-field data sets

Uncertainty analysis of 3D potential-field deterministic inversion using mixed Lp norms

Research Note: Recovering sparse models in 3D potential‐field inversion without bound dependence or staircasing problems using a mixed Lp norm regularization

Unveiling the 3D undercover structure of a Precambrian intrusive complex by integrating airborne magnetic and gravity gradient data into 3D quasi-geology model building

Predicting Magnetization Directions Using Convolutional Neural Networks

Collaborators (5)

Xuqing Wu

Jiefu Chen

William Sager

Xin Fu

Rodolfo Christiansen

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