Joseph Byrnes

Joseph Byrnes serves as Assistant Professor in Earth Systems Science within the School of Natural Sciences and Mathematics at The University of Texas at Dallas, where he leads research in computational seismology and geophysical inversion methods. His work integrates machine learning with seismic data analysis to investigate Earth's deep structure, particularly focusing on lithospheric architecture and mantle dynamics across tectonically active regions worldwide.

His academic foundation includes a PhD in Geophysics from the University of Oregon (2017) and a dual BSc in Physics and Mathematics from Iowa State University (2011).

• PhD Geophysics, University of Oregon (2017)
• BSc Physics and Mathematics, Iowa State University (2011)

Dr. Byrnes' research program centers on computational seismology with three interconnected pillars: (1) developing Bayesian transdimensional inversion frameworks for joint analysis of seismic observables, (2) characterizing lithosphere-asthenosphere system dynamics through shear-wave splitting and attenuation studies, and (3) applying machine learning to denoise seismic data from challenging environments like ocean-bottom sensors. His methodology emphasizes uncertainty quantification in Earth structure models, particularly regarding partial melt distribution and volatile transport in mantle systems.

Analysis of his recent publications reveals dominant trends in joint inversion techniques combining surface/scattered waves, teleseismic data, and electrical observables. Key geographical foci include the Indo-Burman forearc, Southwestern U.S., East African Rift, and Wyoming Craton, with consistent emphasis on how mantle anisotropy and partial melt influence seismic signatures. His 2024-2025 work shows increasing integration of machine learning for noise reduction in ocean-bottom data.

Scientific recognition includes:

• #1 Dad award from Pinecone Preschool (2023)
• AGU Outstanding Student Presentation award (2014)

Dr. Byrnes actively mentors students and secures major research funding, currently leading five active grants totaling over $900,000. His collaborative projects address seismic hazard assessment in Las Vegas, subduction zone geohazards through the SZ4D initiative, lower crustal composition studies, and machine learning algorithms for ocean-bottom seismometer data. The USGS and NSF-funded projects demonstrate strong industry-academic partnerships focused on both fundamental geoscience and practical hazard mitigation.

His research group develops specialized computational tools for seismic data processing, particularly advancing shear-wave splitting techniques for ocean environments and creating denoising algorithms for OBS data. Current projects involve multi-institutional teams across the U.S. and international collaborators studying rift systems and cratonic margins, with future work targeting real-time seismic hazard applications through machine learning integration.