Author: Omar Abuassaf

TBA

TBA

TBA

TBA

In our Solar System, there are numerous mean-motion resonances for the minor bodies and satellites, but there are no mean-motion resonances between the planets. The first mean-motion resonance in an extrasolar planetary system – the 2:1 resonance between two Jupiter-mass planets around the star GJ 876 – was discovered in 2001. Since then, an increasing number of pairs of planets in or near mean-motion resonances and resonant chains of three or more planets have been detected. I will discuss the dynamics of these systems and the constraints that they provide on the formation and dynamical evolution of planets. Topics will include high-order mean-motion resonances in the HD 202206 and nu Ophiuchi systems and the formation of resonant chains near the inner edge of protoplanetary disks.

 Venus, Earth’s twin, is not only the closest planet to us in the solar system, it the closest to Earth in mass, density and size and yet it evolved very differently than the Earth. Venus has an atmosphere that has 90 times the surface pressure as the Earth with a surface temperature of 460 C. Venus does not have a system of plate tectonics like the Earth which is one of the key reasons that Earth is a habitable planet. So how did two planets with roughly the same physical parameters evolve so differently? The NASA Magellan mission to Venus in the Early 1990’s used radar to image the planet’s surface through the optically opaque atmosphere at ~150 m resolution and showed that Venus has a young surface that had been volcanically resurfaced with the last 500 million years. As much a Magellan informed us about Venus it also left many key questions about Venus’s planetary evolution unanswered. Two missions to Venus, VERITAS by NASA, and EnVision by ESA in partnership with NASA, will return to Venus in the 2030’s with the goal of answering how these planet’s evolved so differently. The answer to this question will help inform how many Earth and Venus like planets are there in other solar systems. Radars play a key role on each mission and this talk will describe their role in these missions to Venus and how in combination with the other instruments hope to resolve one of the key mysteries in planetary science.  

Our knowledge of the satellite population in the solar system has grown rapidly in the past 100 years.
In the early 1900s almost every known moon was a regular satellite — the large, primordial bodies that formed with their parent planets.
The Voyager flybys fundamentally changed that picture by revealing numerous small inner satellites of the giant planets, bodies likely tied
to ring-system evolution and ongoing collisional processing near the planet. Beginning around 2000, wide-field CCD surveys (e.g. CFHT, Subaru) opened a third population regime: most new discoveries were irregular or outer satellites — dynamically distinct, highly inclined, often retrograde
objects whose origins are not native to the planet system but are best explained as captured heliocentric planetesimals from the early solar system. At JPL, we develop and maintain ephemerides for all known satellites. The orbital models range from simple precessing ellipses to full dynamical models that include tides, relativistic terms, satellite libration, and high order gravity field expansions. These models draw on data sets spanning more than a century of astrometric measurements, from early visual observations to modern spacecraft tracking. Ultimately, satellite ephemerides are not just navigation products needed to point a telescope or fly a spacecraft – they are scientific observables that encode the history and dynamics of entire planetary systems. Each orbit tells a story about its origin, its interactions, and its ongoing evolution.
We will review the current state of satellite ephemerides across the solar system and highlight some interesting dynamical puzzles.

Being able to image the ruptures of moderate earthquakes would significantly increase our observations towards comprehending earthquake source physics, fault properties and seismic hazards.

However, resolving their rupture characteristics remains challenging for conventional seismic networks due to limited station density. The emergence of Distributed Acoustic Sensing (DAS) offers a potential solution by providing dense and continuous measurements. In this study, we systematically evaluate the resolution capabilities of multi-fibre DAS networks for back-projection (BP) and demonstrate the feasibility of using DAS networks through both synthetic tests and analysis of a Mw 4.9 event in the Eastern California Shear Zone.

Furthermore, we propose a two-step inversion procedure that strategically integrates DAS with the conventional network. Our results suggest that strategically deployed multi-fibre DAS network can serve as the next generation of earthquake observation system and significantly enhance our understanding of earthquake rupture physics, as well as seismic risk preparedness.

Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally expensive. Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastodynamic wave equation orders of magnitude faster than conventional methods. Full waveform inversion is a prime beneficiary of the accelerated simulations. Neural operators, as end‐to‐end differentiable operators, combined with automatic differentiation, provide an alternative approach to the adjoint‐state method. State‐of‐the‐art optimization techniques built into PyTorch provide neural operators with greater flexibility to improve the optimization dynamics of full waveform inversion, thereby mitigating cycle‐skipping problems. We demonstrate the application of neural operators for full waveform inversion on real seismic data, using nodal transects collected across the San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area. 

TBA

TBA