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Magnetic fields have been included in cosmological simulations of galaxy formation only recently, due to advances in numerical techniques and a better understanding of the galaxy formation physics. In this paper, we develop a new subgrid…
Onboard terrain sensing and mapping for safe planetary landings often suffer from missed hazardous features, e.g., small rocks, due to the large observational range and the limited resolution of the obtained terrain data. To this end, this…
In this study, we introduce a novel approach to synthesizing subsurface velocity models using diffusion generative models. Conventional methods rely on extensive, high-quality datasets, which are often inaccessible in subsurface…
The detection of earthquakes is a fundamental prerequisite for seismology and contributes to various research areas, such as forecasting earthquakes and understanding the crust/mantle structure. Recent advances in machine learning…
The downward continuation of potential fields from the Earth's surface into the subsurface is a critical task in gravity exploration, as it helps to identify the sources of gravity anomalies. This problem is often addressed by solving a…
Accurate prediction and synthesis of seismic waveforms are crucial for seismic-hazard assessment and earthquake-resistant infrastructure design. Existing prediction methods, such as ground-motion models and physics-based wave-field…
Seismic intensity prediction from early or initial seismic waves received by a few seismic stations can enhance Earthquake Early Warning (EEW) systems, particularly in ground motion-based approaches like PLUM. While many operational EEW…
We propose a variation on wavefield reconstruction inversion for seismic inversion, which takes advantage of randomized linear algebra as a way to overcome the typical limitations of conventional inversion techniques. Consequently, we can…
The stochastic gradient descent (SGD) algorithm is the algorithm we use to train neural networks. However, it remains poorly understood how the SGD navigates the highly nonlinear and degenerate loss landscape of a neural network. In this…
Spaceborne gravity gradients are proposed in this paper to provide autonomous orbit determination capabilities for near Earth satellites. The gravity gradients contain useful position information which can be extracted by matching the…
Summary of the long term data taking, related to one of the proposed next generation ground-based gravitational detector's location is presented here. Results of seismic and infrasound noise, electromagnetic attenuation and cosmic muon…
The emergence of precision gravity simulators in quantum and fluid systems is opening new avenues for probing curved-spacetime physics and black-hole phenomenology under controlled laboratory conditions. In parallel, advances in…
Dynamic rupture simulations generate synthetic waveforms that account for non-linear source, path, and site complexity. Here, we analyze millions of spatially dense waveforms from 3D dynamic rupture simulations in a novel way to illuminate…
In this article we propose a new method for reducing Newtonian noise in laser-interferometric gravitational-wave detectors located on the Earth's surface. We show that by excavating meter-scale recesses in the ground around the main test…
The nonlinear mechanical responses of rocks and soils to seismic waves play an important role in earthquake physics, influencing ground motion from source to site. Continuous geophysical monitoring, such as ambient noise interferometry, has…
An enhancement in seismic measuring instrumentation has been proven to have implications in the quantity of observed earthquakes, since denser networks usually allow recording more events. However, phenomena such as strong earthquakes or…
An array of seismometers is being developed at the Sanford Underground Laboratory, the former Homestake mine, in South Dakota to study the properties of underground seismic fields and Newtonian noise, and to investigate the possible…
Terrestrial long-baseline atom interferometer experiments are emerging as powerful tools for probing new fundamental physics, including searches for dark matter and gravitational waves. In the frequency range relevant to these signals,…
We introduce \textit{SeismoGPT}, a transformer-based model for forecasting three-component seismic waveforms in the context of future gravitational wave detectors like the Einstein Telescope. The model is trained in an autoregressive…
Stochastic Gradient Descent (SGD) is the workhorse algorithm of deep learning technology. At each step of the training phase, a mini batch of samples is drawn from the training dataset and the weights of the neural network are adjusted…