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Exascale simulations underpin quake-resistant infrastructure designs

Phys.org: Earth science - Tue, 08/19/2025 - 05:53

Simulations still can't predict exactly when an earthquake will happen, but with the incredible processing power of modern exascale supercomputers, they can now predict how they will happen and how much damage they will likely cause.

Grand Canyon's Dragon Bravo megafire shows the growing wildfire threat to water systems

Phys.org: Earth science - Tue, 08/19/2025 - 01:00

As wildfire crews battled the Dragon Bravo Fire on the Grand Canyon's North Rim in July 2025, the air turned toxic.

PoViT-UQ: P-wave Polarity and Arrival Time Determination using Vision Transformer with Uncertainty Quantification

Geophysical Journal International - Tue, 08/19/2025 - 00:00

SummaryDetermining earthquake focal mechanisms is essential for understanding fault geometry and the stress field in the Earth's crust. When focal mechanisms are estimated using P-wave first-motion polarities, accurate polarity determination is critical. In recent years, deep-learning-based polarity-determination models have been developed. However, the estimation of focal mechanisms using P-wave polarities is often not robust. When automating this process using deep learning models, it is crucial to identify and utilize only those polarity predictions that the model infers with high accuracy and low uncertainty. In this study, we propose a novel deep learning model, PoViT-UQ, that combines a Vision Transformer (ViT) with Monte Carlo Dropout (MCD) to estimate high-precision initial P-wave polarity classification and arrival time detection with uncertainty quantification. Using seismic waveform data sampled at 100 and 250 Hz, the model classifies polarities into three classes (Up, Down, and Noise) and simultaneously estimates P-wave arrival times. The results showed a classification accuracy exceeding 98% and a standard deviation of 0.027 s in the arrival time estimation using the 250 Hz model. By integrating MCD, we evaluate prediction uncertainty and apply an interquartile range threshold of ≤0.15 to improve the accuracy of focal mechanism estimates. Validation using aftershock data from the 2016 Central Tottori Earthquake confirmed that our approach contributes to efficient and high-precision focal mechanism estimates. Our model advances automated initial P-wave polarity determination and enables reliable data selection based on uncertainty quantification.

Surface core flow dynamic pressure estimation

Geophysical Journal International - Tue, 08/19/2025 - 00:00

SummaryThe flows within Earth’s fluid outer core push and pull on the core-mantle boundary (CMB) through dynamic pressure variations, potentially leading to deformation of the CMB. It is therefore crucial to obtain a realistic estimate of the pressure associated with flows within the fluid core. In many studies, it is commonly assumed that the flow tangent to the CMB is in balance between Coriolis and pressure gradient forces, known as a tangentially geostrophic (TG) flow. A static pressure field is thereby associated kinematically to the flow field at the core’s surface. We run direct numeric simulations of the magnetohydrodynamic equations in the Boussinesq approximation that can solve for the pressure field and allow for a comparison between a fully dynamic solution and the TG pressure estimate. An excellent agreement between the two pressure fields is found for a steady image of the core surface dynamics. However, the performance of the TG pressure estimate is not without limitations. Although it effectively captures most of the temporal dynamics associated with the fluid flow, discrepancies arise, particularly near the equator and for rapid changes in flow dynamics.

Discovery of hidden faults sheds light on mystery of 'slow earthquakes'

Phys.org: Earth science - Mon, 08/18/2025 - 20:30

Scientists have uncovered a key piece of the puzzle behind the unusual "slow earthquakes" occurring off the east coast of New Zealand's North Island.

Researcher: We can build safer tunnels with artificial intelligence

Phys.org: Earth science - Mon, 08/18/2025 - 20:16

Every day, new tunnels are being built through rock across the country. The completed tunnels are safe, but the construction phase presents challenges.