Cover Image

Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-Concept

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Scivier, Sam A., Nissen-Meyer, Tarje, Koelemeijer, Paula, Baydin, Atılım Güneş
Format:	Preprint
Published:	2024
Subjects:	Geophysics Machine Learning Applications
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

EarthquakeNPP: A Benchmark for Earthquake Forecasting with Neural Point Processes
by: Stockman, Samuel, et al.
Published: (2024)

At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition
by: Dahal, Ashok, et al.
Published: (2024)

Analysis and Optimization of Seismic Monitoring Networks with Bayesian Optimal Experiment Design
by: Callahan, Jake, et al.
Published: (2024)

Constructing balanced datasets for predicting failure modes in structural systems under seismic hazards
by: Kim, Jungho, et al.
Published: (2025)

Facies Classification with Copula Entropy
by: Ma, Jian
Published: (2025)

Leveraging generative adversarial networks with spatially adaptive denormalization for multivariate stochastic seismic data inversion
by: Miele, Roberto, et al.
Published: (2025)

Bayesian Event Categorization Matrix Approach for Explosion Monitoring
by: Koermer, Scott, et al.
Published: (2024)

Efficacy of Temporal Fusion Transformers for Runoff Simulation
by: Koya, Sinan Rasiya, et al.
Published: (2025)

Tidal Triggering of Magnitude 7+ Earthquakes by Jupiter
by: Holt, E. W., et al.
Published: (2025)

Scalable Gaussian Process for Learning Non-Ergodic Ground Motion Model from Physics-Based Simulations with Application to Power Infrastructure Assessment
by: Zhao, Jinyan, et al.
Published: (2026)

Fast approximate simulation of seismic waves with deep learning
by: Moseley, Benjamin, et al.
Published: (2018)

Strategic Geosteering Workflow with Uncertainty Quantification and Deep Learning: A Case Study on the Goliat Field
by: Rammay, Muzammil Hussain, et al.
Published: (2022)

Modelling Evolutionary Power Spectral Density Functions of Strong Earthquakes Via Copulas
by: Isaías, Bañales, et al.
Published: (2024)

DISTINGUISH Workflow: A New Paradigm of Dynamic Well Placement Using Generative Machine Learning
by: Alyaev, Sergey, et al.
Published: (2025)

Kahramanmaras-Gaziantep, Turkiye Mw 7.8 Earthquake on February 6, 2023: Preliminary Report on Strong Ground Motion and Building Response Estimations
by: Papazafeiropoulos, George, et al.
Published: (2023)

Recorded Versus Synthetic Spectral-compatible Ground Motions: A Comparative Analysis of Structural Seismic Responses
by: Kim, Jungho, et al.
Published: (2025)

Gemini & Physical World: Large Language Models Can Estimate the Intensity of Earthquake Shaking from Multi-Modal Social Media Posts
by: Mousavi, S. Mostafa, et al.
Published: (2024)

On the Residual-based Neural Network for Unmodeled Distortions in Coordinate Transformation
by: Rofatto, Vinicius Francisco, et al.
Published: (2025)

Diffusion models for multivariate subsurface generation and efficient probabilistic inversion
by: Miele, Roberto, et al.
Published: (2025)

WISE: full-Waveform variational Inference via Subsurface Extensions
by: Yin, Ziyi, et al.
Published: (2023)

Embracing Uncertainty in "Small Data" Problems: Estimating Earthquakes from Historical Anecdotes
by: Glatt-Holtz, Nathan E., et al.
Published: (2021)

Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling
by: Ren, Pu, et al.
Published: (2024)

Double Difference Earthquake Location with Graph Neural Networks
by: McBrearty, Ian W., et al.
Published: (2024)

Leveraging LLMs and Social Media to Understand User Perception of Smartphone-Based Earthquake Early Warnings
by: Wang, Hanjing, et al.
Published: (2026)

Evaluating and Explaining Earthquake-Induced Liquefaction Potential through Multi-Modal Transformers
by: Youwai, Sompote, et al.
Published: (2025)

Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?
by: Riess, Hans, et al.
Published: (2024)

Turkey's Earthquakes: Damage Prediction and Feature Significance Using A Multivariate Analysis
by: Shah, Shrey, et al.
Published: (2024)

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting
by: Jafari, Alireza, et al.
Published: (2024)

Extending the Joint Probability Method to Compound Flooding: Statistical Delineation of Transition Zones and Design Event Selection
by: Bartlett, Mark S., et al.
Published: (2025)

Smooth surface reconstruction of earthquake faults from distributed moment-potency-tensor solutions
by: Sato, Dye SK, et al.
Published: (2025)

Non-parametric kernel density estimation of magnitude distribution for the analysis of seismic hazard posed by anthropogenic seismicity
by: Tong, Francis, et al.
Published: (2025)

Uncertainty quantification for seismic response using dimensionality reduction-based stochastic simulator
by: Kim, Jungho, et al.
Published: (2024)

Residual Kriging for Regional-Scale Canopy Height Mapping: Insights into GEDI-Induced Anisotropies and Sparse Sampling
by: Lahssini, Kamel, et al.
Published: (2025)

Stochastic ecohydrological perspective on semi-distributed rainfall-runoff dynamics
by: Bartlett, Mark S., et al.
Published: (2025)

The EEPAS Model Revisited: Statistical Formalism and a High-Performance, Reproducible Open-Source Framework
by: Chung, Szu-Chi, et al.
Published: (2025)

Risk-based framework to determine climate-informed design storms for road drainage infrastructure
by: Fereshtehpour, Mohammad, et al.
Published: (2025)

Quest for an efficient mathematical and computational method to explore optimal extreme weather modification
by: Sawada, Yohei
Published: (2024)

Entropy-based feature selection for capturing impacts in Earth system models with extreme forcing
by: Watkins, Jerry, et al.
Published: (2024)

Nationwide frequency-dependent seismic site amplification models for Iceland
by: Darzi, Atefe, et al.
Published: (2024)

Extrapolation from historical data cannot reliably predict the time of a potential AMOC collapse
by: Morr, Andreas, et al.
Published: (2026)