New benchmark for earthquake forecasting models shows current neural point processes are not yet suitable.
problem Lack of a modern benchmark for evaluating neural point process models in earthquake forecasting.
method Curated and standardized earthquake catalog, evaluation protocols, and datasets.
result None of the tested NPPs outperformed the classical ETAS model.
Reconstructing Finsler manifolds from sphere data.
problem Recovering a Finsler manifold from sphere data.
method Solving the geometrical inverse problem locally along geodesics.
result Local reconstruction of Finsler manifolds.
Boundary rigidity proven for non-reversible Finsler metrics.
problem Recovering non-reversible Finsler metrics from boundary distance data.
method Sum of reversible Finsler norm and closed 1-form, boundary rigidity results.
result 1-form can be uniquely recovered from boundary distance data.
Paper develops a neural model to assess cascading extreme events.
problem Risk assessment of domino effects like earthquakes and tsunamis.
method Develops a Kolmogorov-Arnold neural network (KANE) framework.
result Estimates the probability of one extreme event triggering another.
Paper reconstructs compact Riemannian manifolds from travel time data.
problem Reconstructing compact Riemannian manifolds from partial travel time data.
method Embedding in function space, studying distance function regularity.
result Reconstruction of compact Riemannian manifolds from travel time data.
New method detects changes in high-dimensional Gaussian data streams.
problem Detecting changes in high-dimensional data streams.
method Likelihood ratio tests across scales and coordinates.
result Patience (null rejection rate) at desired level, response delay under alternative.
EQShapelets detect earthquakes with high accuracy and interpretability.
problem Automated detection and cataloging of earthquakes.
method Time-series shape-based approach embedded in machine learning.
result EQShapelets detected all cataloged and 281 uncataloged events with lower false detection rate.
EikoNet uses deep learning to solve the Eikonal equation quickly and efficiently.
problem Solving the Eikonal equation for first-arrival-time fields in complex 3D structures.
method Grid-free deep learning approach that optimizes network parameters to minimize equation violations.
result EikoNet provides accurate travel time solutions without violating the Eikonal equation.
Researchers reconstruct stiffness tensors from limited data in anisotropic elasticity.
problem Reconstructing stiffness tensors from partial data around one polarization.
method Using algebraic geometry and slowness surfaces, the approach leverages the algebraic geometry of families of slowness surfaces.
result For tensors in a dense open subset, a small amount of data around one polarization uniquely determines the entire slowness surface and stiffness tensor.
Researchers prove a new measure for a financial volatility model.
problem Modeling financial volatility with a Hawkes process.
method Prove existence of equivalent martingale measures for a Heston-Hawkes model.
result Existence of a family of equivalent martingale measures for the model.
Text analytics based on supervised machine learning classifiers has shown great promise in a multitude of domains, but has yet to be applied to Seismology. We test various standard models (Naive Bayes, k-Nearest Neighbors, Support Vector Machines, and Random Forests) on a seismological corpus of 100 articles related to…
Paper develops fast, flexible Hawkes process inference for space-time data.
problem Capturing self-exciting, clustering spatio-temporal data.
method Finite support kernels, discretization, precomputations, ℓ2 gradient-based solver. result Statistically accurate and fast inference for space-time Hawkes processes.
Flexible model tackles high-dimensional, missing data, and stochastic processes.
problem High-dimensional longitudinal data with structured missingness and unknown measurement time points.
method Latent variable model using Gaussian processes and variational autoencoder.
result Competitive performance on simulated and real datasets.
A new model for point processes without intensity function trade-offs.
problem Inefficiency and trade-offs in existing point process models.
method Point Set Diffusion, a diffusion-based latent variable model.
result Achieves state-of-the-art performance in point process generation.
Fast emulators built with neural search accelerate expensive scientific simulations.
problem Slow execution of accurate simulations limits scientific discovery.
method Neural architecture search to build accurate emulators with limited data.
result Simulations accelerated by up to 2 billion times in various scientific fields.
A method to identify new classes of price jumps in financial markets.
problem Separating endogenous and exogenous causes of price jumps.
method Wavelet-based representation of jump time-series.
result Identification of new classes of jumps and investigation of co-jumps.
Researchers reconstruct simple Riemannian manifolds from boundary wave arrival times.
problem Reconstructing Riemannian manifolds from unknown interior sources and arrival times.
method Discrete metric approximation using labeled Gromov--Hausdorff distance.
result Finite-time approximations converge to the true Riemannian manifold.
Bayesian approach models earthquake clustering with spatial mainshocks and aftershocks.
problem Estimating uncertainty in earthquake clustering models due to complex likelihood functions.
method Nonparametric Dirichlet process mixture prior for spatial mainshocks and an auxiliary latent variable routine for efficient inference.
result Efficient Bayesian forecasting of spatial earthquake occurrences with uncertainty quantification.
Paper shows minimum observation time for network recovery.
problem Inferring latent networks from event-based observations.
method Two-stage estimator using clipped and binned event data.
result Observation time of order log(d) is sufficient and necessary.
A new PCA method for analyzing point processes.
problem Analyzing variability in replicated point processes.
method Functional Principal Component Analysis (fPCA) on cumulative mass functions.
result Established convergence and introduced principal measures.
Study on ridge regression in convolutional models shows double descent error behavior.
problem Understanding generalization and estimation error in over-parameterized convolutional models.
method Analysis of ridge estimators for convolutional linear models, derivation of exact error formulae.
result Ridge estimators exhibit double descent error behavior in high-dimensional convolutional models.
Advances in deep learning for spatio-temporal event modeling.
problem Limitations of traditional parametric models in capturing nonstationary dynamics.
method Integration of deep neural architectures to model conditional intensity function and influence kernels.
result Deep influence kernel approach enhances expressiveness and statistical explainability.
New model predicts weekly earthquakes with better tail risk assessment.
problem Violation of Poisson assumption in seismic data.
method Neural network for per-cell overdispersion estimation.
result 8.6% reduction in mean pinball deviation, 12.5% lower CRPS in tail events.
The last decade has seen a flurry of research on all-pairs-similarity-search (or, self-join) for text, DNA, and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. Surprisingly, however, little progress has been made on addressing this problem for time series subseque…
Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthq…
Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is widely employed to detect earthquakes on permanent and temporary seismic networks, and underlies most seismicity catalogs produced around t…
We propose a modified time lag random matrix theory in order to study time lag cross-correlations in multiple time series. We apply the method to 48 world indices, one for each of 48 different countries. We find long-range power-law cross-correlations in the absolute values of returns that quantify risk, and find that …