This work is devoted to the study of modeling geophysical and financial time series. A class of volatility models with time-varying parameters is presented to forecast the volatility of time series in a stationary environment. The modeling of stationary time series with consistent properties facilitates prediction with…
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48 results for “Geophysical parameter estimation”
Sequential geophysical and flow inversion to characterize fracture networks in subsurface systemscs.CE
Subsurface applications including geothermal, geological carbon sequestration, oil and gas, etc., typically involve maximizing either the extraction of energy or the storage of fluids. Characterizing the subsurface is extremely complex due to heterogeneity and anisotropy. Due to this complexity, there are uncertainties…
Neural architecture improves geophysical data assimilation with uncertainty quantification.
problem Improving geophysical data interpolation with uncertainty quantification.
method Neural variational data assimilation with SPDE priors.
result Demonstrated improved performance and uncertainty quantification.
Discovering state-parameter mappings in subsurface models using generative adversarial networksphysics.data-an
A fundamental problem in geophysical modeling is related to the identification and approximation of causal structures among physical processes. However, resolving the bidirectional mappings between physical parameters and model state variables (i.e., solving the forward and inverse problems) is challenging, especially …
Bayesian Neural Networks improve geophysical model ensembles with reduced uncertainty.
problem Improving geophysical model projections and uncertainty quantification.
method Developed a Bayesian Neural Network ensemble strategy for geophysical models.
result Bayesian Neural Network ensemble outperforms existing methods in ozone prediction.
Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methodsphysics.ao-ph
Kernel methods improve geophysical forecasting accuracy and efficiency.
problem Improving geophysical forecasting models for accuracy and efficiency.
method Data-driven modeling of geophysical processes using kernel flows for faster, more accurate predictions.
result Kernel methods outperform neural networks and classical models in geophysical forecasting.
Physics-constrained deep learning predicts geophysical dynamics with boundedness.
problem Forecasting geophysical systems with hidden variables and incomplete observations.
method Physics-constrained neural ordinary differential equation (NODE) representations with boundedness constraints.
result The approach generalizes learned dynamics to arbitrary initial conditions.
Deep learning improves PS pixel selection in SAR interferometry.
problem Selecting persistent scatterer pixels for geophysical parameter estimation in multi-temporal SAR interferometry.
method Proposed two deep learning architectures: CNN-ISS and CLSTM-ISS trained on phase history to classify PS and non-PS pixels.
result CLSTM-ISS outperforms conventional methods in PS pixel selection and classification accuracy.
Seismic inversion improved using semi-supervised sequence modeling.
problem Lack of geophysical constraints in machine learning seismic inversion.
method Semi-supervised sequence modeling with recurrent neural networks.
result Achieved 98% correlation between estimated and target elastic impedance.
SDA method reduces memory and time for assimilating noisy geophysical data.
problem Challenges in identifying state trajectories of high-dimensional geophysical systems.
method Score-based data assimilation with modified score network architecture.
result Promising results for a two-layer quasi-geostrophic model.
Dual neural networks tackle uncertainty in geophysical data.
problem Quantifying and separating epistemic and aleatoric uncertainties in geophysical data.
method Combination of Bayesian Neural Network (BNN) and Artificial Neural Network (ANN).
result Reduces uncertainties in rock and fluid property estimation for better reservoir optimization.
The paper calibrates geophysical predictions using marginal distributions and machine learning.
problem Sensitivity to initial conditions in geophysical systems leads to large deviations in long-term forecasts.
method The method introduces a calibration algorithm based on normalization and Kernelized Stein Discrepancy (KSD) to enhance ML predictions.
result The method improves the fidelity of ML predictions to known physical distributions, ensuring consistency with non-local statistical structures.
Model parameter estimation using coherent structure coloringphysics.flu-dyn
Lagrangian data assimilation is a complex problem in oceanic and atmospheric modeling. Tracking drifters in large-scale geophysical flows can involve uncertainty in drifter location, complex inertial effects, and other factors which make comparing them to simulated Lagrangian trajectories from numerical models extremel…
Physics-consistent method improves seismic inversion accuracy.
problem Challenges in seismic full-waveform inversion (FWI) due to ill-posedness and high cost.
method Hybrid approach combining physics-based models with data-driven methodologies, incorporating physics into data augmentation.
result Physics-consistent data-driven inversion yields higher accuracy and better generalization.
Deep models memorize training data in geophysical inversion, leading to biased posterior distributions.
problem Memorization of training data biases learned priors in geophysical inverse problems.
method Casting generative models' training as maximum likelihood, we show memorization results in a reweighted empirical distribution for diffusion models, leading to Gaussian mixture priors and posteriors.
result Memorization leads to posterior distributions that are likelihood-weighted lookup among stored training examples, affecting full waveform inversion outcomes.
Study uses neural fields to improve geophysical inversions by reducing artifacts.
problem Improving geophysical inversions by reducing artifacts and improving model recovery.
method Employing neural fields for test-time learning in geophysical inversions.
result Test-time learning with neural fields eliminates unwanted artifacts in recovered models.
Improved variational inference for geophysical inverse problems with data correction.
problem High computational cost and accuracy issues in Bayesian inference for geophysical inverse problems.
method Amortized variational inference with latent distribution correction using physics-based priors.
result Improved robustness of amortized variational inference under data distribution shifts.
TSCoNet forecasts correlated geophysical fields with uncertainty estimates.
problem Accurate and reliable forecasts of correlated geophysical fields across many locations.
method Two-stage CNN-LSTM coupled with Gaussian copula.
result Calibrated prediction intervals without sacrificing point accuracy.
Geostatistical learning faces unique challenges due to spatial correlation and covariate shifts.
problem Challenges in applying statistical learning to geospatial data.
method Assessing generalization error under covariate shift and spatial correlation.
result No classical learning methods are adequate for model selection in geospatial contexts.
Gaussian Processes improve geoscience data analysis.
problem Improving function approximation in geoscience.
method Review and development of new Gaussian Process algorithms.
result Automatic feature ranking and uncertainty intervals.
Neural network enhances seismic imaging in salt-prone areas.
problem Improving velocity model building for faster FWI convergence.
method 3D convolutional, de-convolutional, and max-pooling neural network architecture with data augmentations and regularization.
result Proposed neural network generates salt body probability cubes for FWI regularization.
Fractal Flow enhances normalizing flows with interpretable latent space and hierarchical modeling.
problem High-dimensional density estimation and generative modeling challenges.
method Integrates topic modeling (LDA) and fractal strategy into normalizing flows.
result Achieves latent clustering, controllable generation, and superior estimation accuracy.
Order patterns, their variation and change points in financial time series and Brownian motionq-fin.ST
Study of financial time series and Brownian motion using order patterns and permutation entropy.
problem Analyzing order patterns and variation in financial time series and Brownian motion.
method Use of order patterns and permutation entropy to study financial data and Brownian motion, focusing on turning rate and up-down balance.
result For small lags, pattern frequencies in financial data remain constant. Up-down balance is better for change points in financial data.
Automated quality control for seismic data reduces human labor and time.
problem Costly and time-consuming manual QC of seismic data.
method Active learning to select and label relevant seismic data.
result Active learning technique reduces QC time and improves accuracy.
We present a framework for describing the evolution of stochastic observables having a non-stationary distribution of values. The framework is applied to empirical volume-prices from assets traded at the New York stock exchange. Using Kullback-Leibler divergence we evaluate the best model out from four biparametric mod…
Review of diffusion models for SBI in non-ideal data scenarios.
problem Inference of parameters from complex simulation outputs with intractable likelihoods.
method Diffusion models for likelihood-free inference, addressing model misspecification, unstructured observations, and missing data.
result Improved robustness and efficiency in SBI methods for non-ideal data scenarios.
How to self-localize large teams of underwater nodes using only noisy range measurements? How to do it in a distributed way, and incorporating dynamics into the problem? How to reject outliers and produce trustworthy position estimates? The stringent acoustic communication channel and the accuracy needs of our geophysi…
A new method simplifies variational inference for complex models.
problem Challenges in exact Bayesian inference due to intractable integrals.
method Rewriting the lower bound on model log-likelihood using a finite sample of Gaussian latent variables.
result Demonstrated effectiveness on synthetic and real-world examples.
Method improves clarity in forecasting spatio-temporal data.
problem Forecasting spatio-temporal data with clarity and interpretability.
method Supervised semi-nonnegative matrix factorization with frequency regularization.
result Method offers clearer interpretability in forecasting spatio-temporal data.
Hyperbolic conservation laws posed on manifolds arise in many applications to geophysical flows and general relativity. Recent work by the author and his collaborators attempts to set the foundations for a study of weak solutions defined on Riemannian or Lorentzian manifolds and includes an investigation of the existen…
Bayesian method combines data assimilation, machine learning, and EM for chaotic dynamics.
problem Reconstructing high-dimensional chaotic dynamics from noisy, partial observations over long time series.
method Bayesian inference using expectation-maximization and coordinate descent.
result Successfully tested on two chaotic models, estimating model, state trajectory, and model error statistics.
Accurate goodness-of-fit tests for the extreme tails of empirical distributions is a very important issue, relevant in many contexts, including geophysics, insurance, and finance. We have derived exact asymptotic results for a generalization of the large-sample Kolmogorov-Smirnov test, well suited to testing these extr…
We study ray transforms on spherically symmetric manifolds with a piecewise metric. Assuming the Herglotz condition, the X-ray transform is injective on the space of functions on such manifolds. We also prove injectivity results for broken ray transforms (with and without periodicity) on such manifolds …
The exponentially truncated q-distribution: A generalized distribution for real complex systemscond-mat.stat-mech
To know the statistical distribution of a variable is an important problem in management of resources. Distributions of the power law type are observed in many real systems. However power law distributions have an infinite variance and thus can not be used as a standard distribution. Normally professionals in the area …
Recovering the isometry type of a Riemannian manifold from local boundary diffraction travel timesmath.AP
We analyze the inverse problem, originally formulated by Dix in geophysics, of reconstructing the wave speed inside a domain from boundary measurements associated with the single scattering of seismic waves. We consider a domain with a varying and possibly anisotropic wave speed which we model as a Riemannia…
This paper tackles learning functions on manifolds using parallel distributed learning.
problem Learning real-valued functions on manifolds from input-output data pairs.
method Filtered hyperinterpolation and parallel distributed learning.
result Optimal approximation order for non-distributed case, and quantitative relations for distributed case.
Dynamics of a financial market index after a crashcond-mat.stat-mech
We discuss the statistical properties of index returns in a financial market just after a major market crash. The observed non-stationary behavior of index returns is characterized in terms of the exceedances over a given threshold. This characterization is analogous to the Omori law originally observed in geophysics. …
Power law relaxation in a complex system: Omori law after a financial market crashcond-mat.stat-mech
We study the relaxation dynamics of a financial market just after the occurrence of a crash by investigating the number of times the absolute value of an index return is exceeding a given threshold value. We show that the empirical observation of a power law evolution of the number of events exceeding the selected thre…
We show that there is a common mode of origin for the power laws observed in two different models: (i) the Pareto law for the distribution of money among the agents with random saving propensities in an ideal gas-like market model and (ii) the Gutenberg-Richter law for the distribution of overlaps in a fractal-overlap …
The relaxation dynamics of aftershocks after large volatility shocks are investigated based on two high-frequency data sets of the Shanghai Stock Exchange Composite (SSEC) index. Compared with previous relevant work, we have defined main financial shocks based on large volatilities rather than large crashes. We find th…
Novel method uses deep generative models for efficient Bayesian inverse problem solving.
problem Efficiently solving inverse problems with large, discrete fields and limited prior information.
method Bayesian inference with deep generative models in low-dimensional latent space.
result Accurate and reliable uncertainty estimates for large-scale inverse problems.
Geometric integrator preserves coadjoint orbits in dissipative systems.
problem Preserving coadjoint orbits in dissipative mechanical systems.
method Adapted discrete variational integrators for forced Euler-Poincaré and Lie-Poisson systems.
result Preserves coadjoint orbits exactly, improving over general-purpose methods.
One of the most important applications of seismic reflection is the hydrocarbon exploration which is closely related to salt deposits analysis. This problem is very important even nowadays due to it's non-linear nature. Taking into account the recent developments in deep learning networks TGS-NOPEC Geophysical Company …
We propose a new learning-based approach to solve ill-posed inverse problems in imaging. We address the case where ground truth training samples are rare and the problem is severely ill-posed - both because of the underlying physics and because we can only get few measurements. This setting is common in geophysical ima…
CGAN fails to improve deterministic sequence predictions, revealing a theoretical limitation.
problem Improving deterministic sequence predictions with CGAN.
method Developed an adversarial content loss approach.
result CGAN does not improve deterministic sequence predictions.
The paper calculates curvature of quantomorphism group and its relation to QG equations.
problem Computing curvature of quantomorphism group and its implications.
method Explicit formula derivation using Arnold's formula for curvature of geodesic equations.
result Curvature of quantomorphism group is nonpositive for certain conditions.
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation dat…
Uncertainty Quantification in Seismic Inversion Through Integrated Importance Sampling and Ensemble Methodsphysics.geo-ph
This study improves uncertainty quantification in seismic inversion.
problem Uncertainty in seismic inversion due to limited data and model diversity.
method Integrates ensemble methods with importance sampling.
result More accurate uncertainty quantification in velocity models.