A new method uses SVMs and active learning for efficient fragility curve estimation.
problem Estimating fragility curves for structures under seismic and other excitations.
method Support Vector Machines (SVMs) coupled with active learning algorithm.
result Efficient estimation of fragility curves with reduced numerical calculations.
Active learning method optimizes seismic fragility curve estimation.
problem Optimizing calls to complex numerical models for fragility curve estimation.
method Importance sampling based active learning for parametric seismic fragility curve estimation.
result The method optimizes the estimation of fragility curves with mathematical rigor.
A framework for the generation of bridge-specific fragility utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and…
Transfer learning framework for fragility modeling under domain shift and class imbalance
problem Data gaps in structural fragility modeling
method Transfer learning
result Improves failure detection and predictive stability in low-data regimes
Deep learning model improves seismic rock property estimation.
problem Estimating reservoir rock properties from seismic reflection data.
method Proposes a deep learning-based seismic inversion workflow that models seismic traces spatiotemporally.
result Achieves best performance on SEAM dataset with r2 coefficient of 79.77\% 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.
Bayesian approach estimates sub-resolution reservoir properties from seismic data.
problem Estimating sub-resolution reservoir properties from seismic data.
method Bayesian evidential learning approach, direct relation between seismic data and reservoir properties.
result Efficient estimation of reservoir properties with uncertainty quantification.
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.
Novel method uses U-net for seismic data reconstruction without large datasets.
problem Reconstruction of seismic data with missing traces.
method Unsupervised learning with U-net exploiting deep seismic prior.
result DSPRecon algorithm outperforms SSA and Cadzow methods in reconstruction performance.
Seismic inversion method uses GAN to improve efficiency and accuracy.
problem Difficulty in combining geological knowledge with seismic data and assessing uncertainty.
method Generative Adversarial Network (GAN) for seismic inversion.
result GAN-generated models conform to observation data with low uncertainty.
A deep learning model improves seismic noise reduction.
problem Seismic noise attenuation in pre-stack data processing.
method N2N-Seismic model based on residual neural networks.
result The model significantly outperforms conventional approaches in noise reduction metrics.
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurface attributes makes the classification task difficult. In this context,…
Optimizes seismic monitoring networks using Bayesian OED.
problem Improve seismic event identification and location.
method Bayesian optimal experimental design (OED) to configure sensor networks.
result Optimized sensor network improves seismic event identification and location.
Acquisition cost is a crucial bottleneck for seismic workflows, and low-rank formulations for data interpolation allow practitioners to `fill in' data volumes from critically subsampled data acquired in the field. Tremendous size of seismic data volumes required for seismic processing remains a major challenge for thes…
Study uses machine learning to optimize seismic design parameters.
problem Optimizing seismic design parameters for performance-based design.
method Implementing explainable machine learning models to map design variables and performance metrics, integrated into a genetic optimization algorithm.
result Highly accurate surrogate models (R2> 90%) across diverse building types and hazards, identifying optimal member properties.
New method uses CNN for seismic inversion uncertainty quantification.
problem Uncertainty quantification in seismic inversion for noisy data.
method Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) with CNN denoiser.
result High-resolution, trustworthy posterior samples for subsurface structures.
This document describes an approach to the problem of predicting dangerous seismic events in active coal mines up to 8 hours in advance. It was developed as a part of the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines. The solutions presented consist of ensembles of various pred…
A new method uses denoising diffusion models to improve seismic data interpolation.
problem Improving the accuracy of seismic data interpolation to enhance imaging and interpretation.
method The approach combines denoising diffusion probabilistic models with coherence-corrected resampling strategies.
result The proposed method achieves superior performance and generalization to various missing patterns and noise levels.
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…
Quantum physics model uses knot theory for fragile topology.
problem Modeling quantum physics' fragile topology.
method Knot theoretic algorithm.
result Quantum physics' fragile topology modeled.
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.
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.
Study constructs balanced datasets for seismic failure prediction.
problem Imbalanced datasets limit machine learning performance in seismic failure prediction.
method Framework with three steps: GMF identification, probability density estimation, and sample transformation.
result Framework improves machine learning performance in seismic failure mode prediction.
Research on unique continuation principles in medical and seismic imaging.
problem Understanding unique continuation principles for inverse problems.
method Integral geometry and fractional calculus methods applied to various imaging problems.
result Developed new techniques for solving inverse problems with partial data.
New dataset for seismic interpretation improves machine learning applications.
problem Insufficient high-quality labeled seismic data for machine learning.
method Publicly available dataset with 190,000 labeled images for seismic interpretation.
result Deep learning applications using the dataset produced compelling results.
SAGE generates subsurface velocity models from sparse well logs and seismic images.
problem Lack of high-quality subsurface velocity models due to limited data availability.
method Subsurface AI-driven geostatistical extraction using proxy posterior.
result SAGE produces geologically plausible and statistically accurate velocity realizations.
Deep learning improves salt deposits segmentation in seismic data.
problem Segmenting salt deposits in seismic reflection data for hydrocarbon exploration.
method A novel deep learning approach combining U-Net with ResNeXt-50 encoder, Spatial-Channel Squeeze & Excitation, Lovasz loss, CoordConv, and Hypercolumn methods.
result Achieved 27th place in Kaggle competition for salt deposits segmentation.
Study combines VICReg and TNC for better encoding of non-stationary seismic signals.
problem Ineffective self-supervised learning on non-stationary time series.
method Combines VICReg and Temporal Neighborhood Coding (TNC).
result Effective for self-supervised learning on non-stationary seismic signals.
Deep learning calibrates CO2 storage formations from seismic and well data.
problem Uncertainty in CO2 storage formation properties.
method Two deep learning models for well and seismic data, integrated into MCMC history matching.
result Significant uncertainty reduction in key parameters and accurate CO2 plume predictions.
Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based on pre-stack seismic measurements. Our detection method employs a…
Paper uses GAN to generate synthetic seismic data for earthquake detection.
problem Challenges in detecting earthquake events from seismic time series data.
method Generative Adversarial Network (GAN) to generate synthetic seismic data.
result GAN-generated synthetic seismic data significantly improves earthquake detection accuracy.
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.
New neural network learns seismic horizon locations from few images.
problem Automated detection of seismic horizons from small patches is inefficient.
method Multi-resolution U-net with projected loss-function for non-linear regression.
result Network accurately predicts horizon locations even far from known areas.
Bayesian deep learning improves seismic imaging uncertainty.
problem Uncertainty in seismic imaging due to data noise and linearization errors.
method Combines Bayesian inference and deep neural networks to quantify uncertainty in horizon tracking.
result Uncertainty in automatically tracked horizons can be quantified and visualized.
New research investigates why influence functions are fragile and proposes new validation procedures.
problem Understanding and mitigating the fragility of influence functions in deep learning model explanations.
method Verification of influence functions using various conditions and procedures, including convexity and non-convexity.
result Validation procedures may cause the observed fragility of influence functions.
We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using sparsity optimization…
Complex-valued neural networks improve seismic data analysis by preserving phase information.
problem Low-frequency aliasing in seismic data due to discarded phase information.
method Developed complex-valued deep convolutional networks to leverage phase information in deterministic physical data.
result Complex-valued networks outperform real-valued networks in training and inference from deterministic physical data.
BN helps learn fragile features, which can improve adversarial robustness.
problem The role of batch normalization in adversarial training and its impact on robustness.
method Investigated the expressiveness of BN in learning robust features compared to random features.
result Adversarially fine-tuning BN layers can result in non-trivial adversarial robustness.
Study uses machine learning to predict nonlinear seismic brace behavior.
problem Predicting nonlinear seismic response of structural braces.
method State-of-the-art machine learning techniques, specifically LSTM, were used.
result LSTM method effectively captures nonlinear brace behavior.
CNN improves salt body interpretation in seismic imaging.
problem Manual salt body interpretation is time-consuming and prone to bias.
method U-Net and ResNet with ELU activation and Lovász-Softmax loss.
result CNN predictions match manual interpretations well, especially in weak reflection areas.
Expert-guided model improves seismic compliance monitoring.
problem Classifying seismic data with missingness and expert knowledge.
method Expert-guided class-conditional model with interpretable goodness-of-fit features.
result Interpretable classifier outperforms standard machine learning, especially with small training data.
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.
Clusters of withdrawals emerge in banks due to latent fragility.
problem Understanding clustered withdrawals in dynamic bank runs.
method Mean-field game framework to model dynamic bank runs with clustered withdrawals.
result Existence of equilibrium and characterization of earliest-run and latest-run equilibria.
We present an application of deep generative models in the context of partial-differential equation (PDE) constrained inverse problems. We combine a generative adversarial network (GAN) representing an a priori model that creates subsurface geological structures and their petrophysical properties, with the numerical so…
Recent SVD-free matrix factorization formulations have enabled rank minimization for systems with millions of rows and columns, paving the way for matrix completion in extremely large-scale applications, such as seismic data interpolation. In this paper, we consider matrix completion formulations designed to hit a targ…
Measuring systemic risk or fragility of financial systems is a ubiquitous task of fundamental importance in analyzing market efficiency, portfolio allocation, and containment of financial contagions. Recent attempts have shown that representing such systems as a weighted graph characterizing the complex web of interact…
We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable. Our approach is motivated by a model of seismic events and signals, where events detected in d…
Spatially-aware model improves earthquake hazard assessment accuracy.
problem Misrepresentation of seismic effects across diverse landscapes.
method Causal Bayesian network with Gaussian Processes and normalizing flows.
result Achieves up to 35.2% AUC improvement over existing methods.