On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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48 results for “hydrocarbon exploration”

Automatic salt deposits segmentation: A deep learning approachcs.LG

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.

salt deposits seismic reflection hydrocarbon exploration deep learning U-Net

2018-11-21abs ↗pdf ↗

Supervised learning with artificial hydrocarbon networks: an open source implementation and its applicationscs.LG

AHN package simplifies supervised learning with hydrocarbon networks.

problem Time-consuming encoding and integration with other technologies.

method Develops an open-source R package for AHN.

result AHN package facilitates supervised learning applications.

supervised learning hydrocarbon networks machine learning open source R package

2020-05-20abs ↗pdf ↗

Spatial Modeling of Oil Exploration Areas Using Neural Networks and ANFIS in GISstat.ML

Exploration of hydrocarbon resources is a highly complicated and expensive process where various geological, geochemical and geophysical factors are developed then combined together. It is highly significant how to design the seismic data acquisition survey and locate the exploratory wells since incorrect or imprecise …

2016-08-21abs ↗pdf ↗

A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systemscs.LG

PIML enhances machine learning for subsurface energy systems.

problem Lack of interpretability and domain-specific knowledge in machine learning models.

method Integrates physics principles into data-driven models using deep learning.

result PIML improves model generalization and adherence to physical laws.

Physics-Informed Machine Learning Subsurface Energy Systems Reservoir Simulation Seismic Applications Hydrocarbons Production

2023-08-06abs ↗pdf ↗

Dual Neural Network Architecture for Determining Epistemic and Aleatoric Uncertaintieseess.SP

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.

uncertainty quantification geophysical data Bayesian Neural Network Artificial Neural Network epistemic uncertainty

2019-10-10abs ↗pdf ↗

Proceedings of the Workshop on Data Mining for Oil and Gascs.AI

The process of exploring and exploiting Oil and Gas (O&G) generates a lot of data that can bring more efficiency to the industry. The opportunities for using data mining techniques in the "digital oil-field" remain largely unexplored or uncharted. With the high rate of data expansion, companies are scrambling to develo…

2017-05-09abs ↗pdf ↗

A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Datasetcs.LG

Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one cl…

2016-12-02abs ↗pdf ↗

Bayesian Source Separation Applied to Identifying Complex Organic Molecules in Spaceastro-ph.IM

Emission from a class of benzene-based molecules known as Polycyclic Aromatic Hydrocarbons (PAHs) dominates the infrared spectrum of star-forming regions. The observed emission appears to arise from the combined emission of numerous PAH species, each with its unique spectrum. Linear superposition of the PAH spectra ide…

2014-03-18abs ↗pdf ↗

Predictive Scale-Bridging Simulations through Active Learningcs.LG

Develops active learning for scale-bridging simulations.

problem Quantitative predictions in nanoporous media and inertial confinement fusion.

method Active learning approach to optimize fine-scale simulations for coarse-scale hydrodynamics.

result Optimizes use of fine-scale simulations for coarse-scale predictions.

active learning scale bridging neural networks hydrodynamics molecular dynamics

2022-09-20abs ↗pdf ↗

Hybrid-DNNs: Hybrid Deep Neural Networks for Mixed Inputscs.LG

Develops HDNNs for mixed geoscience data inputs.

problem Lack of multisource, multi-scale information in deep learning studies.

method Hybrid architecture combining feature and target learning.

result HDNNs achieve higher accuracy and better generalization in reservoir prediction.

deep learning geoscience mixed inputs reservoir prediction feature learning

2020-05-18abs ↗pdf ↗

DeepFlow: History Matching in the Space of Deep Generative Modelscs.LG

DeepFlow uses deep generative models to solve history matching problems.

problem Calibrating reservoir models with transient fluid data.

method Generative adversarial network for property distribution, gradient descent on latent variables, and additional well constraints.

result Solves history matching problems for synthetic reservoir models.

history matching reservoir modeling deep generative models inverse problem petrophysical properties

2019-05-14abs ↗pdf ↗

Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIScs.LG

This paper presents the development of a hybrid learning system based on Support Vector Machines (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and domain knowledge to solve prediction problem. The proposed two-stage Domain Knowledge based Fuzzy Information System (DKFIS) improves the prediction accuracy attained…

2016-12-02abs ↗pdf ↗

A novel multiclassSVM based framework to classify lithology from well logs: a real-world applicationcs.LG

Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifi…

2016-12-02abs ↗pdf ↗

Dispersion based Recurrent Neural Network Model for Methane Monitoring in Albertan Tailings Pondsstat.AP

Model predicts methane emissions from oil sands tailing ponds, suggesting significant environmental impact.

problem Estimating methane emissions from inactive oil sands tailing ponds.

method Physics constrained machine learning model using real-time weather data and laboratory experiments.

result Active oil sands tailing ponds emit between 950 to 1500 tonnes of methane per year, equivalent to 6000 gasoline vehicles.

methane emissions oil sands tailing ponds machine learning greenhouse gases climate impact

2024-11-11abs ↗pdf ↗

ML-LBM: Machine Learning Aided Flow Simulation in Porous Mediaphysics.flu-dyn

Deep learning predicts fluid flow in porous media, accelerating simulations by orders of magnitude.

problem Accurate simulation of fluid flow in complex porous media requires excessive computational resources.

method Combining deep learning with direct simulation, using Gated U-Net CNNs trained on datasets of 2D and 3D porous media.

result Deep learning predictions can reach over 90% accuracy for permeability estimation and accelerate simulations by orders of magnitude.

fluid flow porous media machine learning Deep Learning Convolutional Neural Networks

2020-04-22abs ↗pdf ↗

BYOL-Explore: Exploration by Bootstrapped Predictioncs.LG

BYOL-Explore learns to explore visually-rich environments by predicting world dynamics.

problem Exploration in visually complex environments.

method Optimizes a single prediction loss in latent space to learn world representation, dynamics, and exploration policy.

result Achieves superhuman performance on Atari games with simpler design.

exploration curiosity-driven world representation latent space prediction loss

2022-06-16abs ↗pdf ↗

Intrinsic Exploration as Multi-Objective RLcs.LG

A new multi-objective RL framework improves intrinsic exploration performance.

problem Sub-optimal exploration performance due to ad-hoc handling of intrinsic exploration.

method A multi-objective RL framework where both exploration and exploitation are optimized as separate objectives.

result EMU-Q method outperforms classic and other intrinsic RL methods on benchmarks.

reinforcement learning exploration multi-objective control uncertainty

2020-04-06abs ↗pdf ↗

ISL: A novel approach for deep explorationcs.LG

ISL algorithm tackles deep exploration efficiently.

problem Deep exploration in reinforcement learning.

method Derives ISL algorithm by augmenting RL objective with a novel regularization term.

result Empirically shows state-of-the-art performance on deep-exploration benchmarks.

reinforcement learning deep exploration maximum entropy optimization exploration-exploitation

2019-09-13abs ↗pdf ↗

Community Exploration: From Offline Optimization to Online Learningcs.LG

Study explores how to efficiently explore communities with limited budget.

problem Maximizing the number of members met with limited budget in community exploration.

method Systematic study from offline optimization to online learning, including greedy methods and upper confidence algorithms.

result Achieved logarithmic and constant regret bounds in online learning setting.

community exploration online learning offline optimization greedy methods upper confidence

2018-11-13abs ↗pdf ↗

Learning-Driven Exploration for Reinforcement Learningcs.LG

EBE enables efficient exploration in reinforcement learning.

problem Inefficient exploration in reinforcement learning.

method Entropy-based exploration (EBE) quantifies learning and adaptively explores unexplored regions.

result EBE enables faster learning without hyperparameter tuning.

reinforcement learning exploration entropy state space learning

2019-06-17abs ↗pdf ↗

Learning latent state representation for speeding up explorationcs.LG

This work learns latent representations to speed up exploration in complex environments.

problem Challenging exploration in high-dimensional state and action spaces with sparse rewards.

method Representation learning using prior experience to learn effective latent representations.

result Learned latent representations reduce the dimensionality of the search space for effective exploration.

representation learning latent representation reinforcement learning exploration meta-exploration

2019-05-27abs ↗pdf ↗

Go-Explore: a New Approach for Hard-Exploration Problemscs.LG

Go-Explore improves performance on hard-exploration problems in Atari games.

problem Challenges in reinforcement learning, especially with sparse or deceptive rewards.

method Exploits principles of remembering states, returning to promising states, and solving simulated environments.

result Scores significantly higher than previous state-of-the-art on Montezuma's Revenge and Pitfall.

reinforcement learning hard-exploration Atari games Montezuma's Revenge Pitfall

2019-01-30abs ↗pdf ↗

Exploration Conscious Reinforcement Learning Revisitedcs.LG

This work tackles the exploration-exploitation dilemma in RL by developing optimal policies that are inherently exploration-conscious.

problem The exploration-exploitation tradeoff in Reinforcement Learning, where policies need to balance new action exploration with past experience exploitation.

method Developed exploration-conscious criteria that result in optimal policies, solving these criteria by solving a surrogate Markov Decision Process.

result Demonstrated superior performance of exploration-conscious RL algorithms compared to non-exploration-conscious counterparts in both discrete and continuous action spaces.

Reinforcement Learning Exploration-Exploitation Optimal Policies Markov Decision Process Tabular RL

2018-12-13abs ↗pdf ↗

Reinforcement Learning with Probabilistically Complete Explorationcs.LG

R3L uses planning algorithms to efficiently explore sparse reward environments.

problem Balancing exploration and exploitation in sparse reward reinforcement learning.

method Formulate exploration as a search problem using RRT, leverage demonstrations from initial solutions to refine RL policy.

result R3L outperforms classic and intrinsic exploration techniques, requiring fewer samples and achieving better asymptotic performance.

reinforcement learning exploration sparse reward planning algorithms RRT

2020-01-20abs ↗pdf ↗

Diverse Exploration via Conjugate Policies for Policy Gradient Methodscs.LG

DE via conjugate policies improves exploration and policy performance.

problem Effective exploration in policy gradient methods.

method DE via conjugate policies.

result DE improves policy performance and exploration effectiveness.

policy gradient exploration conjugate policies gradient descent

2019-02-10abs ↗pdf ↗

Incentivized Exploration of Non-Stationary Stochastic Banditscs.LG

Study incentivizes exploration in non-stationary MAB with compensation.

problem Incentivized exploration for non-stationary stochastic bandits with biased feedback.

method Proposed algorithms for abruptly-changing and continuously-changing non-stationary environments.

result Achieves sublinear regret and compensation over time.

bandits incentivized exploration non-stationary compensation regret

2024-03-16abs ↗pdf ↗

Fast active learning for pure exploration in reinforcement learningcs.LG

New exploration bonuses improve reinforcement learning efficiency.

problem Efficient exploration in unknown environments with limited feedback.

method Improved exploration bonuses scaling with 1/n and improved stopping time analysis.

result Faster learning rates and improved sample complexity in pure-exploration settings.

reinforcement learning exploration bonuses pure exploration sample complexity stopping time

2020-07-27abs ↗pdf ↗

Exploration in Action Spacecs.LG

New findings on when to use action space exploration in reinforcement learning.

problem Understanding when to use action space exploration over traditional methods.

method Theoretical analysis and empirical testing of simple exploration methods.

result Exploration in action space is preferred when parametric complexity exceeds action space dimensionality and horizon length.

reinforcement learning action space exploration black-box optimization parameter space exploration

2020-03-31abs ↗pdf ↗

Uniformly Conservative Exploration in Reinforcement Learningcs.LG

Proposes a method to avoid excessive exploration in reinforcement learning.

problem Avoiding excessive exploration in reinforcement learning to deploy it in practice.

method Designs a novel algorithm using UCB reinforcement learning policy with adaptive exploration constraints.

result Proves that the approach remains conservative while minimizing regret in tabular settings and validates on real-world tasks.

reinforcement learning exploration conservatism UCB regret

2021-10-25abs ↗pdf ↗

On the Benefits of Free Exploration for Regret Minimization in Multi-Armed Banditscs.LG

New algorithm reduces regret by allowing free exploration in multi-armed bandits.

problem Designing an adaptive policy to minimize regret with a free exploration budget.

method Introduced

(α,β)

-probably saving policies and a two-phase algorithm UFE-KLUCB-H.

result UFE-KLUCB-H accumulates strictly less regret than non-free exploration policies.

multi-armed bandits free exploration regret minimization probably saving policies phase transitions

2026-05-25abs ↗pdf ↗

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrificescs.LG

New meta-RL method avoids exploration-exploitation trade-off.

problem Learning to explore and exploit simultaneously in meta-RL.

method Developed new objectives for exploration and exploitation.

result DREAM outperforms existing methods on complex tasks.

meta-reinforcement learning exploration-exploitation end-to-end training local optima task-relevant information

2020-08-06abs ↗pdf ↗

Pure exploration in multi-armed bandits with low rank structure using oblivious samplercs.LG

The paper tackles pure exploration in multi-armed bandits with low rank structure using oblivious sampling.

problem Pure exploration in multi-armed bandits with low rank reward sequences.

method The approach involves separating the exploration strategy from feedback, using oblivious sampling, and incorporating kernel information of reward vectors.

result Efficient algorithms with regret bound

O(d\sqrt{(\ln N)/n})

for both time-varying and fixed cases, with a lower bound gap of

O(\sqrt{\ln N})

multi-armed bandits low rank structure oblivious sampling pure exploration regret bound

2023-06-28abs ↗pdf ↗

Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environmentcs.LG

The paper evaluates various bonus-based exploration methods in the ALE and finds limited improvement in performance.

problem Improving exploration in reinforcement learning algorithms, especially in challenging games.

method Empirical evaluation of different reward bonuses on the Arcade Learning Environment.

result Recently developed bonus-based exploration methods do not significantly improve performance in challenging games.

reinforcement learning exploration methods reward bonuses Montezuma's Revenge challenging games

2019-08-06abs ↗pdf ↗

MAME : Model-Agnostic Meta-Explorationcs.LG

MAME models a separate exploration policy for faster adaptation.

problem Efficient exploration strategies for quick task adaptation in meta-reinforcement learning.

method Explicitly models a separate exploration policy for task distribution, using self-supervised or supervised learning objectives for adaptation.

result Superior performance compared to prior works in meta-reinforcement learning.

Meta-Reinforcement Learning Exploration Strategies Model-Agnostic Meta-Exploration Adaptation Self-Supervised Learning

2019-11-11abs ↗pdf ↗

RIE-Greedy: Regularization-Induced Exploration for Contextual Banditsstat.ML

Regularization-induced exploration improves contextual bandit performance.

problem Complex reward models in real-world contextual bandits are hard to explore effectively.

method Regularization-induced exploration using stochasticity in cross-validation.

result Regularization-induced exploration leads to reliable exploration in large-scale business environments.

contextual bandits exploration regularization Thompson Sampling cross-validation

2026-03-11abs ↗pdf ↗

Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policycs.LG

The paper proposes an algorithm to learn efficient and effective exploration policies in reinforcement learning.

problem Balancing exploration and exploitation in reinforcement learning.

method Formalized a counterfactual metric for exploration utility and used meta-learning to learn an end-to-end exploration policy.

result Demonstrated improved performance in high-dimensional control tasks in MuJoCo simulator compared to previous methods.

reinforcement learning exploration policy meta-learning counterfactual MuJoCo

2019-05-28abs ↗pdf ↗

MADE: Exploration via Maximizing Deviation from Explored Regionscs.LG

MADE improves exploration in RL by maximizing deviation from explored regions.

problem Efficient exploration in high-dimensional RL tasks with sparse rewards.

method Proposes a new exploration approach via maximizing the deviation of the occupancy of the next policy from explored regions, adding it as an adaptive regularizer to the RL objective.

result Significantly improves sample efficiency in navigation and locomotion tasks.

reinforcement learning exploration occupancy maximization sparse rewards online learning

2021-06-18abs ↗pdf ↗

When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithmscs.LG

Efficient exploration is one of the key challenges for reinforcement learning (RL) algorithms. Most traditional sample efficiency bounds require strategic exploration. Recently many deep RL algorithms with simple heuristic exploration strategies that have few formal guarantees, achieve surprising success in many domain…

2018-05-23abs ↗pdf ↗

Exploration by Maximizing Rényi Entropy for Reward-Free RL Frameworkcs.LG

Maximizes Rényi entropy for efficient exploration in reward-free RL.

problem Challenges of exploration in reward-free reinforcement learning.

method Maximizes Rényi entropy over state-action space in exploration phase; uses batch RL for planning phase.

result Effective and sample-efficient exploration leading to superior policies.

reinforcement learning reward-free exploration entropy maximization policy gradient

2020-06-11abs ↗pdf ↗

Exploration Unboundcs.LG

An agent explores indefinitely in an environment with unlimited rewards.

problem Balancing exploration and exploitation in environments with unlimited rewards.

method Simple example of an environment with unbounded rewards and optimal agent behavior.

result An optimal agent always explores to maximize rewards, regardless of accumulated knowledge.

sequential decision-making exploration-exploitation unbounded rewards optimal agent behavior

2024-07-16abs ↗pdf ↗

Exploration via Epistemic Value Estimationcs.LG

Proposes EVE for efficient exploration in reinforcement learning.

problem Efficient exploration in reinforcement learning.

method EVE: a recipe for posterior over parameters, facilitating efficient exploration.

result Competitive performance on benchmarks, efficient exploration confirmed.

reinforcement learning exploration epistemic value sequential decision making neural networks

2023-03-07abs ↗pdf ↗

Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learningstat.ML

A new exploration method for RL using parameter space noise.

problem Improving exploration in deep reinforcement learning.

method Switching isotropic and directional exploration in parameter space with parameter space noise.

result The proposed method achieves competitive results and better performance in sparse reward environments.

reinforcement learning exploration parameter space noise deep learning sparse rewards

2018-09-18abs ↗pdf ↗

VASE: Variational Assorted Surprise Exploration for Reinforcement Learningcs.LG

VASE uses Bayesian neural networks to improve exploration in sparse reward environments.

problem Exploration in environments with continuous control and sparse rewards.

method VASE uses a Bayesian neural network model of the environment dynamics and variational inference to alternately update the model's accuracy and policy.

result VASE outperforms other surprise-based exploration techniques in continuous control sparse reward environments.

reinforcement learning exploration sparse rewards Bayesian neural networks variational inference

2019-10-31abs ↗pdf ↗

Provably Safe PAC-MDP Exploration Using Analogiescs.LG

ASE safely explores unknown MDPs with unknown dynamics, improving sample efficiency.

problem Balancing exploration and safety in unknown MDPs with stochastic dynamics.

method Exploits analogies between state-action pairs to safely learn near-optimal policies.

result Empirically improves sample efficiency compared to existing methods.

safe exploration MDP PAC-MDP reinforcement learning sample efficiency

2020-07-07abs ↗pdf ↗

Implicit Generative Modeling for Efficient Explorationcs.LG

This work uses model uncertainty for efficient exploration in sparse reward environments.

problem Challenging exploration in sparse reward reinforcement learning environments.

method Implicit generative modeling approach to estimate Bayesian uncertainty of the agent's belief of the environment dynamics.

result Our implicit generative model consistently outperforms competing approaches in data efficiency for exploration.

reinforcement learning exploration model uncertainty Bayesian inference Stein Variational Gradient Descent

2019-11-19abs ↗pdf ↗

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Modelscs.AI

Achieving efficient and scalable exploration in complex domains poses a major challenge in reinforcement learning. While Bayesian and PAC-MDP approaches to the exploration problem offer strong formal guarantees, they are often impractical in higher dimensions due to their reliance on enumerating the state-action space.…

2015-07-03abs ↗pdf ↗

Directed Exploration for Reinforcement Learningcs.LG

Directed exploration improves reinforcement learning efficiency and robustness.

problem Achieving good sample efficiency in reinforcement learning with efficient exploration.

method Directed exploration through goal-conditioned policies that are independent of uncertainty.

result Directed exploration is more efficient and robust to uncertainty than reward bonuses.

reinforcement learning exploration goal-conditioned policies uncertainty

2019-06-18abs ↗pdf ↗

Influence-Based Multi-Agent Explorationcs.LG

Two new exploration methods for multi-agent systems improve team performance.

problem Exploration in transition-dependent multi-agent settings.

method EITI and EDTI, using mutual information and VoI to encourage coordinated exploration.

result Significant improvement in multi-agent performance through coordinated exploration.

multi-agent systems exploration methods intrinsic motivation reinforcement learning coordination

2019-10-12abs ↗pdf ↗