Papers Geophysics
“Geophysics” 태그가 달린 논문 77편 · 필터 해제
Nonstationary blind deconvolution using spectral constraints
This study develops a nonstationary blind deconvolution using spectral constraints (NBDSC), a novel method to address the ill-posed nature of wavelet inversion in nonstationary seismic data. NBDSC does not require prior …
GeophysicsDitch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum
Self-Supervised Learning (SSL) has become a powerful solution to extract rich representations from unlabeled data. Yet, SSL research is mostly focused on clean, curated and high-quality datasets. As a result, applying SS…
GeophysicsRepresentation LearningSelf-Supervised LearningA Self-Supervised Method for Attenuating Seismic Random and Tracewise Coherent Noise under the Non-Pixelwise Independence Assumption
The attenuation of seismic field noise using self-supervised deep learning has gained attention due to its label-free training process. However, common self-supervised methods are limited by the pixelwise independence as…
DenoisingGeophysicsOn the workflow, opportunities and challenges of developing foundation model in geophysics
Foundation models, as a mainstream technology in artificial intelligence, have demonstrated immense potential across various domains in recent years, particularly in handling complex tasks and multimodal data. In the fie…
Computational EfficiencyGeophysicsTransfer LearningMicroFlow: Domain-Specific Optical Flow for Ground Deformation Estimation in Seismic Events
Dense ground displacement measurements are crucial for geological studies but are impractical to collect directly. Traditionally, displacement fields are estimated using patch matching on optical satellite images from di…
GeophysicsOptical Flow EstimationPatch MatchingLeveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)
Full Waveform Inversion (FWI) is an important geophysical technique considered in subsurface property prediction. It solves the inverse problem of predicting high-resolution Earth interior models from seismic data. Tradi…
GeophysicsProperty PredictionSynergizing Deep Learning and Full-Waveform Inversion: Bridging Data-Driven and Theory-Guided Approaches for Enhanced Seismic Imaging
This review explores the integration of deep learning (DL) with full-waveform inversion (FWI) for enhanced seismic imaging and subsurface characterization. It covers FWI and DL fundamentals, geophysical applications (vel…
GeophysicsSeismic ImagingInversion of Magnetic Data using Learned Dictionaries and Scale Space
Magnetic data inversion is an important tool in geophysics, used to infer subsurface magnetic susceptibility distributions from surface magnetic field measurements. This inverse problem is inherently ill-posed, character…
Dictionary LearningGeophysicsSimultaneous Automatic Picking and Manual Picking Refinement for First-Break
First-break picking is a pivotal procedure in processing microseismic data for geophysics and resource exploration. Recent advancements in deep learning have catalyzed the evolution of automated methods for identifying f…
GeophysicsPhysics-Driven Self-Supervised Deep Learning for Free-Surface Multiple Elimination
In recent years, deep learning (DL) has emerged as a promising alternative approach for various seismic processing tasks, including primary estimation (or multiple elimination), a crucial step for accurate subsurface ima…
GeophysicsOperator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach
Machine learning methods have shown great success in various scientific areas, including fluid mechanics. However, reconstruction problems, where full velocity fields must be recovered from partial observations, remain c…
GeophysicsGPUOperator learningA Novel Diffusion Model for Pairwise Geoscience Data Generation with Unbalanced Training Dataset
Recently, the advent of generative AI technologies has made transformational impacts on our daily lives, yet its application in scientific applications remains in its early stages. Data scarcity is a major, well-known ba…
GeophysicsSeismic ImagingGeological and Well prior assisted full waveform inversion using conditional diffusion models
Full waveform inversion (FWI) often faces challenges due to inadequate seismic observations, resulting in band-limited and geologically inaccurate inversion results. Incorporating prior information from potential velocit…
GeophysicsDAWN-FM: Data-Aware and Noise-Informed Flow Matching for Solving Inverse Problems
Inverse problems, which involve estimating parameters from incomplete or noisy observations, arise in various fields such as medical imaging, geophysics, and signal processing. These problems are often ill-posed, requiri…
DeblurringGeophysicsImage DeblurringUncertainty QuantificationA novel algorithm for the decomposition of non-stationary multidimensional and multivariate signals
The decomposition of a signal is a fundamental tool in many fields of research, including signal processing, geophysics, astrophysics, engineering, medicine, and many more. By breaking down complex signals into simpler o…
GeophysicsPosterior Sampling for Random Noise Attenuation via Score-based Generative Models
Random noise attenuation is an ill-posed inverse problem with multiple solutions,especially in complicated field noise situations. We present a method to sample stochastic solutions from the posterior distribution of sei…
DenoisingGeophysicsSeismic Inversion2D Basement Relief Inversion using Sparse Regularization
Basement relief gravimetry is crucial in geophysics, especially for oil exploration and mineral prospecting. It involves solving an inverse problem to infer geological model parameters from observed data. The model repre…
GeophysicsFrom PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sp…
GeophysicsKolmogorov-Arnold NetworksPhysics-informed machine learningUncertainty QuantificationA Real Benchmark Swell Noise Dataset for Performing Seismic Data Denoising via Deep Learning
The recent development of deep learning (DL) methods for computer vision has been driven by the creation of open benchmark datasets on which new algorithms can be tested and compared with reproducible results. Although D…
BenchmarkingDenoisingGeophysicsInversion-DeepONet: A Novel DeepONet-Based Network with Encoder-Decoder for Full Waveform Inversion
Full waveform inversion (FWI) plays a crucial role in the field of geophysics. There has been lots of research about applying deep learning (DL) methods to FWI. The success of DL-FWI relies significantly on the quantity …
DecoderGeophysics