Papers Seismic Inversion
“Seismic Inversion” 태그가 달린 논문 25편 · 필터 해제
Seismic inversion using hybrid quantum neural networks
Quantum computing leverages qubits, exploiting superposition and entanglement to solve problems intractable for classical computers, offering significant computational advantages. Quantum machine learning (QML), which in…
DecoderQuantum Machine LearningSeismic InversionGeophysical inverse problems with measurement-guided diffusion models
Solving inverse problems with the reverse process of a diffusion model represents an appealing avenue to produce highly realistic, yet diverse solutions from incomplete and possibly noisy measurements, ultimately enablin…
DenoisingSeismic InversionUncertainty QuantificationPosterior 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 InversionUncertainty Quantification in Seismic Inversion Through Integrated Importance Sampling and Ensemble Methods
Seismic inversion is essential for geophysical exploration and geological assessment, but it is inherently subject to significant uncertainty. This uncertainty stems primarily from the limited information provided by obs…
Computational EfficiencySeismic InversionUncertainty QuantificationOrthoSeisnet: Seismic Inversion through Orthogonal Multi-scale Frequency Domain U-Net for Geophysical Exploration
Seismic inversion is crucial in hydrocarbon exploration, particularly for detecting hydrocarbons in thin layers. However, the detection of sparse thin layers within seismic datasets presents a significant challenge due t…
Seismic InversionSSIMTime-lapse seismic inversion for CO2 saturation with SeisCO2Net: An application to Frio-II site
Seismic monitoring of geological CO2 storage (GCS) involves highly nonlinear seismic inversion and petrophysical inversion, making it challenging to estimate CO2 volume efficiently and detect possible early CO2 leakages.…
Seismic InversionTransfer LearningIntraSeismic: a coordinate-based learning approach to seismic inversion
Seismic imaging is the numerical process of creating a volumetric representation of the subsurface geological structures from elastic waves recorded at the surface of the Earth. As such, it is widely utilized in the ener…
Data CompressionSeismic ImagingSeismic InversionUncertainty QuantificationDeep Compressed Learning for 3D Seismic Inversion
We consider the problem of 3D seismic inversion from pre-stack data using a very small number of seismic sources. The proposed solution is based on a combination of compressed-sensing and machine learning frameworks, kno…
3D Reconstructioncompressed sensingDecoderDimensionality Reduction+1Physics-informed neural network for seismic wave inversion in layered semi-infinite domain
Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. The recent development of physics-informed neural network (PINN) has shed new light on seismic in…
Seismic InversionContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion
The automated interpretation and inversion of seismic data have advanced significantly with the development of Deep Learning (DL) methods. However, these methods often require numerous costly well logs, limiting their ap…
Contrastive LearningPseudo LabelregressionSeismic Inversion+1Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion
Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion is a notoriously ill-posed inverse prob…
Decision MakingSeismic InversionUncertainty QuantificationVariational InferenceEnhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks
Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitoring because of its computational efficie…
Computational EfficiencyData AugmentationPredictionquantile regression+2Wave simulation in non-smooth media by PINN with quadratic neural network and PML condition
Frequency-domain simulation of seismic waves plays an important role in seismic inversion, but it remains challenging in large models. The recently proposed physics-informed neural network (PINN), as an effective deep le…
Seismic InversionEncoder-Decoder Architecture for 3D Seismic Inversion
Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational load due to iterative numerical solutions of the wav…
DecoderSeismic InversionSSIMRaw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler
Traditional seismic inversion (SI) maps the hundreds of terabytes of raw-field data to subsurface properties in gigabytes. This inversion process is expensive, requiring over a year of human and computational effort. Re…
Auxiliary LearningSeismic InversionSSIMConvolutional Sparse Coding Fast Approximation with Application to Seismic Reflectivity Estimation
In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building blocks. Similarly, neural networks perform a given task by le…
Seismic InversionMaking Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation
Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the …
Data AugmentationSeismic ImagingSeismic InversionSeismic Inverse Modeling Method based on Generative Adversarial Network
Seismic inverse modeling is a common method in reservoir prediction and it plays a vital role in the exploration and development of oil and gas. Conventional seismic inversion method is difficult to combine with complica…
Generative Adversarial NetworkSeismic InversionDirect Velocity Inversion of Ground Penetrating Radar Data Using GPRNet
Ground penetrating radar (GPR) is used to image the shallow subsurface as evident in earth and planetary exploration. Electromagnetic (EM) velocity (permittivity) models are inverted from GPR data for accurate migration.…
GeophysicsGPRSeismic ImagingSeismic InversionA Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage
Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO2) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computation…
Decision MakingManagementSeismic InversionUncertainty Quantification