IntraSeismic: a coordinate-based learning approach to seismic inversion

• URL: http://arxiv.org/abs/2312.10568v1
• Date: Sun, 17 Dec 2023 00:29:25 GMT
• Title: IntraSeismic: a coordinate-based learning approach to seismic inversion
• Authors: Juan Romero, Wolfgang Heidrich, Nick Luiken, Matteo Ravasi
• Abstract summary: IntraSeismic is a novel hybrid seismic inversion method that seamlessly combines coordinate-based learning with the physics of the post-stack modeling operator. Key features of IntraSeismic are unparalleled performance in 2D and 3D post-stack seismic inversion, rapid convergence rates, and ability to seamlessly include hard constraints. Synthetic and field data applications of IntraSeismic are presented to validate the effectiveness of the proposed method.
• Score: 14.625250755761662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 energy and construction sectors for applications ranging from oil and gas prospection, to geothermal production and carbon capture and storage monitoring, to geotechnical assessment of infrastructures. Extracting quantitative information from seismic recordings, such as an acoustic impedance model, is however a highly ill-posed inverse problem, due to the band-limited and noisy nature of the data. This paper introduces IntraSeismic, a novel hybrid seismic inversion method that seamlessly combines coordinate-based learning with the physics of the post-stack modeling operator. Key features of IntraSeismic are i) unparalleled performance in 2D and 3D post-stack seismic inversion, ii) rapid convergence rates, iii) ability to seamlessly include hard constraints (i.e., well data) and perform uncertainty quantification, and iv) potential data compression and fast randomized access to portions of the inverted model. Synthetic and field data applications of IntraSeismic are presented to validate the effectiveness of the proposed method.

Related papers

• PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation with Devito [0.1657441317977376]
  PyAWD is a Python library designed to generate high-dimensional synthetic-acoustic wave propagation datasets. By allowing fine control over parameters such as wave speed, external forces, spatial and temporal discretization, PyAWD enables the creation of ML-scale datasets.
  arXiv  Detail & Related papers  (2024-11-19T16:49:58Z)
• Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields [46.72819846541652]
  We propose a framework based on Vari Data Assimilation and Deep Learning concepts. This framework is applied to recover rich-in-time, high-resolution information on sea surface wind speed.
  arXiv  Detail & Related papers  (2023-12-14T13:40:39Z)
• DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model [44.490978394267195]
  We propose a spatial-temporal probabilistic model for trajectory generation (DiffTraj) The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories.
  arXiv  Detail & Related papers  (2023-04-23T08:42:45Z)
• Prediction of single well production rate in water-flooding oil fields driven by the fusion of static, temporal and spatial information [3.743500951752524]
  A novel machine learning model is constructed to fuse the static geological information, dynamic well production history, and spatial information of the adjacent water injection wells. It is proved theoretically and practically that the presented model can make full use of the model structure to integrate the characteristics of the data and the experts' knowledge.
  arXiv  Detail & Related papers  (2023-02-22T08:10:25Z)
• Mitigation of Spatial Nonstationarity with Vision Transformers [1.690637178959708]
  We show the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance. We propose the mitigation of such impacts using self-attention (vision transformer) models.
  arXiv  Detail & Related papers  (2022-12-09T02:16:05Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• Deep Learning for Surface Wave Identification in Distributed Acoustic Sensing Data [1.7237878022600697]
  We present a highly scalable and efficient approach to process real, complex DAS data. Deep supervised learning is used to identify "useful" coherent surface waves generated by anthropogenic activity. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.
  arXiv  Detail & Related papers  (2020-10-15T15:53:03Z)
• Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition [46.836815779215456]
  We present a novel decoupled spatial-temporal attention network(DSTA-Net) for skeleton-based action recognition. Three techniques are proposed for building attention blocks, namely, spatial-temporal attention decoupling, decoupled position encoding and spatial global regularization. To test the effectiveness of the proposed method, extensive experiments are conducted on four challenging datasets for skeleton-based gesture and action recognition.
  arXiv  Detail & Related papers  (2020-07-07T07:58:56Z)
• Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation [12.653673008542155]
  Machine learning-based inversion usually works in a trace-by-trace fashion on seismic data. We propose a deep learning-based seismic inversion workflow that models each seismic trace not only temporally but also spatially.
  arXiv  Detail & Related papers  (2020-06-28T00:19:58Z)
• Semiparametric Bayesian Forecasting of Spatial Earthquake Occurrences [77.68028443709338]
  We propose a fully Bayesian formulation of the Epidemic Type Aftershock Sequence (ETAS) model. The occurrence of the mainshock earthquakes in a geographical region is assumed to follow an inhomogeneous spatial point process.
  arXiv  Detail & Related papers  (2020-02-05T10:11:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.