Encoder-Decoder Architecture for 3D Seismic Inversion

• URL: http://arxiv.org/abs/2207.14789v1
• Date: Fri, 29 Jul 2022 17:14:07 GMT
• Title: Encoder-Decoder Architecture for 3D Seismic Inversion
• Authors: Maayan Gelboim, Amir Adler, Yen Sun, Mauricio Araya-Polo
• Abstract summary: This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder-decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes.
• Score: 1.2234742322758418
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 wave equation, as required by industry-standard tools such as Full Waveform Inversion (FWI). For example, in an area with surface dimensions of 4.5km $\times$ 4.5km, hundreds of seismic shot-gather cubes are required for 3D model reconstruction, leading to Terabytes of recorded data. This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder-decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes. The proposed solution demonstrates that realistic 3D models can be reconstructed with a structural similarity index measure (SSIM) of 0.8554 (out of 1.0) in the presence of field noise at 10dB signal-to-noise ratio.

Related papers

• Mixed Diffusion for 3D Indoor Scene Synthesis [55.94569112629208]
  We present MiDiffusion, a novel mixed discrete-continuous diffusion model architecture. We represent a scene layout by a 2D floor plan and a set of objects, each defined by its category, location, size, and orientation. Our experimental results demonstrate that MiDiffusion substantially outperforms state-of-the-art autoregressive and diffusion models in floor-conditioned 3D scene synthesis.
  arXiv  Detail & Related papers  (2024-05-31T17:54:52Z)
• Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation [66.3814684757376]
  This work presents Zero123-6D, the first work to demonstrate the utility of Diffusion Model-based novel-view-synthesizers in enhancing RGB 6D pose estimation at category-level. The outlined method shows reduction in data requirements, removal of the necessity of depth information in zero-shot category-level 6D pose estimation task, and increased performance, quantitatively demonstrated through experiments on the CO3D dataset.
  arXiv  Detail & Related papers  (2024-03-21T10:38:18Z)
• SeisFusion: Constrained Diffusion Model with Input Guidance for 3D Seismic Data Interpolation and Reconstruction [26.02191880837226]
  We propose a novel diffusion model reconstruction framework tailored for 3D seismic data. We introduce a 3D neural network architecture into the diffusion model, successfully extending the 2D diffusion model to 3D space. Our method exhibits superior reconstruction accuracy when applied to both field datasets and synthetic datasets.
  arXiv  Detail & Related papers  (2024-03-18T05:10:13Z)
• Diffraction and Scattering Aware Radio Map and Environment Reconstruction using Geometry Model-Assisted Deep Learning [14.986314279939952]
  This paper proposes to employ the received signal strength (RSS) data to jointly construct the radio map and a virtual environment. We develop a virtual obstacle model and characterize the geometry relation between the propagation paths and the virtual obstacles. Numerical experiments demonstrate that, in addition to reconstructing a 3D virtual environment, the proposed model outperforms the state-of-the-art methods in radio map construction with 10%-18% accuracy improvements.
  arXiv  Detail & Related papers  (2024-03-01T02:20:01Z)
• Neural RF SLAM for unsupervised positioning and mapping with channel state information [51.484516640867525]
  We present a neural network architecture for jointly learning user locations and environment mapping up to isometry. The proposed model learns an interpretable latent, i.e., user location, by just enforcing a physics-based decoder.
  arXiv  Detail & Related papers  (2022-03-15T21:32:44Z)
• Machine learning-based porosity estimation from spectral decomposed seismic data [0.0]
  Estimating porosity models via seismic data is challenging due to the signal noise and insufficient resolution of seismic data. We propose a machine learning-based workflow to convert seismic data to porosity models.
  arXiv  Detail & Related papers  (2021-11-23T00:03:38Z)
• Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop [13.1144828613672]
  Full-Waveform Inversion (FWI) has been widely used in geophysics to estimate subsurface velocity maps from seismic data. We introduce a new large-scale dataset OpenFWI, to establish a more challenging benchmark for the community. Experiment results show that our model (using seismic data alone) yields comparable accuracy to the supervised counterpart.
  arXiv  Detail & Related papers  (2021-10-14T17:47:22Z)
• Data-Driven Shadowgraph Simulation of a 3D Object [50.591267188664666]
  We are replacing the numerical code by a computationally cheaper projection based surrogate model. The model is able to approximate the electric fields at a given time without computing all preceding electric fields as required by numerical methods. This model has shown a good quality reconstruction in a problem of perturbation of data within a narrow range of simulation parameters and can be used for input data of large size.
  arXiv  Detail & Related papers  (2021-06-01T08:46:04Z)
• NuSPAN: A Proximal Average Network for Nonuniform Sparse Model -- Application to Seismic Reflectivity Inversion [23.080395291046408]
  We solve the problem of proximal deconvolution in the context of high-resolution recovery of seismic data. We employ a combination of convex and non-uniform signalizers. The resulting sparse network architecture can be acquired in a data-driven fashion.
  arXiv  Detail & Related papers  (2021-05-01T04:33:02Z)
• Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis [143.22192229456306]
  This paper proposes a deep 3D energy-based model to represent volumetric shapes. The benefits of the proposed model are six-fold. Experiments demonstrate that the proposed model can generate high-quality 3D shape patterns.
  arXiv  Detail & Related papers  (2020-12-25T06:09:36Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)

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.