A Label-Free High-Precision Residual Moveout Picking Method for Travel Time Tomography based on Deep Learning

• URL: http://arxiv.org/abs/2503.06038v1
• Date: Sat, 08 Mar 2025 03:27:55 GMT
• Title: A Label-Free High-Precision Residual Moveout Picking Method for Travel Time Tomography based on Deep Learning
• Authors: Hongtao Wang, Jiandong Liang, Lei Wang, Shuaizhe Liang, Jinping Zhu, Chunxia Zhang, Jiangshe Zhang,
• Abstract summary: Residual moveout (RMO) provides critical information for travel time tomography.<n>Current analytical approach does not accurately capture local saltation.<n>Supervised learning-based image segmentation methods for picking can effectively capture local variations.
• Score: 7.081408406139507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Residual moveout (RMO) provides critical information for travel time tomography. The current industry-standard method for fitting RMO involves scanning high-order polynomial equations. However, this analytical approach does not accurately capture local saltation, leading to low iteration efficiency in tomographic inversion. Supervised learning-based image segmentation methods for picking can effectively capture local variations; however, they encounter challenges such as a scarcity of reliable training samples and the high complexity of post-processing. To address these issues, this study proposes a deep learning-based cascade picking method. It distinguishes accurate and robust RMOs using a segmentation network and a post-processing technique based on trend regression. Additionally, a data synthesis method is introduced, enabling the segmentation network to be trained on synthetic datasets for effective picking in field data. Furthermore, a set of metrics is proposed to quantify the quality of automatically picked RMOs. Experimental results based on both model and real data demonstrate that, compared to semblance-based methods, our approach achieves greater picking density and accuracy.

Related papers

• PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency [8.719356558714246]
  Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media.<n>Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima.<n>Various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process.
  arXiv  Detail & Related papers  (2025-02-17T15:30:17Z)
• SUDS: A Strategy for Unsupervised Drift Sampling [0.5437605013181142]
  Supervised machine learning encounters concept drift, where the data distribution changes over time, degrading performance. We present the Strategy for Drift Sampling (SUDS), a novel method that selects homogeneous samples for retraining using existing drift detection algorithms. Our results demonstrate the efficacy of SUDS in optimizing labeled data use in dynamic environments.
  arXiv  Detail & Related papers  (2024-11-05T10:55:29Z)
• Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning [2.5782420501870296]
  This paper proposes a voice conversion method that works with limited data. It is based on variational deep kernel learning (SVDKL) It is possible to estimate non-smooth and more complex functions.
  arXiv  Detail & Related papers  (2023-09-08T16:32:47Z)
• A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels [27.7867122240632]
  In this paper, we propose a novel-based approach to enable the online selection of noisy labels. Online Gradient-based Selection Selection (OGRS) can automatically select clean samples by steps of update from datasets with varying clean ratios without changing the parameter setting.
  arXiv  Detail & Related papers  (2023-06-08T08:57:06Z)
• Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning [28.042568086423298]
  Repeated Sampling of Random Subsets (RS2) is a powerful yet overlooked random sampling strategy. We test RS2 against thirty state-of-the-art data pruning and data distillation methods across four datasets including ImageNet. Our results demonstrate that RS2 significantly reduces time-to-accuracy compared to existing techniques.
  arXiv  Detail & Related papers  (2023-05-28T20:38:13Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)
• Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time [0.0]
  We propose a method to reconstruct activations of classification and semantic segmentation networks by stitching them with a GAN generator utilizing a 1x1 convolution. We test our approach on images of animals from the AFHQ wild dataset, ImageNet1K, and real-world digital pathology scans of stained tissue samples.
  arXiv  Detail & Related papers  (2023-02-04T15:28:49Z)
• Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation [151.70234052015948]
  We propose a novel approach that encourages the optimization algorithm to seek a flat trajectory. We show that the weights trained on synthetic data are robust against the accumulated errors perturbations with the regularization towards the flat trajectory. Our method, called Flat Trajectory Distillation (FTD), is shown to boost the performance of gradient-matching methods by up to 4.7%.
  arXiv  Detail & Related papers  (2022-11-20T15:49:11Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation [54.49894381464853]
  We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
  arXiv  Detail & Related papers  (2020-07-21T13:27:09Z)
• Ensemble Wrapper Subsampling for Deep Modulation Classification [70.91089216571035]
  Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems.
  arXiv  Detail & Related papers  (2020-05-10T06:11:13Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

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.