Predictive Geological Mapping with Convolution Neural Network Using Statistical Data Augmentation on a 3D Model

• URL: http://arxiv.org/abs/2110.14440v1
• Date: Wed, 27 Oct 2021 13:56:40 GMT
• Title: Predictive Geological Mapping with Convolution Neural Network Using Statistical Data Augmentation on a 3D Model
• Authors: Cedou Matthieu, Gloaguen Erwan, Blouin Martin, Cat\'e Antoine, Paiement Jean-Philippe, Tirdad Shiva
• Abstract summary: We develop a data augmentation workflow that uses a 3D geological and magnetic susceptibility model as input. A Gated Shape Convolutional Neural Network algorithm was trained on a generated synthetic dataset to perform geological mapping. The validation conducted on a portion of the synthetic dataset and data from adjacent areas shows that the methodology is suitable to segment the surficial geology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Airborne magnetic data are commonly used to produce preliminary geological maps. Machine learning has the potential to partly fulfill this task rapidly and objectively, as geological mapping is comparable to a semantic segmentation problem. Because this method requires a high-quality dataset, we developed a data augmentation workflow that uses a 3D geological and magnetic susceptibility model as input. The workflow uses soft-constrained Multi-Point Statistics, to create many synthetic 3D geological models, and Sequential Gaussian Simulation algorithms, to populate the models with the appropriate magnetic distribution. Then, forward modeling is used to compute the airborne magnetic responses of the synthetic models, which are associated with their counterpart surficial lithologies. A Gated Shape Convolutional Neural Network algorithm was trained on a generated synthetic dataset to perform geological mapping of airborne magnetic data and detect lithological contacts. The algorithm also provides attention maps highlighting the structures at different scales, and clustering was applied to its high-level features to do a semi-supervised segmentation of the area. The validation conducted on a portion of the synthetic dataset and data from adjacent areas shows that the methodology is suitable to segment the surficial geology using airborne magnetic data. Especially, the clustering shows a good segmentation of the magnetic anomalies into a pertinent geological map. Moreover, the first attention map isolates the structures at low scales and shows a pertinent representation of the original data. Thus, our method can be used to produce preliminary geological maps of good quality and new representations of any area where a geological and petrophysical 3D model exists, or in areas sharing the same geological context, using airborne magnetic data only.

Related papers

• Improving embedding of graphs with missing data by soft manifolds [51.425411400683565]
  The reliability of graph embeddings depends on how much the geometry of the continuous space matches the graph structure. We introduce a new class of manifold, named soft manifold, that can solve this situation. Using soft manifold for graph embedding, we can provide continuous spaces to pursue any task in data analysis over complex datasets.
  arXiv  Detail & Related papers  (2023-11-29T12:48:33Z)
• Synthetic Data Generation and Deep Learning for the Topological Analysis of 3D Data [0.0]
  This research uses deep learning to estimate the topology of sparse, unordered point cloud scenes in 3D. The experimental results of this pilot study support the hypothesis that, with the aid of sophisticated synthetic data generation, neural networks can perform segmentation-based topological data analysis.
  arXiv  Detail & Related papers  (2023-09-29T04:37:35Z)
• Graph Representation of the Magnetic Field Topology in High-Fidelity Plasma Simulations for Machine Learning Applications [0.7650548154518115]
  Topological analysis of the magnetic field in simulated plasmas allows the study of various physical phenomena in a wide range of settings. Magnetic reconnection, a phenomenon related to the dynamics of the magnetic field topology, is difficult to detect and characterize in three dimensions. We propose a pipeline for scalable data analysis and a graph representation of three magnetic fields.
  arXiv  Detail & Related papers  (2023-07-10T18:08:05Z)
• Semi-Automated Segmentation of Geoscientific Data Using Superpixels [4.035753155957697]
  Geological processes determine the distribution of resources such as critical minerals, water, and geothermal energy. Inspired by the concept of superpixels, we propose a deep-learning based approach to segmentized survey data into regions with similar characteristics.
  arXiv  Detail & Related papers  (2023-03-20T19:21:46Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)
• Self-Supervised Graph Representation Learning for Neuronal Morphologies [75.38832711445421]
  We present GraphDINO, a data-driven approach to learn low-dimensional representations of 3D neuronal morphologies from unlabeled datasets. We show, in two different species and across multiple brain areas, that this method yields morphological cell type clusterings on par with manual feature-based classification by experts. Our method could potentially enable data-driven discovery of novel morphological features and cell types in large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-23T12:17:47Z)
• Learning 3D Mineral Prospectivity from 3D Geological Models with Convolutional Neural Networks: Application to a Structure-controlled Hydrothermal Gold Deposit [4.647073295455922]
  We present a novel method that leverages convolutional neural networks (CNNs) to learn 3D mineral prospectivity from the 3D geological models. Specifically, to explore the unstructured 3D geological models with the CNNs whose input should be structured, we develop a 2D CNN framework. This ensures an effective and efficient training of CNNs while allowing the prospective model to approximate the ore-forming process.
  arXiv  Detail & Related papers  (2021-09-02T07:34:10Z)
• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation [68.8204255655161]
  A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable. Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
  arXiv  Detail & Related papers  (2021-02-15T10:37:52Z)
• Mix Dimension in Poincar\'{e} Geometry for 3D Skeleton-based Action Recognition [57.98278794950759]
  Graph Convolutional Networks (GCNs) have already demonstrated their powerful ability to model the irregular data. We present a novel spatial-temporal GCN architecture which is defined via the Poincar'e geometry. We evaluate our method on two current largest scale 3D datasets.
  arXiv  Detail & Related papers  (2020-07-30T18:23:18Z)
• Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
  We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
  arXiv  Detail & Related papers  (2020-04-17T11:55:03Z)
• Topological Machine Learning for Mixed Numeric and Categorical Data [0.0]
  Topological data analysis is a new branch of machine learning that excels in studying high dimensional data. Mixed data objects with mixed numeric and categorical attributes are ubiquitous in real-world applications. We propose a novel topological machine learning method for mixed data classification.
  arXiv  Detail & Related papers  (2020-03-10T08:50:12Z)

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.