Enhancing Lithological Mapping with Spatially Constrained Bayesian Network (SCB-Net): An Approach for Field Data-Constrained Predictions with Uncertainty Evaluation

• URL: http://arxiv.org/abs/2403.20195v1
• Date: Fri, 29 Mar 2024 14:17:30 GMT
• Title: Enhancing Lithological Mapping with Spatially Constrained Bayesian Network (SCB-Net): An Approach for Field Data-Constrained Predictions with Uncertainty Evaluation
• Authors: Victor Silva dos Santos, Erwan Gloaguen, Shiva Tirdad,
• Abstract summary: The SCB-Net aims to effectively exploit the information from auxiliary variables while producing spatially constrained predictions. The SCB-Net has been applied to two selected areas in northern Quebec, Canada. This study highlights the promising advancements of deep neural networks in geolinear, particularly in handling complex spatial feature learning tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Geological maps are an extremely valuable source of information for the Earth sciences. They provide insights into mineral exploration, vulnerability to natural hazards, and many other applications. These maps are created using numerical or conceptual models that use geological observations to extrapolate data. Geostatistical techniques have traditionally been used to generate reliable predictions that take into account the spatial patterns inherent in the data. However, as the number of auxiliary variables increases, these methods become more labor-intensive. Additionally, traditional machine learning methods often struggle with spatially correlated data and extracting valuable non-linear information from geoscientific datasets. To address these limitations, a new architecture called the Spatially Constrained Bayesian Network (SCB-Net) has been developed. The SCB-Net aims to effectively exploit the information from auxiliary variables while producing spatially constrained predictions. It is made up of two parts, the first part focuses on learning underlying patterns in the auxiliary variables while the second part integrates ground-truth data and the learned embeddings from the first part. Moreover, to assess model uncertainty, a technique called Monte Carlo dropout is used as a Bayesian approximation. The SCB-Net has been applied to two selected areas in northern Quebec, Canada, and has demonstrated its potential in generating field-data-constrained lithological maps while allowing assessment of prediction uncertainty for decision-making. This study highlights the promising advancements of deep neural networks in geostatistics, particularly in handling complex spatial feature learning tasks, leading to improved spatial information techniques.

Related papers

• Towards physics-informed neural networks for landslide prediction [1.03590082373586]
  PINN is a neural network tasked with explicitly retrieving geotechnical parameters from common proxy variables. Our model produces excellent predictive performance in the form of standard susceptibility output. This architecture is framed to tackle coseismic landslide prediction, something that, if confirmed in other studies, could open up towards PINN-based near-real-time predictions.
  arXiv  Detail & Related papers  (2024-07-09T11:54:49Z)
• A Survey of Generative Techniques for Spatial-Temporal Data Mining [93.55501980723974]
  This paper focuses on the integration of generative techniques into spatial-temporal data mining. The paper provides a comprehensive analysis of generative technique-based spatial-temporal methods. It also introduces a standardized framework specifically designed for the spatial-temporal data mining pipeline.
  arXiv  Detail & Related papers  (2024-05-15T12:07:43Z)
• Exploring Geometric Deep Learning For Precipitation Nowcasting [28.44612565923532]
  We propose a geometric deep learning-based temporal Graph Convolutional Network (GCN) for precipitation nowcasting. The adjacency matrix that simulates the interactions among grid cells is learned automatically by minimizing the L1 loss between prediction and ground truth pixel value. We test the model on sequences of radar reflectivity maps over the Trento/Italy area.
  arXiv  Detail & Related papers  (2023-09-11T21:14:55Z)
• TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction [56.22339016797785]
  We introduce a novel saliency prediction model that learns to output saliency maps in sequential time intervals. Our approach locally modulates the saliency predictions by combining the learned temporal maps. Our code will be publicly available on GitHub.
  arXiv  Detail & Related papers  (2023-01-05T22:10:16Z)
• Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility [5.864710987890994]
  Deep GA-vLS assumes data to be of fixed and regular shaped tensor shaped and face challenges of handling irregular data. We present a novel geo-aware enabled learning operation based on a novel data structure for dependencies while maintaining the recurrent mechanism.
  arXiv  Detail & Related papers  (2022-11-27T16:51:28Z)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation [92.96204497841032]
  Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. We propose a model-agnostic data augmentation method that allows us to exploit the prior knowledge of the conditional independence (CI) relations. We experimentally show that the proposed method is effective in improving the prediction accuracy, especially in the small-data regime.
  arXiv  Detail & Related papers  (2021-02-27T06:13:59Z)
• Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics? [3.5450828190071655]
  We show how deep neural networks can learn the complex relationships between point-sampled target variables and gridded auxiliary variables. We obtain uncertainty estimates via a Bayesian approximation known as Monte Carlo dropout.
  arXiv  Detail & Related papers  (2020-08-17T13:56:43Z)
• Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets [21.703238902823937]
  We propose a structure-aware neural network with spatial attention blocks to exploit the spatial relationship of visual features. Second, we introduce a global focal relative loss for uniform point pairs to enhance spatial constraint in the prediction. Third, based on analysis of failure cases for prior methods, we collect a new Hard Case (HC) Depth dataset of challenging scenes.
  arXiv  Detail & Related papers  (2020-07-22T08:21:02Z)
• Seismic horizon detection with neural networks [62.997667081978825]
  This paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model. The main contribution of this paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model.
  arXiv  Detail & Related papers  (2020-01-10T11:30:50Z)

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.