Pixel-MPS:用于基于像素的多点地质统计模拟的随机嵌入和基于密度的图像模式聚类

Adel Asadi, Snehamoy Chatterjee
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引用次数: 0

摘要

多点地质统计(MPS)是地球系统建模不确定性量化的一种成熟工具,尤其是在处理地质数据的复杂性和异质性时。本研究提出了一种新颖的基于像素的 MPS 方法,利用先进的机器学习算法对空间数据进行建模。基于像素的多点模拟意味着通过从训练图像中借用空间信息并尊重调节数据点,对模拟网格上的单个点进行一次一个的顺序建模。所开发的方法基于使用 t 分布随机邻域嵌入(t-SNE)算法对训练图像模式数据库进行映射以降低维度,并通过应用基于密度的有噪声应用空间聚类(DBSCAN)算法对模式进行聚类,以此作为一种高效的无监督分类技术。在自动化、优化和输入参数调整方面,实现了多个阶段,包括基于熵的模板大小确定和用于聚类参数选择的 k 近邻搜索,以确保所提出的方法无需用户干预。利用合成的二维和三维数据集对所提出的模型进行了有条件和无条件模拟验证,并提供了运行时间信息。最后,该方法被应用于一个案例研究金矿的随机矿体建模。为了证明所提方法的计算效率和准确性,仅使用 361 个硬数据点(约占模拟网格的 3.5%),在 453 秒(约 4.5 秒/次)内对 101 x 101 像素的二维训练图像进行了 100 次有条件实现模拟,所得到的平均模拟结果与训练图像具有良好的视觉匹配,像素错配率仅为 11.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation
Multiple-point geostatistics (MPS) is an established tool for the uncertainty quantification of Earth systems modeling, particularly when dealing with the complexity and heterogeneity of geological data. This study presents a novel pixel-based MPS method for modeling spatial data using advanced machine-learning algorithms. Pixel-based multiple-point simulation implies the sequential modeling of individual points on the simulation grid, one at a time, by borrowing spatial information from the training image and honoring the conditioning data points. The developed methodology is based on the mapping of the training image patterns database using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm for dimensionality reduction, and the clustering of patterns by applying the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, as an efficient unsupervised classification technique. For the automation, optimization, and input parameter tuning, multiple stages are implemented, including entropy-based determination of the template size and a k-nearest neighbors search for clustering parameter selection, to ensure the proposed method does not require the user’s interference. The proposed model is validated using synthetic two- and three-dimensional datasets, both for conditional and unconditional simulations, and runtime information is provided. Finally, the method is applied to a case study gold mine for stochastic orebody modeling. To demonstrate the computational efficiency and accuracy of the proposed method, a two-dimensional training image with 101 by 101 pixels is simulated for 100 conditional realizations in 453 s (~4.5 s per realization) using only 361 hard data points (~3.5% of the simulation grid), and the resulting average simulation has a good visual match and only an 11.8% pixel-wise mismatch with the training image.
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