Linear feature analysis plays a fundamental role in geospatial applications, from detecting infrastructure networks to characterizing geological formations. In this paper, we introduce linkinglines, an open-source Python package tailored for the clustering and feature extrac- tion of linear structures in geospatial data. Our package leverages the Hough Transform, commonly used in image processing, performs clustering of line segments in the Hough Space, and then provides unique feature extraction methods and visualization. linkinglines em- powers researchers, data scientists, and analysts across diverse domains to efficiently process, understand, and extract valuable insights from linear features, contributing to more informed decision-making and enhanced data-driven exploration. We have used linkinglines to map dike swarms with thousands of segments associated with Large Igneous Provinces in Kubo Hutchison et al. (2023).
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Multiscale Spatial Patterns in Giant Dike Swarms Identified Through Objective Feature Extraction
Dike swarms are ubiquitous on terrestrial planets and represent the frozen remnants of magma transport networks. However, spatial complexity, protracted emplacement history, and uneven surface exposure typically make it difficult to quantify patterns in dike swarms on different scales. In this study, we address this challenge using the Hough transform (HT) to objectively link dissected dike segments and analyze multiscale spatial structure in dike swarms. We apply this method to swarms of three scales: the Spanish Peaks, USA; the Columbia River Flood Basalt Group (CRBG), USA; the Deccan Traps Flood Basalts, India. First, we cluster dike segments in HT space, recognizing prevalent linearly aligned structures that represent single dikes or dike packets, with lengths up to 10 − 30x the mapped mean segment length. Second, we identify colinear and radial dike segment mesoscale structures within each data set, using the HT to segment swarms into constituent spatial patterns. We show that for both the CRBG and Deccan Traps, a single radial or circumferential swarm does not well characterize the data. Instead, multiple and sometimes overlapping mesoscale linear and radial features are prevalent suggesting a complex history of crustal stresses. The HT can provide useful insights in a variety of geologic settings where many quasi‐linear features, at any scale, are superimposed spatially.
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- Award ID(s):
- 1848554
- PAR ID:
- 10515284
- Publisher / Repository:
- American Geophysical Union
- Date Published:
- Journal Name:
- Geochemistry, Geophysics, Geosystems
- Volume:
- 24
- Issue:
- 9
- ISSN:
- 1525-2027
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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