Challenges and Opportunities in CNN-Based Lineament Detection Across New Geological Areas

Abstract

Convolutional Neural Networks (CNNs) have emerged as a promising tool for geological lineament detection, offering rapid and accurate results. However, their performance often diminishes when applied to regions distinct from their training areas, limiting their generalization capabilities. This paper critically examines these challenges, explores contributing factors, and proposes comprehensive strategies, including dataset diversification, advanced feature engineering, and community-driven collaboration, to enhance the applicability of CNNs across diverse geological environments.

Introduction

Lineaments, or linear geological features discernible from the Earth’s surface, are critical for unravelling geological structures and assessing resource potential. The advent of machine learning, particularly CNNs, has enabled efficient and automated lineament detection. Despite their success in well-characterized regions, CNNs struggle in new and geographically diverse terrains due to the unique geological contexts they encounter. This study investigates these limitations and proposes actionable strategies to improve CNN generalization, thereby broadening their utility in geological applications.

Performance Challenges of CNNs in New Areas

CNNs designed for lineament detection often fail to adapt to regions beyond their training scope. This performance degradation stems from several interrelated challenges:

1. Limited Training Data Diversity?

·?????? CNNs excel at identifying patterns within their training datasets but falter when tasked with novel geological features not represented during training. For instance, models trained on datasets from regions like British Columbia, Canada, or New South Wales, Australia, often underperform in geologically distinct areas like the Swayze greenstone belt in Ontario, Canada.

2. Insufficient Feature Abstraction

?? Conventional CNN architectures focus heavily on extracting local patterns but may neglect broader geological characteristics critical for cross-regional generalization. This limitation restricts their ability to identify lineaments in unfamiliar geological settings.

3. Training Data Reliability Issues

·?????? The accuracy of CNNs is inherently tied to the quality of their training datasets. Errors in interpreted lineaments or incomplete datasets can severely hinder model performance and reliability in new environments.

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Proposed Solutions?

To address these challenges, this paper outlines a multifaceted approach emphasizing training data diversity, enhanced feature engineering, and collaborative efforts:

1. Expanding Training Data?

·?????? Curating diverse datasets from varied geological regions ensures that CNNs encounter a wider range of patterns during training, improving their ability to generalize.

2. Advanced Feature Engineering?

·?????? Augmenting raw input data such as Digital Elevation Models (DTMs) with derived geospatial indices (e.g., slope, curvature, and gradient maps) can enhance the geological relevance of CNN-extracted features.

3. Ensuring Data Quality?

·?????? Implementing rigorous data validation protocols, including ground-truth verification and cross-referencing with field data, minimizes errors in training datasets and improves model robustness.

4. Open-Source Collaboration

·?????? Establishing open platforms for sharing CNN architectures, training datasets, and results fosters collaboration within the geoscience community. This collective effort accelerates innovation and addresses the broader challenge of generalization through shared resources and expertise.

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Case Study Insights

A comparative analysis of CNN performance across multiple regions underscores the pivotal role of dataset diversity and feature refinement. Models trained on data from British Columbia and New South Wales demonstrated excellent accuracy within similar geological settings but exhibited significant performance declines in the Swayze greenstone belt. These findings validate the importance of incorporating diverse geological contexts into training datasets and leveraging advanced feature engineering techniques to enhance generalization.

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Future Directions

To ensure reliable CNN-based lineament detection in diverse geological settings, researchers must adopt a holistic and iterative development approach:

·?????? Dataset Diversification: Collect and incorporate data from underrepresented geological regions into training pipelines.?

·?????? Feature Optimization: Integrate advanced geospatial indices and domain-specific features to improve CNN feature abstraction.?

·?????? Validation Rigor: Establish stringent protocols for validating both training and testing datasets against field data.?

·?????? Collaborative Ecosystem: Promote open-source initiatives to enable resource sharing and collective problem-solving within the geoscience community.

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Conclusion

CNNs represent a transformative approach to geological lineament detection, offering unprecedented speed and accuracy. However, their limitations in adapting to new regions necessitate targeted advancements in training data diversity, feature abstraction, and validation practices. By adopting a collaborative and innovative framework, researchers can unlock the full potential of CNNs, enabling robust and reliable geological interpretations across geographically diverse settings.