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Deep Neural Networks for Image-Based Dietary Assessment

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VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log Images.

M Quamer Nasim^1,2, Narendra Patwardhan^1,3, Tannistha Maiti¹

¹Deepkapha AI Research, Street Vaart ZZ n° 1.d, 9401 GE Assen, The Netherlands.

Journal of Imaging

|July 28, 2023

Summary

This summary is machine-generated.

VeerNet, a novel deep neural network, automates the digitization of geological raster logs. This AI approach enhances efficiency and accuracy in extracting subsurface formation data from scanned well logs.

Keywords:

deep learning digitization raster log transformer well-log curves

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Area of Science:

Geoscience and Computer Vision
Application of AI in subsurface data analysis

Background:

Raster logs are scanned analog data crucial for geological interpretation.
Manual interpretation of raster logs is time-consuming and prone to errors.
Existing digital extraction methods require significant manual intervention and lack accuracy.

Purpose of the Study:

To develop VeerNet, a deep neural network for automated semantic segmentation and digitization of well-log data from raster images.
To overcome limitations of existing algorithms in terms of speed, accuracy, and manual intervention.

Main Methods:

Proposed VeerNet, a modified UNet-inspired architecture with an attention-augmented read-process-write strategy.
Employed unsupervised computer vision techniques for digital extraction and interpretation of well-log curves.
Focused on semantic segmentation to differentiate curves from background grid and subsequent digitization.

Main Results:

Achieved an overall F1 score of 35% and Intersection over Union of 30% for binary segmentation of multiple curves.
Demonstrated high recall (97%) and low Mean Absolute Error (0.11) in curve digitization.
Showcased VeerNet's capability in predicting Gamma-ray values with a Pearson coefficient of 0.62.

Conclusions:

VeerNet efficiently classifies and digitizes well-log data from raster images, significantly reducing manual effort.
The proposed architecture offers a promising solution for accurate and automated analysis of subsurface geological data.
Further validation and application of VeerNet can enhance geological exploration and resource assessment.