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Hydrogeophysics Data for Machine Learning Hydrofacies Classification

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Storage:	The size of this resource is 73.3 MB
Created:	Mar 13, 2024 at 6:41 p.m.
Last updated:	May 18, 2024 at 3:28 a.m.
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Sharing Status:	Public
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Abstract

Direct interpretation of complex and heterogeneous geological systems in terms of facies from individual tomograms remains a significant challenge because of noise in the measured data and non-uniqueness in geophysical inversion. We introduce a machine learning-based approach for hydrogeophysical image reconstruction using the Expectation-Maximization algorithm for joint classification of distinct hydrofacies from two or more independently inverted geophysical data sets.

To understand the impact of noise on facies discrimination, we design two synthetic models of hydrofacies with varying levels of complexity and heterogeneity. This synthetic study allows us to compare the classified image with the benchmark model for different noise scenarios. With field data in the Laramie range, WY, USA, we explore the effects of regularization on the hydrofacies classification.

The dataset consists of electrical resistivity and seismic velocities in both the field case and the synthetic case.

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Coverage

Spatial

Coordinate System/Geographic Projection:

WGS 84 EPSG:4326

Coordinate Units:

Decimal degrees

Place/Area Name:

Laramie Range

Longitude

-105.5055°

Latitude

41.2837°

Temporal

Start Date:
End Date:

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How to Cite

Oladeji, E. (2024). Hydrogeophysics Data for Machine Learning Hydrofacies Classification, HydroShare, http://www.hydroshare.org/resource/006636e6a25e487ea0cd1c43cfbebaba

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/

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