Machine Learning in Applied Earth Science
A special issue of Geosciences (ISSN 2076-3263).
Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 2318
Special Issue Editor
Interests: geologic sequestration of carbon dioxide by mineral carbonation, which converts CO2 into solid minerals; geophysical remote sensing for resource exploration and environmental monitoring; mathematics of seismic imaging and inversion; airborne geophysical remote sensing; technology and policy strategies for sustainable resource management
Special Issue Information
Dear Colleagues,
The revolution in sensor technology of the last two decades—from precision magnetometers on drones to high-resolution LIDAR and hyperspectral cameras on aircraft; broadband three-axis geophones in modern land and marine seismic surveys; distributed acoustic sensing (DAS) on borehole fiber-optic cables; mobile gravity, electromagnetic, and ground-penetrating radar arrays; and more—has brought a flood of new data to traditional fields of applied Earth science concerned with mapping Earth’s shallow crust to depths of a few kilometers. These data have been obtained in an effort not only to find and manage needed resources like groundwater, minerals, and hydrocarbons, but also to monitor the safe disposal of waste products of our modern economy, including CO2 and other greenhouse gases driving accelerated climate change.
The extraction of more and better information from the Big Data generated by modern sensors requires automation. That might be as simple as rules-based AI software to conduct quality control for existing workflows, or as complex as training neural networks to fully replace certain time-consuming tasks of data and image interpretation that are currently carried out by human experts.
The goal of this Special Issue of Geosciences is to assemble a collection of a dozen or more papers exploring the state-of-the-art in the automated processing and interpretation of the large, new datasets for applied Earth science made possible by modern sensors. Papers highlighting progress in individual fields are welcome, as well as those promoting new insights to be gained by studying data from integrated, multi-physics surveys.
Prof. Dr. Michael Oristaglio
Guest Editor
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Keywords
- machine learning (ML)
- artificial intelligence (AI)
- geophysical surveying
- environmental monitoring
- resource exploration
- reservoir management
- time-lapse geophysics
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