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ZENODO
Software . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Software . 2026
License: CC BY
Data sources: Datacite
ZENODO
Software . 2026
License: CC BY
Data sources: Datacite
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Magnetic Data Processing: Python

Authors: Ingale, Vaibhav Vijay;

Magnetic Data Processing: Python

Abstract

This repository contains Python scripts and functions for processing magnetic data collected by Autonomous Underwater Vehicles (AUVs) like Sentry. It provides a complete workflow from raw data loading to processed magnetic anomalies ready for analysis. The tools support spin fitting calibration to correct platform-induced magnetic distortions, coordinate rotation into North-East-Down (NED) frame, temperature and heading-dependent corrections, and robust filtering using median and iterative smoothing. Users can process selected straight segments or full dives, with or without spin maneuvers, and compute magnetic anomalies relative to the IGRF model. The repository also implements crossover correction to adjust anomalies across dives and 2-D upward continuationusing the Guspi (1987) method. Outputs include .mat and .txt files containing timestamps, locations, vehicle orientation, raw and corrected magnetic components, and computed anomalies. This repository is designed for scientific analysis of seafloor magnetic anomalies and can be applied to AUV datasets for mid-ocean ridge studies, hydrothermal vent exploration, and marine geophysics research. Pre-requisites: Python 3.10 or newer, with navigation and magnetometer .mat files generated from AUV surveys.Input sentry files are saved at: https://doi.org/10.5281/zenodo.18298901

Keywords

Magnetic anomalies, Autonomous Underwater Vehicles, Upward Continuation, Crossover analysis

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average