SigMT: An open-source Python package for magnetotelluric data processing

The magnetotelluric (MT) data processing is often a time-consuming job due to the manual inspection of the time series and removal of noisy segments. Use of different data selection tools combined with the robust estimation of the impedances has enabled the automation of MT data processing to a larg...

Full description

Saved in:
Bibliographic Details
Published in:Computers & geosciences Vol. 171; p. 105270
Main Authors: Ajithabh, K.S., Patro, Prasanta K.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2023
Subjects:
automation
computer software
Data processing
Himalayan region
Magnetotellurics
Python package
SigMT
time series analysis
Himalayan region
Magnetotellurics
Data processing
SigMT
Python package
ISSN:0098-3004
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The magnetotelluric (MT) data processing is often a time-consuming job due to the manual inspection of the time series and removal of noisy segments. Use of different data selection tools combined with the robust estimation of the impedances has enabled the automation of MT data processing to a large extent. In this paper, we introduce an open-source Python package, named ‘SigMT’ for the automated MT data processing. This Python package offers Python scripts to estimate MT impedances and tipper data from the raw time series data. The overview of the SigMT package explaining different steps involved in the processing is discussed in this paper. Different data selection tools such as Mahalanobis distance based-, coherency threshold based-, polarization direction based-selection tools are integrated in the package. The incorporation of different data selection tools with robust estimation technique delivers best estimates of the impedances. SigMT is applied to the MT data from Himalaya with different selection tools and found to be yielding satisfactory results. •Open-source Python package for magnetotelluric data processing.•Automated processing of data, no need of any manual time series editing.•Availability of different data selection tools.•Robust estimation of the magnetotelluric impedance values.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0098-3004
DOI:10.1016/j.cageo.2022.105270