Geology topics

Karl Kappler

Publications and source records attributed to Karl Kappler.

4 recordsLinked to original sources

USGS2024

Aurora: An open-source Python implementation of the EMTF package for magnetotelluric data processing using MTH5 and mt-metadata

The Aurora software package robustly estimates single station and remote reference electromagnetic transfer functions (TFs) from magnetotelluric (MT) time series. Aurora is part of an open-source processing workflow that leverages the self-describing data container MTH5, which in turn leverages the general mt-metadata framework to manage metadata. These pre-existing packages simplify the processing by providing managed data structures, allowing for transfer functions to be generated with only a few lines of code. The processing depends on two inputs -- a table defining the data to use for TF estimation, and a JSON file specifying the processing parameters, both of which are generated automatically, and can be modified if desired. Output TFs are returned as mt_metadata objects, and can be exported to a variety of common formats for plotting, modeling and inversion.

Journal of Open Source Software↗

USGS2023

Determining ULF wave contributions to geomagnetically induced currents: The important role of sampling rate

Past studies found that large-amplitude geomagnetically induced current (GIC) related to magnetospheric Ultra Low Frequency (ULF) waves tend to be associated with periods >120 s at magnetic latitudes >60°, with comparatively (a) smaller GIC amplitudes at lower latitudes and shorter wave periods and (b) fewer reports of waves associated with GIC at lower latitudes. ULF wave periods generally decrease with decreasing latitude; thus, we examine whether these trends might be due, in part, to the undersampling of ULF wave fields in commonly available measurements with 60 s sampling intervals. We use geomagnetic field ( B ), geoelectric field ( E ), and GIC measurements with 0.5–10 s sampling intervals during the 29–31 October 2003 geomagnetic storm to show that waves with periods <∼120 s were present during times with the largest amplitude E and GIC variations. These waves contributed to roughly half the maximum E and GIC values, including during times with the maximum GIC values reported over a 14-year monitoring interval in New Zealand. The undersampling of wave periods <120 s in 60 s measurements can preclude identification of the cause of the GIC during some time intervals. These results indicate (a) ULF waves with periods ≤120 s are an important contributor to large amplitude GIC variations, (b) the use of 0.1–1.0 Hz sampling rates reveals their contributions to B , E , and GIC, and (c) these waves' contributions are likely strongest at magnetic latitudes <60° where ULF waves often have periods <120 s.

Space Weather↗

USGS2022

MTH5: An archive and exchangeable data format for magnetotelluric time series data

Magnetotellurics (MT) is a passive electromagnetic geophysical method that measures variations in subsurface electrical resistivity. MT data are collected in the time domain and processed in the frequency domain to produce estimates of a transfer function representing the Earth’s electrical structure. Unfortunately, the MT community lacks metadata and data standards for time series data. As the community grows and findability, accessibility, interoperability, and reuse of digital assets (FAIR) data principles are enforced by government and funding agencies, a standard is needed for time series data. Presented here is a hierarchical data format (MTH5) that is logically formatted to how MT data are collected. Open-source Python packages are also described to read, write, and manipulate MTH5 files. These include a package to deal with metadata ( mt_metadata ) based on standards developed by the Working Group for Magnetotelluric Data Handling and Software assembled by the Incorporated Research Institutions for Seismology (IRIS), and mth5 : a package to interact with MTH5 files that uses mt_metadata . Example code and workflows are presented.

Computers & Geosciences↗

USGS2011

Documentation for a web site to serve ULF-EM (Ultra-Low Frequency Electromagnetic) data to the public

The Stanford Ultra-Low Frequency Electromagnetic (ULF-EM) Monitoring Project is recording naturally varying electromagnetic signals adjacent to active earthquake faults, in an attempt to establish whether there is any variation in these signals associated with earthquakes. Our project is collaborative between Stanford University, the U.S. Geological Survey (USGS), and UC Berkeley. Lead scientists are Simon Klemperer (Stanford University), Jonathan Glen (USGS) and Darcy Karakelian McPhee (USGS). Our initial sites are in the San Francisco Bay Area, monitoring different strands of the San Andreas fault system, at Stanford University's Jasper Ridge Biological Preserve (JRSC), Marin Headlands of the Golden Gate National Recreation Area (MHDL), and the UC Berkeley's Russell Reservation Field Station adjacent to Briones Regional Park (BRIB). In addition, we maintain in conjunction with the Berkeley Seismological Laboratory (BSL) two remote reference stations at the Bear Valley Ranch in Parkfield, Calif., (PKD) and the San Andreas Geophysical Observatory at Hollister, Calif., (SAO). Metadata about our site can be found at http://ulfem-data.stanford.edu/info.html. Site descriptions can be found at the BSL at http://seismo.berkeley.edu/, and seismic data can be obtained from the Northern California Earthquake Data Center at http://www.ncedc.org/. The site http://ulfem-data.stanford.edu/ allows access to data from the Stanford-USGS sites JRSC, MHDL and BRIB, as well as UC Berkeley sites PKD and SAO.

Open-File Report↗