An official website of the United States government
Characterization of soft-sediment deformation: Detection of cryptoslumps using magnetic methods (Dataset)
Paleomagnetic, rock magnetic, or geomagnetic data found in the MagIC data repository from a paper titled: Characterization of soft-sediment deformation: Detection of cryptoslumps using magnetic methods
more »
« less
- Award ID(s):
- 2126298
- PAR ID:
- 10558644
- Publisher / Repository:
- Magnetics Information Consortium (MagIC)
- Date Published:
- Subject(s) / Keyword(s):
- Sedimentary Sediment Layer Limey shale Shallow Marine Sediments 46000000 49000000 Years BP
- Format(s):
- Medium: X
- Location:
- (Latitude:32.8544; Longitude:-117.2445); (Latitude:32.8694; Longitude:-117.2537); (Latitude:32.8694; Longitude:-117.2537); (Latitude:32.8694; Longitude:-117.2537)
- Right(s):
- Creative Commons Attribution 4.0 International
- Institution:
- Paleomagnetic Lab Scripps Institution Of Oceanography, UCSD, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
The coronal magnetic field over NOAA Active Region 11,429 during a X5.4 solar flare on 7 March 2012 is modeled using optimization based Non-Linear Force-Free Field extrapolation. Specifically, 3D magnetic fields were modeled for 11 timesteps using the 12-min cadence Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager photospheric vector magnetic field data, spanning a time period of 1 hour before through 1 hour after the start of the flare. Using the modeled coronal magnetic field data, seven different magnetic field parameters were calculated for 3 separate regions: areas with surface | B z |≥ 300 G, areas of flare brightening seen in SDO Atmospheric Imaging Assembly imagery, and areas with surface | B | ≥ 1000 G and high twist. Time series of the magnetic field parameters were analyzed to investigate the evolution of the coronal field during the solar flare event and discern pre-eruptive signatures. The data shows that areas with | B | ≥ 1000 G and | T w |≥ 1.5 align well with areas of initial flare brightening during the pre-flare phase and at the beginning of the eruptive phase of the flare, suggesting that measurements of the photospheric magnetic field strength and twist can be used to predict the flare location within an active region if triggered. Additionally, the evolution of seven investigated magnetic field parameters indicated a destabilizing magnetic field structure that could likely erupt.more » « less
-
Abstract Axial Seamount in the northeast Pacific erupted in 2015, 2011, and 1998. Although monitored by the Regional Cabled Array of the Ocean Observatory Initiative, few magnetic surveys have been conducted over the region. This study uses high‐resolution magnetic data over the seamount collected by autonomous underwater vehicleSentryduring three years (2015, 2017, and 2020). The goal is to investigate whether there are temporal changes in the near‐surface magnetic field observable over the time scale of one volcanic cycle. We compare magnetic maps from repeated tracklines from each year. We find maps of the yearly difference in magnetization show coherent patterns that are not random. The central region of the caldera has become more magnetic during recent years, suggesting cooling of the surficial lava flows since 2015.Sentrydata are more sensitive to shallow crustal structure compared to sea surface data which show longer wavelength anomalies extending deeper into the crust.more » « less
-
We develop an open-access database that provides a large array of datasets specialized for magnetic compounds as well as magnetic clusters. Our focus is on rare-earth-free magnets. Available datasets include (i) crystallography, (ii) thermodynamic properties, such as the formation energy, and (iii) magnetic properties that are essential for magnetic-material design. Our database features a large number of stable and metastable structures discovered through our adaptive genetic algorithm (AGA) searches. Many of these AGA structures have better magnetic properties when compared to those of the existing rare-earth-free magnets and the theoretical structures in other databases. Our database places particular emphasis on site-specific magnetic data, which are obtained by high-throughput first-principles calculations. Such site-resolved data are indispensable for machine-learning modeling. We illustrate how our data-intensive methods promote efficiency of the experimental discovery of new magnetic materials. Our database provides massive datasets that will facilitate an efficient computational screening, machine-learning-assisted design, and the experimental fabrication of new promising magnets.more » « less
