↓ Skip to main content

Using Machine Learning Techniques with Incomplete Polarity Datasets to Improve Earthquake Focal Mechanism Determination

Overview of attention for article published in Seismological Research Letters, September 2022
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
4 X users

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
16 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Using Machine Learning Techniques with Incomplete Polarity Datasets to Improve Earthquake Focal Mechanism Determination
Published in
Seismological Research Letters, September 2022
DOI 10.1785/0220220103
Authors

Robert J. Skoumal, David R. Shelly, Jeanne L. Hardebeck

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 25%
Student > Master 2 13%
Student > Doctoral Student 1 6%
Other 1 6%
Professor > Associate Professor 1 6%
Other 0 0%
Unknown 7 44%
Readers by discipline Count As %
Earth and Planetary Sciences 8 50%
Nursing and Health Professions 1 6%
Unknown 7 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 October 2022.
All research outputs
#13,856,595
of 23,476,369 outputs
Outputs from Seismological Research Letters
#894
of 1,396 outputs
Outputs of similar age
#179,362
of 434,840 outputs
Outputs of similar age from Seismological Research Letters
#28
of 71 outputs
Altmetric has tracked 23,476,369 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,396 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 434,840 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 71 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.