HelioWebDiego Melgar
flowchart
A[Diego Melgar]
AC["Associated Concepts (23)"]
AW["Authored Works (18)"]
CA["Linked Co-Authors (19)"]
CI["Linked Collaborating Institutions (21)"]
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AW== author ==>CA
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click AC "#associated-concepts"
click AW "#authored-works"
click CA "#co-authors"
click CI "#collaborating-institutions"
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- ORCiD
- https://orcid.org/0000-0001-6259-1852
- OpenAlex ID
- https://openalex.org/A5007357041 (API record)
Associated Concepts [?]
- Geology
- Engineering
- Seismology
- Physics
- Computer science
- Geography
- Aerospace engineering
- Telecommunications
- Biology
- Paleontology
- Geodesy
- Operating system
- Civil engineering
- Astronomy
- Quantum mechanics
- Global Positioning System
- Tectonics
- Thermodynamics
- Mathematics
- Slip (aerodynamics)
- Warning system
- Subduction
- Oceanography
Authored Works
sorted by decreasing year, and then by display-name
- What Did the 1700 AD Cascadia Earthquake Look Like? : Correlating Deformation and Tsunami Inundation Modeling with Paleoseismic Clues
- Source Characterization of the M6.4 Mainshock in the 2020 Southwestern Puerto Rico Seismic Sequence with Seismic and Geodetic Observations
- Rapid Ground Motion Forecasting for Large Earthquakes with HR-GNSS and Deep Learning
- Rapid Estimation of Single-Station Earthquake Magnitudes with Machine Learning on a Global Scale
- Measuring and Forecasting the Background Open Ocean Tsunami Spectrum
- Geodetic Constraints on the Slow Slip Source and Transient Detection with Machine Learning: Kinematic Slow Slip and Synthetic Displacement Data
- Automated DInSAR + GNSS Integrated Time Series and Deformation Maps for Machine Learning and Volcanic Early Warning Applications
- A comparison of foraminiferal and diatom-based transfer function estimates of coseismic subsidence during the 1700 CE earthquake along the Oregon and California coasts
- Three-Dimensional Kinematics and the Crustal Deformation of Tsunami Earthquakes
- Surface Deformation from July 29th Alaska 8.2 Magnitude Earthquake via Sentinel-1 InSAR and GNSS
- Source characteristics and tsunami energetics of the 2020 Mw 7.0 Neon Karlovasion (Samos) Earthquake in the Eastern Aegean Sea
- Rapid Determination of Magnitude, Location, and Source Extent with HR-GNSS and Deep Learning
- Megathrust co-seismic slip during the Sand Point, Alaska strike-slip Earthquake
- Expansion of the NEIC finite fault modeling capabilities to include regional seismic and geodetic data
- Detecting Earthquakes in Noisy Real-Time GNSS Data with Machine Learning
- Collaborations on Advancing GNSS Based Technology for Tsunami Forecasting, Hazard and Loss Estimates
- Can probabilistic hazard estimates reliably capture M9 events? A Case study from the Japan trench
- A Year of Rupture: Kinematic and Postseismic Modeling of the Mw 7.8 Simeonof Island, Mw 7.6 Sand Point, and Mw 8.2 Chignik Earthquakes in Alaska
Linked Co-Authors
- Amanda Thomas
- B. W. Crowell
- D. Goldberg
- D. Golriz
- D. Mencin
- D. Small
- David T. Sandwell
- Hank M. Cole
- Harvey M. Kelsey
- K. F. Tiampo
- Margarita Solares-Colón
- Robert C. Witter
- S. Riquelme
- Sean R Santellanes
- T. I. Melbourne
- V. J. Sahakian
- William L. Yeck
- Xiaohua Xu
- Yehuda Bock
Linked Collaborating Institutions
- Central Washington University
- Durham University, UK
- Humboldt State University, California
- Istanbul Technical University, Turkey
- National Observatory of Athens, Greece
- Rutgers University, New Jersey
- U.S. Geological Survey
- U.S. Geological Survey, Alaska
- UMass Amherst
- University of California, San Diego, Scripps Institution of Oceanography
- University of Chile
- University of Colorado, Boulder
- University of Hamburg, Germany
- University of Iceland
- University of North Carolina, Wilmington
- University of Oregon
- University of Rhode Island
- University of Texas, Austin
- University of Utah
- University of Washington, Seattle
- Virginia Polytechnic Institute and State University
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