Ricardo A. Olea, Ph.D.
Ricardo Olea is a Research Mathematical Statistician with the USGS Geology, Energy & Minerals (GEM) Science Center in Reston, VA.
Ricardo has extensive experience in quantitative modeling in the earth sciences and public health, primarily in the areas of petroleum geology and engineering, coal resource assessment, geostatistics, classical statistics, compositional data modeling, economic evaluation, well log analysis, marine geology, medical geography, geophysics, and geohydrology.
Professional Experience
2006 to Present: U.S. Geological Survey. Statistical Support: Coal Resource Assessment Methodology Implementation; Mathematical Model Enhancement; Exploitation of subsurface geologic resources in the land loss in coastal Louisiana; Advance the application of statistics to the earth sciences world-wide; and Reducing the risk of explosions at underground coal mines
Department of Petroleum Engineering, Stanford University
Marine Geology Section, Baltic Research Institute, University of Rostock, Warnemünde, Germany
Department of Environmental Sciences and Engineering, School of Public Health, University of North Carolina Chapel Hill
Research Scientist, Kansas Geological Survey, Lawrence, Kansas
Exploration Seismologist, Geostatistician, Log Analyst, Economic Analyst, and Reservoir Engineer, National Oil Company of Chile (ENAP)
Education and Certifications
Ph.D. Engineering, Chemical and Petroleum Engineering, University of Kansas
Mining Engineer Degree, University of Chile
Affiliations and Memberships*
Compositional Data Association, Member
Society of Petroleum Engineers, Member
American Association of Petroleum Geologists, Member
International Association for Mathematical Geosciences, Member
Sigma Xi and serves as an Associate Editor of the Springer journal Stochastic Environmental Research and Risk Assessment
International Association for Mathematical Geology (IAMG), Secretary General (1992–96) and President (1996–2000)
Honors and Awards
IAMG Krumbein Medal - Science and Professional Contributions, 2004
Science and Products
Inference of distributional parameters from compositional samples containing nondetects
Experimental geostatistical model of a continuous gas accumulation, Rocky Mountains, Utah
Kolmogorov-Smirnov test for spatially correlated data
Measuring CO2 emissions from coal fires in the U.S.
Basic Statistical Concepts and Methods for Earth Scientists
Recent results on the spatiotemporal modelling and comparative analysis of Black Death and bubonic plague epidemics
Declustering of clustered preferential sampling for histogram and semivariogram inference
CORRELATOR 5.2 - A program for interactive lithostratigraphic correlation of wireline logs
Subdivision of Holocene Baltic sea sediments by their physical properties [Gliederung holozaner ostseesedimente nach physikalischen Eigenschaften]
Singularity and Nonnormality in the Classification of Compositional Data
Kriging: Understanding allays intimidation
Compensating for estimation smoothing in kriging
Non-USGS Publications**
Martín-Fernández, J. A., Palarea-Alabaladejo, J., and Olea, R.A., 2011, Dealing with zeros. In V. Pawlowsky-Glahn and A. Buccianti (eds.), Compositional Data Analysis—Theory and Applications: Wiley & Sons, Ltd, Chichester, UK, p. 43–58.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Inference of distributional parameters from compositional samples containing nondetects
Experimental geostatistical model of a continuous gas accumulation, Rocky Mountains, Utah
Kolmogorov-Smirnov test for spatially correlated data
Measuring CO2 emissions from coal fires in the U.S.
Basic Statistical Concepts and Methods for Earth Scientists
Recent results on the spatiotemporal modelling and comparative analysis of Black Death and bubonic plague epidemics
Declustering of clustered preferential sampling for histogram and semivariogram inference
CORRELATOR 5.2 - A program for interactive lithostratigraphic correlation of wireline logs
Subdivision of Holocene Baltic sea sediments by their physical properties [Gliederung holozaner ostseesedimente nach physikalischen Eigenschaften]
Singularity and Nonnormality in the Classification of Compositional Data
Kriging: Understanding allays intimidation
Compensating for estimation smoothing in kriging
Non-USGS Publications**
Martín-Fernández, J. A., Palarea-Alabaladejo, J., and Olea, R.A., 2011, Dealing with zeros. In V. Pawlowsky-Glahn and A. Buccianti (eds.), Compositional Data Analysis—Theory and Applications: Wiley & Sons, Ltd, Chichester, UK, p. 43–58.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government