James D. Bliss

Publications listing

Interests: 

Aggregate

Industrial Minerals

Spatial modeling 

Surficial processes

520-670-5502

Fax 520-670-5571

jbliss@usgs.gov

Education

Experience

I have worked for the USGS in Reston, Menlo Park, and Tucson since 1978. Previous experience was with the U.S. Environmental Protection Agency in Las Vegas involving computer and statistical analytical support of studies of nutrimental levels in US lakes and radon in ground water in central Florida. My USGS activities include operation of the National Geothermal Resources Information System, a national database of geothermal wells and springs used for a successfully completed national low temperature assessment; development of computer-based resource management tools for the Navajo Tribal government in Window Rock, Ariz.; and assisting with the establishment of the Center for Inter-American Mineral Resources Investigations (CIMRI) in Tucson in 1987. Research interests were focused on development of grade and tonnage models suitable for use in quantitative assessment for a number of mineral deposit types including low-sulfide gold quartz veins, and gold placers among others. A number of mineral resource assessments were completed some while participating in assessment teams. Mineral deposit modeling efforts included assesting in the development of mineral deposit spatial models, and in the development of models for industrial minerals (graphite, diamonds, pegmatites).

Current Activities

Current research focuses on development of models and methods suitable for quantitative assessment of materials suitable for use in construction including sand and gravel and crushed stone. This involves modeling engineering characteristics used to identify suitable materials within an associated geological situation. An assessment is underway of the sand and gravel in the Boise River Valley, Idaho, to explore how successfully these types of models, together with spatial information, can be used to forecast how much suitable aggregate may be present. A second effort is examining how probabilistic neural networks may be used in the classification of crushed carbonate rocks used as aggregate.