Delineation of geologic facies with statistical learning theory

Details Delineation of geologic facies with statistical learning theory Delineation of geologic facies with statistical learning theory

Sep 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004georl..3118502t&link_type=abstract

Geophysical Research Letters, Volume 31, Issue 18, CiteID L18502

Physics

Geophysics

2

Hydrology: Groundwater Hydrology, Hydrology: Stochastic Processes, Mathematical Geophysics: Modeling

Scientific paper

Insufficient site parameterization remains a major stumbling block for efficient and reliable prediction of flow and transport in a subsurface environment. The lack of sufficient parameter data is usually dealt with by treating relevant parameters as random fields, which enables one to employ various geostatistical and stochastic tools. The major conceptual difficulty with these techniques is that they rely on the ergodicity hypothesis to interchange spatial and ensemble statistics. Instead of treating deterministic material properties as random, we introduce tools from machine learning to deal with the sparsity of data. To demonstrate the relevance and advantages of this approach, we apply one of these tools, the Support Vector Machine, to delineate geologic facies from hydraulic conductivity data.

Affiliated with

Also associated with

No associations

Rating

Delineation of geologic facies with statistical learning theory does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Delineation of geologic facies with statistical learning theory, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Delineation of geologic facies with statistical learning theory will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-1065059