[MGI39-03] Quantitative Logging Unit Classification with Hidden Markov Model
Keywords:Logging, Hidden Markov Model, Clustering
This study uses Hidden Markov Model (HMM) to classify logging data into log units. We consider that the hidden state corresponds to log units to be estimated. We assume gaussian distributions as the observable data generation probabilities. The average vector and covariance matrix characterize each log units. We estimate those parameters with Expectation-Maximization (EM) algorithm. When applying EM algorithm, we use K-means++ method to select initial values for the average vector. We determine the total number of clusters using the evidence values estimated through EM algorithm.
We applied HMM to several drilling sites around Japan. The total number of clusters are usually larger than the log units determined by onboard logging scientists. For easier geological interpretations, we apply hierarchical clustering to the estimated clusters. The distance between gaussian distributions are defined by the Earth Mover’s distance.