Abstract: A method, including: obtaining a scale-depth or scale-time phase image of a continuous wavelet transform of an input signal, the scale-depth or scale-time phase image including oval-shaped circular patterns observed on the mirrored phase image; and extracting, with a computer, hierarchical multiscale intervals from the scale-depth or scale-time phase image, wherein the hierarchical multiscale intervals correspond to the oval-shaped circular patterns observed on the mirrored scale-depth or scale-time phase image of the continuous wavelet transform of the input signal. Another method includes: characterizing, with a computer, curve shapes of intervals of a signal using beta distribution; and visualizing and analyzing, with the computer, shape parameters of the curve shapes on a shape-parameter crossplot.

Abstract: A method, including: obtaining a scale-depth or scale-time phase image of a continuous wavelet transform of an input signal, the scale-depth or scale-time phase image including oval-shaped circular patterns observed on the mirrored phase image; and extracting, with a computer, hierarchical multiscale intervals from the scale-depth or scale-time phase image, wherein the hierarchical multiscale intervals correspond to the oval-shaped circular patterns observed on the mirrored scale-depth or scale-time phase image of the continuous wavelet transform of the input signal. Another method includes: characterizing, with a computer, curve shapes of intervals of a signal using beta distribution; and visualizing and analyzing, with the computer, shape parameters of the curve shapes on a shape-parameter crossplot.

Abstract: A method for ordering electrofacies to assist in identification of mineral deposits is disclosed. Automated ordering of electrofacies allows geologists to draw inferences about the geological settings in which sediment deposit occurred without directly examining core samples or outcrops. The electrofacies order is determined by (a) training a one-dimensional linear self-organizing map to form an initial neural network that includes a plurality of neurons. The number of neurons is small in comparison to the number of electrofacies kernels (i.e., not greater than one-third the number of electrofacies kernels). (b1) A neuron is selected from the initial neural network. In the next step (b2), the processor determines if more than one electrofacies kernel is attached to the neuron. (b3) If more than one electrofacies kernel is attached to the neuron, then the neuron is split into the number of electrofacies kernels attached to the neuron.

Abstract: A method for ordering electrofacies to assist in identification of mineral deposits is disclosed. Automated ordering of electrofacies allows geologists to draw inferences about the geological settings in which sediment deposit occurred without directly examining core samples or outcrops. The electrofacies order is determined by (a) training a one-dimensional linear self-organizing map to form an initial neural network that includes a plurality of neurons. The number of neurons is small in comparison to the number of electrofacies kernels (i.e., not greater than one-third the number of electrofacies kernels). (b1) A neuron is selected from the initial neural network. In the next step (b2), the processor determines if more than one electrofacies kernel is attached to the neuron. (b3) If more than one electrofacies kernel is attached to the neuron, then the neuron is split into the number of electrofacies kernels attached to the neuron.

Abstract: An apparatus and method for obtaining facies of geological formations for identifying mineral deposits is disclosed. Logging instruments are moved in a bore hole to produce log measurements at successive levels of the bore hole. The set of measurements at each such level of the bore hole interval is associated with reference sample points within a multidimensional space. The multidimensional scatter of sample points thus obtained is analyzed to determine a set of characteristic modes. The sample points associated with characteristic modes are grouped to identify clusters. A facies is designated for each of the clusters and a graphic representation of the succession of facies as a function of the depth is thus obtained. To identify the clusters, a “neighboring index” of each log measurement point in the data set is calculated. Next, small natural groups of points are formed based on the use of the neighboring index to determine a K-Nearest-Neighbor (KNN) attraction for each point.

Abstract: Method for automatic detection of planar heterogeneities crossing the stratification of an environment.