2,372 research outputs found
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization
Spontaneous self-organization is ubiquitous in systems far from thermodynamic
equilibrium. While organized structures that emerge dominate transport
properties, universal representations that identify and describe these key
objects remain elusive. Here, we introduce a theoretically-grounded framework
for describing emergent organization that, via data-driven algorithms, is
constructive in practice. Its building blocks are spacetime lightcones that
embody how information propagates across a system through local interactions.
We show that predictive equivalence classes of lightcones -- local causal
states -- capture organized behaviors and coherent structures in complex
spatiotemporal systems. Employing an unsupervised physics-informed machine
learning algorithm and a high-performance computing implementation, we
demonstrate automatically discovering coherent structures in two real world
domain science problems. We show that local causal states identify vortices and
track their power-law decay behavior in two-dimensional fluid turbulence. We
then show how to detect and track familiar extreme weather events -- hurricanes
and atmospheric rivers -- and discover other novel coherent structures
associated with precipitation extremes in high-resolution climate data at the
grid-cell level
Characterization of hydrothermal alteration along geothermal wells using unsupervised machine-learning analysis of X-ray powder diffraction data
Zonal distribution of hydrothermal alteration in and around geothermal fields is important for understanding the hydrothermal environment. In this study, we assessed the performance of three unsupervised classification algorithms—K-mean clustering, the Gaussian mixture model, and agglomerative clustering—in automated categorization of alteration minerals along wells. As quantitative data for classification, we focused on the quartz indices of alteration minerals obtained from rock cuttings, which were calculated from X-ray powder diffraction measurements. The classification algorithms were first examined by applying synthetic data and then applied to data on rock cuttings obtained from two wells in the Hachimantai geothermal field in Japan. Of the three algorithms, our results showed that the Gaussian mixture model provides classes that are reliable and relatively easy to interpret. Furthermore, an integrated interpretation of different classification results provided more detailed features buried within the quartz indices. Application to the Hachimantai geothermal field data showed that lithological boundaries underpin the data and revealed the lateral connection between wells. The method’s performance is underscored by its ability to interpret multi-component data related to quartz indices
Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Automatic Stratigraphic Interpretation of Oil Sand wells from well logs datasets typically
involve recognizing the patterns of the well logs. This is done through classification of the well
log response into relatively homogenous subgroups based on eletrofacies and lithofacies. The
electrofacies based classification involves identifying clusters in the well log response that reflect
‘similar’ minerals and lithofacies within the logged interval. The identification of lithofacies
relies on core data analysis which can be expensive and time consuming as against the
electrofacies which are straight forward and inexpensive. To date, challenges of interpreting as
well as correlating well log data has been on the increase especially when it involves numerous
wellbore that manual analysis is almost impossible.
This thesis investigates the possibilities for an automatic stratigraphic interpretation of an Oil
Sand through statistical pattern recognition and rule-based (Artificial Intelligence) method. The
idea involves seeking high density clusters in the multivariate space log data, in order to define
classes of similar log responses. A hierarchical clustering algorithm was implemented in each of
the wellbores and these clusters and classifies the wells in four classes that represent the
lithologic information of the wells. These classes known as electrofacies are calibrated using a
developed decision rules which identify four lithology -Sand, Sand-shale, Shale-sand and Shale in the gamma ray log data. These form the basis of correlation to generate a subsurface model
Cluster analysis of multiple planetary flow regimes
A modified cluster analysis method was developed to identify spatial patterns of planetary flow regimes, and to study transitions between them. This method was applied first to a simple deterministic model and second to Northern Hemisphere (NH) 500 mb data. The dynamical model is governed by the fully-nonlinear, equivalent-barotropic vorticity equation on the sphere. Clusters of point in the model's phase space are associated with either a few persistent or with many transient events. Two stationary clusters have patterns similar to unstable stationary model solutions, zonal, or blocked. Transient clusters of wave trains serve as way stations between the stationary ones. For the NH data, cluster analysis was performed in the subspace of the first seven empirical orthogonal functions (EOFs). Stationary clusters are found in the low-frequency band of more than 10 days, and transient clusters in the bandpass frequency window between 2.5 and 6 days. In the low-frequency band three pairs of clusters determine, respectively, EOFs 1, 2, and 3. They exhibit well-known regional features, such as blocking, the Pacific/North American (PNA) pattern and wave trains. Both model and low-pass data show strong bimodality. Clusters in the bandpass window show wave-train patterns in the two jet exit regions. They are related, as in the model, to transitions between stationary clusters
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