About the SP&M Lab
The Surface Processes and Modelling Laboratory (SP&M Lab) is a state-of-the-art computational facility established in January 2025, located on the third floor of the Mining and Mineral Resources Building. Funded by the NSF EPSCoR 'Climate Resilience through Multidisciplinary Big Data Learning, Prediction & Building Response Systems (CLIMBS)' award, our lab aims to advance Kentucky's climate resiliency through hazard assessment based on Kentucky-specific scientific research.
Hardware
- 5x workstations with: 14900KS CPU, 192 GB memory, 1x RTX 4080 Super GPU
- 2x Alienware with: 285K CPU, 64GB memory, 1x RTX 5080
- 1 modeling server with: 2 x 48-core Xeon, 2TB memory, 4 x RTX 4090 GPU
Workflows
- Brute force, Monte Carlo, & stochastic implementation of machine learning algorithms
- Canning custom scripts into GUI standalone programs
- Modeling 3D surfaces using satellite SAR data
- Processing drone-based LiDAR
Our Mission
The intent of the SP&M Lab is to combine and streamline complex workflows to develop integrated surface process models that will elucidate landscape evolution, hazard prediction & response under changing climate regimes, and associated risk to small rural communities.
Available Datasets
View All DatasetsAssessing the effects of anthropogenesis on Appalachian flood severity: An eastern Kentucky case study [dataset].
Featured Projects
View All Lab ProjectsFlood Modeling
Advanced flood modeling using sub-grid sampling techniques for computational efficiency while preserving critical channel detail. Our approach enables rapid analysis of flood scenarios across large geographic areas.
Products & Outcomes:
- 1st SP&M-enabled publication in review, 2nd in preparation
- 2 training workshops scheduled (UK-KGS-Morehead collaboration)
Monte Carlo Analysis
Comprehensive assessment of machine learning approaches for landslide susceptibility modeling in Kentucky's diverse geological settings. This research focuses on developing robust, statistically validated models for landslide hazard prediction.
Research Focus:
- Detailed analysis of relationship between inventory and susceptibility results
- Thousands of models generated within sensitivity assessment framework
- Comprehensive evaluation of different machine learning algorithms (SVM, LR, NB, BT)
- Statistical validation using AUC and model performance metrics
- Regional adaptation for Appalachian terrain characteristics
The project employs multiple machine learning algorithms including Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), and Boosted Trees (BT) to create the most accurate susceptibility models for Kentucky's unique geological conditions.
Status: active
Products & Outcomes:
Ongoing Activities:
- Statewide landslide inventory compilation and validation
- Multi-algorithm performance comparison study
- Climate-informed susceptibility modeling development
Latest SP&M News
View All NewsUK landslide research expands to Northern Kentucky and Cincinnati
The Kentucky Geological Survey (KGS) received a $182,961 grant from the U.S. Geological Survey to advance geological understanding and hazard preparedness for Northern Kentucky and Cincinnati. The project will produce landslide inventory maps and landslide susceptibility maps for the region that will benefit city planners, local governments and landowners.
CLIMBS Researchers Go Global in Finding Hazard Solutions through NSF’s CLaSH Initiative
Geologic hazards are complicated, connected, and continuous. While extreme precipitation events can cause catastrophic flooding, the intense precipitation can also induce widespread landslide activity. The landslide deposits can block small streams or send pulses of materials into rivers, exacerbating the destructiveness of the flood by changing the nature of the flow and providing larger debris that can impact infrastructure. Similarly, wildfire burns vegetation, making hillslopes more susceptible to landsliding during intense precipitation. The positive feedback between one hazard exacerbating the severity of another hazard is referred to as a hazard cascade. Understanding hazard cascades and their impacts on infrastructure and people is difficult, and exactly the type of challenge addressed through the National Science Foundation’s new CLaSH – Center for Land Surface Hazards (CLaSH). Kentucky researchers are playing a key role.
SP&M Lab Updates: Exciting Developments in Landslide Research and Flood Modelling
The Surface Processes & Modelling Laboratory (SP&M) at the Kentucky Geological Survey (KGS) is making significant strides in research and collaboration, including welcoming an esteemed international scholar, publishing research, and securing new funding. The SP&M Lab is funded by the NSF EPSCoR 'Climate Resilience through Multidisciplinary Big Data Learning, Prediction & Building Response Systems (CLIMBS)' award, which aims to advance Kentucky's climate resiliency through hazard assessment based on Kentucky-specific scientific research.