GEO Theses and Research Projects
Permanent URI for this collection
Browse
Recent Submissions
Item Disseminated phosphate content of the Bonneterre formation(Upper Cambrian) of Southeastern Missouri and its bearing on the paleoecology(Wichita State University, 1961-04) Lammons, James MonroeThe present study contributes new Knowledge to the understanding of the paleoecology of lower Paleozoic sediments. The stratigraphic relationship of disseminated phosphate in Paleozoic sediments is discussed and fully documented. The determination of several lithologic components of the Bonneterre formation which may have stratigraphic value, and which have definite paleoecologic value, are presented. A demonstration of the relationship between the variable phosphate contents of the biogenic, oolitic and glauconitic portions of the Bonneterre formation is shown in relation to the stable area during early Cambrian time, i.e., the St. Francois igneous mass. The relative concentrations of phosphate in the various carbonate rock types of the Bonneterre formation are reported. An explanation is forwarded which relates the phosphate concentrations found to the original permeability pathways believed to have been present in the early stages of lithification of the Bonneterre sediments. A review of the Bonneterre biota and of modern knowledge of the chemical composition of living biotas, is related to the variations of disseminated phosphate in the Bonneterre formation.Item The Morrowan and Chesteran Rocks of SouthWestern Kansas(Wichita State University, 1961-06) Gill Jr., H.W.The purpose of this report is to study the subsurface geology of the area of investigation with special emphasis on the pre-Permian strata. The stratigraphy of the Pennsylvanian and Mississippian systems has been emphasized because of their great petroleum importance.Item Chromium adsorption onto titanium nanoparticles in varying water chemistries(Wichita State University, 2024-05) Grams, Gavin Scott; Swindle, Andrew L.Chromium, a transition metal, is commonly used in metallurgical applications due to its corrosion resistance; in chemical applications, it is used as sodium dichromate. It has been documented, though, that it is toxic to humans, plants, and animals. Chromium exists in two stable forms in nature, Cr (III) and Cr (VI). Cr (VI) is toxic; however, Cr (III) is not. The use of chromium in industrial processes creates a significant risk of contamination and human exposure to the material working in those environments. Studies have shown chromium can be adsorbed onto TiO2 particles. This process of chromium adsorption could be of high value if it can be implemented in remediation efforts efficiently and sustainably. Due to the wide range in the variation of water compositions on Earth, it is essential to test a variety of the most common ions found in water, like calcium (Ca), sodium (Na), chloride (Cl), and bicarbonate (HCO3). This thesis investigates chromium adsorption in differing water compositions and determines whether adding compounds such as NaHCO3, Fe(NO3)3 9H2O, NaCl, and CaCl2 inhibits chromium adsorption.Item The application of machine learning algorithms for flood susceptibility assessment for the state of Kansas(Wichita State University, 2023-12) Rimal, Prashant; Demissie, Zelalem S.Flooding has been a significant problem in the United States (US) over the past century. Since 1996, more than 1,500 flood events have been recorded in Kansas, resulting in more than billions in losses. This project explored the use of machine learning and publicly available data to assess factors affecting flooding and develop a flood susceptibility map for Kansas at multiple resolutions. It aims to explore the major predictor variables or flood-controlling factors and the response of the Stack Generalization across multiple resolutions and scenarios. Six machine learning (ML) algorithms: Logistic Regression (LR); Random Forest (RF); Support Vector Machine (SVM); K-nearest neighbor (KNN); Adaptive Boosting (Ada Boost); Extreme Gradient Boosting (XG Boost) were employed to determine the most important factors influencing the susceptibility of an area to flooding. The learning set for the ML algorithms comprised geospatial datasets of thirteen flood-controlling factors: rainfall, elevation, slope, aspect, flow direction, flow accumulation, Topographic Wetness Index (TWI), distance from the nearest stream, evapotranspiration, land cover, impervious surface, land surface temperature, and hydrologic soil type. A total of 1,528 non-flood inventories were created for two different scenarios, with the only difference being the inclusion of stream buffers for overall analysis. The ML algorithms were compared and used to estimate flood susceptibility for each location in the geodatabase resulting in a flood-susceptibility map for both cases. Overall, testing results showed that the tree-based ensemble algorithms; XGB and RF ML models performed relatively well in both cases over multiple resolutions compared to other models in predicting flooding with an accuracy ranging from 0.82 to 0.97, respectively. Also, variable importance analysis depicted that predictor variables such as distance from the streams, hydrologic soil type, rainfall, elevation, and impervious surfaces significantly affect flood prediction.Item Evaluating machine learning models using seismic data sets from different settings(Wichita State University, 2023-12) Taskinen, Eldon; Demissie, Zelalem S.In prior decades the concept of using mathematical methods to predict earthquakes was considered infeasible. Recent advances in machine learning and predictive modeling offer promising avenues to potentially realize earthquake prediction. In order to test the viability of machine learning methods, experiments were made with earthquake datasets from Kansas and Puerto Rico. The two datasets were chosen for the distinct differences in their tectonic settings. Kansas has few major faults, with a largely inactive subsurface, this produced a smaller dataset with a few large clusters. Puerto Rico is complexly faulted, with an extremely active tectonic setting, this produced a larger dataset with a large number of small clusters. In order to test the effectiveness of these two datasets for machine learning and prediction they were run through four different machine learning algorithms including an LSTM model, Bi-LSTM model, Bi-LSTM model with attention, as well as a transformer algorithm. Not only were the four different machine learning methods compared against each other for accuracy but also the datasets as well. Conclusive findings show that the two different data sets favor different processing methods. The Kansas data performs the best with the Bi-LSTM with attention model, while the Puerto Rico data performs the best with the LSTM model. This is likely due to the tectonic settings of the two regions, since the Kansas dataset has less overall data, and earthquakes are concentrated in a few large clusters, while the Puerto Rico data set has a more even distribution.