000			02911nam a22002897a 4500
008			260609t2025 bx ab||| |||| 00| 0 eng d
020			_qhardback
040			_aUniversiti Teknologi Brunei _beng _cUTB
084			_aUTB 120 REPORT, THESIS & DISSERTATION _aRTDS 422
100	1		_aKim, Ahram _eauthor.
245	1	0	_aCompositional kriging for geological heterogeneity : _benhanced spatial orediction framework / _cAhram Kim
260			_aBandar Seri Begawan : _bUniversiti Teknologi Brunei, _c2025.
300			_axviii, 260 pages : _billustrations, maps ; _c30 cm.
500			_aSubmitted in fulfilment of the requirements for the degree of Doctor in Philosophy.
500			_aABSRACT This thesis presents an advanced spatial prediction framework based on compositional kriging for the analvsis of datasets that exhibit both compositional constraints and spatial heterogeneity. By integrating isometric log-ratio (il) transformations, the method preserves the geometry of the simplex space, ensuring statistically coherent spatial modelling of compositional data. The study applies this framework across four diverse case studies to demonstrate its adaptability and accuracy: demographic compositions in Texas, sediment facies on Long Island, coal seam thickness in Queensland, and grain size distributions from outcrops in Brunei. In the Long Island case, compositional kriging was compared with ordinary kriging and showed improved spatial interpolation accuracy, as validated by metrics such as Root Mean Square Error and Mean Squared Error. For the other datasets, compositional kriging produced accurate and reliable spatial predictions, with interpolated distribution shapes closelv matching the original data and effectivelv capturing spatial variability. The interpolated distribution shapes closely matched the original data in each case, underscoring the method's reliability. It makes a significant contribution to spatial statistics by integrating compositional data analysis with geostatistical modelling. The proposed approach provides a robust, accurate, and flexible tool for geoscientific prediction tasks, particularly where data are multimodal and spatially dependent. The results highlight compositional kriging's potential as a preferred method for future studies involving complex spatial datasets.
502			_aDissertation (Doctor of Philosophy) - Universiti Teknologi Brunei (2025)
504			_aIncludes references and appendix
610		4	_aUnversiti Teknologi Brunei _vThesis
610		4	_aUniversiti Teknologi Brunei _vFinal Year Report
650		4	_aDissertation, Academic
650		4	_aThesis writing
650		4	_aDissertation Universiti Teknologi Brunei
650		4	_aEngineering _vPetroleum and Chemical
710			_aUniversiti Teknologi Brunei
942			_2lc _cRTDS
998			_eReport, Thesis & Dissertation _s850594 : 002466 c.1 UTB _xUniversiti Teknologi Brunei
999			_c24236 _d24236