Automated full-pattern-fitting of illite polytypes: Standardizing quantification and uncertainty for Illite Age Analysis

Quantitative analysis of illite polytypes is essential for constraining the thermal and burial history of sedimentary rocks and dating fault slip events through Illite Age Analysis (IAA).However, traditional manual full-pattern fitting (FPF) of X-ray diffraction (XRD) data involves subjective interpretation, inconsistent reproducibility, and poor error quantification.To overcome these limitations, we developed a Pythonbased automated algorithm integrating synthetic reference patterns generated by WILDFIRE-© with systematic baseline correction, polytypespecific weighting, Monte Carlo regression, and uncertainty evaluation.The method excludes quartz interference and focuses on diagnostic illite reflections in the 22.5-26° and 27-32.5°2θ (Cu-Kα) ranges.Validation against expert-based FPF results from eight fault gouge samples (YC4, YC5-1, YC5-2, STB1-9, STB8-14, SNN-03, SNN-10) from major Korean fault zones yielded goodness-of-fit R values consistently below 10%.Both linear and asymmetric least squares baseline corrections effectively accommodate compositional variations.Monte Carlo error propagation provides realistic ±1σ uncertainties previously unavailable in manual analyses.Combined with published K-Ar illite ages, the automated FPF-IAA reproduces established age trends and fault reactivation episodes while greatly enhancing reproducibility and transparency.This framework offers a standardized and transparent alternative to proprietary tools, with representative input files, parameter settings, and outputs provided to support reproducibility, advancing clay mineral-based geochronology studies.