THERMAL HISTORY INVERSION FROM THERMOCHRONOMETRIC DATA AND COMPLEMENTARY INFORMATION: A REVIEW AND WHAT’S NEW

Thermal history inverse modeling has become one of the primary platforms for interpreting thermochronometric data. However, there are a number of established and evolving tools that produce outputs that are superficially similar but not equivalent. This talk will introduce new features in the HeFTy software, and compare them with both earlier HeFTy versions and other programs. The approach for combining multiple analyses into a single probability has been changed to Fisher’s method, improving both statistical accuracy and ease of plain-language interpretation. Generating and visualizing inversion results based on goodness of fit as opposed to probability produce similar-appearing figures but are very different operations. The former expresses the resolving power of the data, but depends on the uncertainty estimates being accurate, and is subject to null solutions if data show severe excess dispersion. The latter expresses the relative frequency that regions of time-temperature space are visited by the algorithm posing candidate paths, and is thus subject to the biases embedded in that algorithm, which are often unrecognized. A new implementation of the controlled random search algorithm has been added to HeFTy, which greatly accelerates convergence while trying to also explore the solution space as effectively as an unbiased Monte Carlo approach, so as to avoid providing an unrealistic impression of resolving power. A new time-depth modeling mode has been added that uses a 1-D thermal model to convert between depth and temperature, under the simplifying convention that depth change corresponds to erosion or deposition at the Earth surface. This mode approximates the thermal buffering effects of the crust, and allows temperature relationships between samples and the Earth surface to evolve in a physically appropriate fashion. Together, these improvements enable multi-sample modeling along an elevation transect, which including testing and constraining the timing of deformation, expressed as tilting, and topography development. In most cases, adding geological constraints during the modeling process is crucial for achieving meaningful results, as the resolving power of the thermochronometric data alone is usually limited, and the best-fitting results are not necessarily geologically realistic.