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The NGL Supported Modeling Team (SMT) members are Kristin J. Ulmer, Kenneth L. Hudson, Scott J. Brandenberg, Jonathan P. Stewart, Paolo Zimmaro, and Steven L. Kramer. We developed tools that process cone penetration test data, including inverse-filtering to remove thin layer effects following Boulanger and DeJong (2018), an agglomerative clustering algorithm by Hudson et al. (2024) to identify stratigraphic layers from inverse-filtered cone data, and a number of additional helper functions to compute soil behavior type index, overburden- and fines-corrected cone tip resistance, etc.

The Python code developed by the supported modeling team contains the following functions primary functions. These functions use helper functions documented on the corresponding wiki pages.

function	description
cpt_inverse_filter(qt, depth, **kwargs)	Apply Boulanger and DeJong (2018) inverse filter to correct for thin layer effects
cpt_layering(qc1Ncs, Ic, depth, **kwargs)	Apply Hudson et al. (2024) clustering algorithm to identify layers in CPT profile
get_pfs(Ic)	Computes probability factor for liquefaction susceptibility. Ulmer et al. (2024)
get_pfts(csrm_hat, crr_hat)	Computes probability factor for liquefaction triggering conditional on susceptibility. Ulmer et al. (2024)
get_pfmt(ztop, Ic)	Computes probability factor for manifestation of a layer conditional on triggering of the layer. Ulmer et al. (2024)
get_pmp(pfmt, pfts, pfs, Ksat, t)	Computes probability of manifestation at surface of profile. Ulmer et al. (2024)