  To reconstruct the impact parameter distributions from the selected events
sample or centrality, which is defined by two-observables, at intermediate
energy heavy ion collisions, we extend the approach proposed by Das \textit{et
al.} [Phys. Rev. C 97, 014905 (2018)], Rogly \textit{et al.} [Phys. Rev. C 98,
024902 (2018)], and Frankland \textit{et al.} [Phys. Rev. C 104, 034609
(2021)]. Based on deep investigations of the fluctuation mechanism, we found
that the intrinsic fluctuations are mainly generated in the microscopic
stochasticity of initialization and nucleon-nucleon collisions in the
nonequilibrium process of heavy ion collisions, and this leads the observables
to fluctuate with respect to impact parameter in a Gaussian form. In this work,
the multiplicity of the charged particles and the total transverse momentum of
the light charged particles are used simultaneously to model-independently
reconstruct the impact parameter distributions for selected events or
centrality based on the Bayesian method. For sorting the centrality with two
observables, we propose to use the $K$-means clustering method (an unsupervised
machine learning algorithm), which can automatically sort events when the class
number is given. Furthermore, the reconstructed impact parameter distributions
from data of the two observables can be used to learn the correlation between
multiplicity and transverse momentum at different centralities, which may be
useful for understanding the fragmentation mechanism.
