  Asteroseismology provides global stellar parameters such as masses, radii or
surface gravities using the mean global seismic parameters as well as the
effective temperature for thousands of low-mass stars $(0.8 M_\odot <M<3
M_\odot)$. This methodology has been successfully applied to stars in which
acoustic modes excited by turbulent convection are measured. Other techniques
such as the Flicker can also be used to determine stellar surface gravities,
but only works for $\log{g}$ above $2.5$ dex. In this work, we present a new
metric called FliPer (the acronym stands for Flicker in spectral power density,
in opposition to the standard Flicker measurement which is computed in the time
domain that is able to extend the range for which reliable surface gravities
can be obtained ($0.1<\log{g}<4.6$ dex) without performing any seismic analysis
for stars brighter than $\textit{Kp}$ $<$ 14. FliPer takes into account the
average variability of a star measured in the power density spectrum in a given
range of frequencies. However, FliPer values calculated on several ranges of
frequency are required to better characterize a star. Using a large set of
asteroseismic targets it is possible to calibrate the behavior of surface
gravity with FliPer through machine learning. This calibration made with a
random forest regressor covers a wide range of surface gravities from
main-sequence stars to subgiants and red giants, with very small uncertainties
from $0.04$ to $0.1$ dex. FliPer values can be inserted in automatic global
seismic pipelines to either give an estimation of the stellar surface gravity
or to assess the quality of the seismic results by detecting any outliers in
the obtained $\nu_{max}$ values. FliPer also constrain the surface gravities of
main-sequence dwarfs using only long cadence data for which the Nyquist
frequency is too low to measure the acoustic-mode properties.
