Page 196 - Physical Principles of Sedimentary Basin Analysis
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178                             Heat flow

                    50                                  0
                                       observations             observations
                                                        50
                   100   linear fit
                                                       100
                   150
                   depth [m]                          depth [m]   150   step
                   200
                                                       200
                                                           periodic    linear increase
                   250
                                                       250

                   300                                 300
                     6.5   7.0   7.5   8.0  8.5   9.0   −0.5    0.0    0.5    1.0     1.5
                               temperature [°C]                temperature difference [°C]
                                    (a)                                (b)
                 Figure 6.37. (a) Observations of temperature against depth and a linear fit against the stationary part
                 of the geotherm. (b) Optimization of three different models against the transient part of the data set.


                   Figure 6.37a shows temperature observations that indicates a higher surface temper-
                 ature in a period leading up to the present time. The data set is from a borehole site
                 in Canada (Mareschal, 2005). The stationary state is obtained by a linear fit of the data
                                                                 ◦
                 below 170 m, and it gives the surface temperature 6.07 C, and the thermal gradient
                              ◦
                 dT/dz = 9.15 C/km. Figure 6.37b shows the difference between the data and the sta-
                 tionary geotherm, which is therefore the transient part. The figure also shows the best
                 match against the data for three simple models of variation in the surface temperature.
                   The first model is equation (6.202) for instantaneous heating of the surface. The surface
                 temperature makes a step  T = 1.3 Cat t = 0 and we are seeking the time t that gives
                                               ◦
                 the best match against the data. By trying out t in the interval from 0 years to 500 years
                 with a step of 10 years we find that the optimal value is t 1 = 150 years.
                   The second model is the thermal transient (6.273) from periodic heating of the surface,
                 where the surface temperature is T surf (t) =  T cos(ωt) with  T = 1.3 C. The geotherm
                                                                          ◦
                 at t = 2πt 0 , where t 0 = 1/ω, is optimized with the result t 0 = 257 years. The full period
                 becomes 2πt 0 = 1614 years, which implies that there has been an increase in the surface
                                                      1
                 temperature from 0 to  T over a time span of πt 0 = 403 years.
                                                      2
                   The third model is the temperature solution (6.283) for a linear increase in the surface
                 temperature. The temperature increases from 0 C at time t = 0 until  T = 1.3 Cat
                                                        ◦
                                                                                    ◦
                 time t. The optimal time span is t = 340 years for this model.
                   These models suggest that the previous 400 years at the boresite has had a period with
                 a hotter climate than normal. This result is in accordance with a more elaborate surface
                 temperature estimate for the same borehole observations (Mareschal, 2005). The match
                 against the data for these models in terms of the root-mean-square deviation are close to
                 each other, as we can also see from Figure 6.37b. These models are simple to match against
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