Basics of Paleomagnetism and Rock Magnetism

Line fitting of y = a + bx: Special solution of York (1966)

An observation and an adjusted point on the line. For analyzing an Arai plot in paleointensity experiments, Coe et al. (1978) proposed to use a special solution of York (1966). In York's theory the quantity to be minimized is given by, \[ t = \sum_{i=1}^n \{w(x_i)(x_i - p_i)^2 + w(y_i)(y_i - q_i)^2\}, \] where \(w(x_i)\) and \(w(y_i)\) are the weights of the observations. The theory develops very complicated general equations for \(b\) which are not described here. Among easier special solutions, when \[ \frac{w(x_i)}{w(y_i)} = c \quad (\mathsf{constant}) \] the equation is given by, \[ \left( \sum_{i=1}^n w(x_i) u_i v_i \right)b^2 - \left( \sum_{i=1}^n w(x_i) v_i^2 - c\sum_{i=1}^n w(x_i) u_i^2 \right)b - c\sum_{i=1}^n w(x_i) u_i v_i = 0, \] where \[ u_i = x_i - \bar x, \quad v_i = y_i - \bar y. \] There are three cases for this special equation, and Coe et al. (1978) proposed to use the last case; \[ w(x_i) = \frac{1}{\sigma_x^2} = \frac{n-1}{\sum_{i=1}^n u_i^2}, \quad w(y_i) = \frac{1}{\sigma_y^2} = \frac{n-1}{\sum_{i=1}^n v_i^2}. \] Then \(b\) is given by the following simple equation, \begin{equation} b = \sqrt{ \frac{\sum_{i=1}^n v_i^2}{\sum_{i=1}^n u_i^2} } = \frac{\sigma_y}{\sigma_x}. \label{eq01} \end{equation} York (1966) also gave an estimate of \(\sigma_b\) as, \begin{equation} \sigma_b^2 = \frac{1}{n-2}\frac{2\sum_{i=1}^n v_i^2 - 2b\sum_{i=1}^n u_i v_i}{\sum_{i=1}^n u_i^2}, \label{eq02} \end{equation} which is called a standard error of \(b\).

Variance of data in the Arai plot.

The above \(\sigma_x^2\) and \(\sigma_y^2\) used for the weights \(w(x_i)\) and \(w(y_i)\) do not necessarily reflect the errors of \(x_i\) and \(y_i\) because they are variances around the means. Nevertheless, they conform with the general idea of the errors in the Arai plot; the absolute value of measurements error is proportional to the remanence intensity. In this case, as known from the figure, the errors of \(x_i\) and \(y_i\) can be expressed as, \[ \sigma_{x_i}^2 = (F_L \sigma)^2, \quad \sigma_{y_i}^2 = (F_E \sigma)^2, \] where \(F_L\) and \(F_E\) are strength of the laboratory and paleo fields, respectively, and \(\sigma\) is a constant which reflects a general error of the measurements. Hence, the above \(b\) and \(\sigma_b\) given by \eqref{eq01} and \eqref{eq02} are considered to be reasonable.

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