Apparent and real bias in numerical transfer functions in palaeolimnology

We present an evaluation of the procedure by which model prediction bias is examined in palaeolimnological transfer function inference models. We argue that most of the prediction biases commonly reported in the literature are, in fact, fallacious, and are the artificial consequence of the inappropriate manner in which residuals are traditionally examined. We show that the extent of the specious model bias is entirely predictable from first principles and is essentially determined by the strength of the predictive model. We suggest that the analysis of residuals should always be examined as a function of the model's predictions and we discuss the implications of the old and new approaches.     

A constrained 2D gravity model of the Sebastián Vizcaíno Basin, Baja California Sur, Mexico

The subsurface geometry of the Sebastián Vizcaíno Basin is obtained from the 2D inversion of gravity data, constrained by a density-versus-depth relationship derived from an oil exploration deep hole. The basin accumulated a thick pile of marine sediments that evolved in the fore-arc region of the compressive margin prevalent along western North America during Mesozoic and Tertiary times. Our interpretation indicates that the sedimentary infill in the Sebastián Vizcaíno Basin reaches a maximum thickness of about 4 km at the centre of a relatively symmetric basin. At the location of the Suaro-1 hole, the depth to the basement derived from this work agrees with the drilled interface between calcareous and volcaniclastic members of the Alisitos Formation. A sensitivity analysis strongly suggests that the assumed density function leads to a nearly unique solution of the inverse problem.