### Abstract

Automatic differentiation (AD) is the technique whereby output variables of a computer code evaluating any complicated function (e.g. the solution to a differential equation) can be differentiated with respect to the input variables. Often AD tools take the form of source to source translators and produce computer code without the need for deriving and hand coding of explicit mathematical formulae by the user. The power of AD lies in the fact that it combines the generality of finite difference techniques and the accuracy and efficiency of analytical derivatives, while at the same time eliminating ‘human’ coding errors. It also provides the possibility of accurate, efficient derivative calculation from complex ‘forward’ codes where no analytical derivatives are possible and finite difference techniques are too cumbersome. AD is already having a major impact in areas such as optimization, meteorology and oceanography. Similarly it has considerable potential for use in non-linear inverse problems in geophysics where linearization is desirable, or for sensitivity analysis of large numerical simulation codes, for example, wave propagation and geodynamic modelling. At present, however, AD tools appear to be little used in the geosciences. Here we report on experiments using a state of the art AD tool to perform source to source code translation in a range of geoscience problems. These include calculating derivatives for Gibbs free energy minimization, seismic receiver function inversion, and seismic ray tracing. Issues of accuracy and efficiency are discussed.

Original language | English |
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Pages (from-to) | 1-8 |

Number of pages | 8 |

Journal | Geophysical Journal International |

Volume | 170 |

Issue number | 1 |

DOIs | |

Publication status | Published - 2007 |

### Keywords

- derivatives
- inverse problems
- sensitivity kernel

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## Cite this

Sambridge, M., Rickwood, P., Rawlinson, N., & Sommacal, S. (2007). Automatic differentiation in geophysical inverse problems.

*Geophysical Journal International*,*170*(1), 1-8. https://doi.org/10.1111/j.1365-246X.2007.03400.x