Global optimization methods in geophysical inversion Making inferences about systems in the Earths subsurface from remotely sensed sparse measurements is a challenging task. Geophysical inversion aims to find models which explain geophysical observations a model based inversion method attempts to infer model parameters by iteratively fitting observations with theoretical predictions from trial models. Global optimization often enables the solution of non linear models employing a global search approach to find the absolute minimum of an objective function so that predicted data best fits the observations. This new edition provides an up to date overview of the most popular global optimization methods including a detailed description of the theoretical development underlying each method and a thorough explanation of the design implementation and limitations of algorithms. A new chapter provides details of recently developed methods such as the neighborhood algorithm and particle swarm optimization. An expanded chapter on uncertainty estimation includes a succinct description on how to use optimization methods for model space exploration to characterize uncertainty and now discusses other new methods such as hybrid Monte Carlo and multi chain MCMC methods. Other chapters include new examples of applications from uncertainty in climate modeling to whole earth studies. Several different examples of geophysical inversion including joint inversion of disparate geophysical datasets are provided to help readers design algorithms for their own applications. This is an authoritative and valuable text for researchers and graduate students in geophysics inverse theory and exploration geoscience and an important resource for professionals working in engineering and petroleum exploration.