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We present a technique for developing seismic models using multiple data sets using a probabilistic inverse technique. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample models from a prior distribution and test them against multiple data types to generate a posterior distribution. This approach has several advantages over deterministic models, notably the reconciliation of different data types that constrain the model, the proper handling of uncertainties, and the ability to include prior information. We also benefit from the advantage of forward modeling rather than inverting the data. We demonstrate this method by using it to determine the 3‐D crust and upper mantle structure for the Yellow Sea and Korean Peninsula (YSKP) region. The model is parameterized as a series of layers in a regular lat‐lon grid. We start with an a priori model and use surface wave dispersion measurements, body wave travel times, teleseismic receiver functions, and gravity data to drive the model. The end result is a...

Reconciling data using Markov chain Monte Carlo: An application to the Yellow Sea–Korean Peninsula region