Weighing Geophysical Data With Trans-Dimensional Algorithms: An Earthquake Location Case Study

In geophysical inverse problems, the distribution of physical properties in an Earth model is inferred from a set of measured data. A necessary step is to select data that are best suited to the problem at hand. This step is performed ahead of solving the inverse problem, generally on the basis of expert knowledge. However, expert-opinion can introduce bias based on pre-conceptions. Here we apply a trans-dimensional algorithm to automatically weigh data on the basis of how consistent they are with the fundamental hypotheses made to solve the inverse problem. We demonstrate this approach by inverting arrival times for the location of a seismic source in an elastic half-space, assuming a point-source and uniform weights in concentric shells. The key advantage is that the data do no longer need to be selected by an expert, but they are assigned varying weights during the inversion procedure. In the Big data era, automated approaches to data evaluation are needed for two main reasons: to be able to process a large amount of data in a limited time, and to avoid bias introduced by data analysists. In this study we present a novel approach to data analysis, where the data themselves measure their consistency with our hypotheses. The approach is applied to earthquake location in mines, where millions of seismic events occur every year, and automatic processing of seismic data is mandatory. We demonstrate that our approach outperforms standard ones when almost nothing is known about the data and their measurement errors. We develop a novel approach for automatic weighting of data in geophysical inverse problems, based on a trans-dimensional algorithmWe apply the novel approach to seismic event location in mines, obtaining consistent results compared to a more standard methodOur approach outperforms standard seismic monitoring approaches, when limited information are available on local seismic structure