Combining asynchronous data sets in regional body-wave tomography

Regional body-wave tomography is a very popular tomographic method consisting in inverting relative traveltime residuals of teleseismic body waves measured at regional networks. It is well known that the resulting inverse seismic model is relative to an unknown vertically varying reference model. If jointly inverting data obtained with networks in the vicinity of each other but operating at different times, the relative velocity anomalies in different areas of the model may have different reference levels, possibly introducing large-scale biases in the model that may compromise the interpretation. This is very unfortunate as we have numerous examples of asynchronous network deployments which would benefit from a joint analysis. We show here how a simple improvement in the formulation of the sensitivity kernels allows us to mitigate this problem. Using sensitivity kernels that take into account that data processing implies a zero mean residual for each event, the large-scale biases that otherwise arise in the inverse model using data from asynchronous station deployment are largely removed. We illustrate this first with a very simple 3-station example, and then compare the results obtained using the usual and the relative kernels in synthetic tests with more realistic station coverage, simulating data acquisition at two neighbouring asynchronous networks.

Automated summary

Regional body-wave tomography is a popular tomographic method, but asynchronous network deployments may introduce large-scale biases. By improving the formulation of sensitivity kernels, these biases can be mitigated.