Projects

Different data types have different sensitivities to different anomalies. For example, ambient noise surface wave data has good lateral resolution in shallow depth, while teleseismic waveform and receiver function data have good vertical resolution. By combining multiple data types within the framework of full-waveform inversion (adjoint tomography) and jointly optimizing the velocity model, we can obtain a more reliable and accurate velocity structure.

Receiver function (RF) is widely used for imaging crustal and uppermost mantle velocity structure based on horizontal layered assumption. However this traditional technique has limitations on resolving non-layered anomalies, such as the dipping Moho, the subduction zone, and plumes. Thus, we develop a innovative technique to invert RFs based on adjoint waveform tomography method for high-resolution seismic array imaging.

Seispy is a graphical interface Python module for receiver function (RF) calculation and post-processing in seismological research. Automated workflows of RF calculations facilitate processing large volumes of different types of seismic data.