Applications of the Hilbert-Huang transform for microtremor data analysis enhancement

In this paper we discuss the use of the Hilbert-Huang transform (HHT) to enhance the time-frequency analysis of microtremor measurements. HHT is a powerful algorithm that combines the process of empirical mode decomposition (EMD) and the Hilbert transform to compose the Hilbert- Huang spectrum that contains the time-frequency-energy information of the recorded signals. HHT is an adaptive algorithm and does not require the signals to be linear or stationary. HHT is advantageous for analyzing microtremor data, since observed microtremors are commonly contaminated by nonstationary transient noises close to the recording instruments. This is especially true when microtremors are measured in an urban environment. In our data processing HHT was used to (1) eliminate the unwanted short-duration transient constituents from microtremor data and use only the coherent portion of the data to carry out the widely used horizontal to vertical spectral ratio (H/V) method; (2) identify and eliminate the continuous industrial noise in certain frequency band; and (3) enhance the H/V analysis by using the Hilbert-Huang spectrum (HHS). The efficacy of this proposed approach is demonstrated by the examples of applying it to microtremor data acquired in the metropolitan Beijing area.