## 1 Introduction  
The recoverable resources of shale gas in China are about 25.1 \(\times\) 10\({}^{12}\) m\({}^{3}\)[1] with the technological innovations of directional drilling and hydraulic fracturing. The Paleozoic marine shale in southern China provides the largest shale gas potential in China [2; 3]. At present, almost all commercial shale gas production in China comes from the Paleozoic marine shale in Sichuan Basin, southern China [4]. Previous research has also predominantly focused on the Sichuan Basin [5; 6; 7; 8]. To meet the growing demand for shale gas, it is necessary to expand the exploration scope beyond the Sichuan Basin and find new shale gas producing areas. The western Hunan-Hubei (WHH) fold belt, adjacent to the Sichuan Basin, contains rich black marine shale. However, the strong heterogeneous reservoir, complex geological structure, and low exploration degree in the WHH fold belt greatly increase the investment risks of shale gas drilling. Seismic attribute technology is a reliable method for describing the spatial distribution of hydrocarbon reservoirs, especially in the case with few drilling data [9; 10]. It can help design the direction and trajectory of directional drilling, reduce the exploration risk, and optimize the production. Seismic attributes commonly used for hydrocarbon detection include two categories. One is the seismic attribute based on rock physics theory, such as amplitude versus offset (AVO) attribute estimated from the pre-stack data through AVO inversion [11]. Nevertheless, in areas lacking enough well controls, AVO attribute may not be adequate to assist hydrocarbon identification [12]. The other is the seismic attribute based on seismic attenuation theory, such as low-frequency shadow [13] and high-frequency attenuation [14] calculated from post-stack data. Compared with the pre-stack attribute, the post-stack one has the advantages of no need for strict well control, low computational cost, and high signal-to-noise ratio [15]. However, the post-stack attributes mentioned above can directly reflect the magnitude of seismic attenuation, but not always directly reflect the property of hydrocarbon reservoir. It is necessary to find a universal seismic attribute indicating hydrocarbon directly. In recent years, some post-stack fluid mobility attributes have been applied to hydrocarbon identification in deep-water sandstone reservoirs [7; 10; 15; 16]. Fluid mobility is the direct reflection of the rock permeability and fluid viscosity of hydrocarbon reservoirs. [PERSON] et al. [17] and [PERSON] and [PERSON] [18] approximated the low-frequency seismic reflection coefficient as a function of fluid mobility in fractured reservoir. Therefore, we extract fluid mobility from low-frequency seismic reflection data to directly indicate gas in (fractured) shale reservoir. Extracting information from low-frequency seismic data is inseparable from spectral decomposition technology [19], which is a powerful tool for obtaining time-frequency (TF) spectrum of seismic data. The bilinear Wigner-Ville distribution (WVD) can achieve a higher-resolution TF localization than the linear spectral decomposition techniques [20], but suffers from the notorious cross-term interferences. The integration of WVD and MP algorithm can produce a stable high-solution TF spectrum without cross-term interferences [10; 21]. MP is a greedy algorithm for compressive sensing [22]. However, the traditional MP algorithm is difficult to determine the precise TF locations of seismic signal due to the interference effect between different seismic frequency contents [23]. More specifically, the seismic low-frequency contents of interest may be suppressed by the seismic high-frequency contents during the process of MP algorithm, which may result in an average effect in the final TF spectrum and thereby probably reduces the accuracy of hydrocarbon detection. EEMD is a nonlinear technique for adaptively decomposing a seismic signal into a finite number of narrow-band signals [24].