## 1 Introduction
### Literary Reviews  
[PERSON] et al. (2016) [3] first derived a source model through joint inversion. The source model was mainly composed of two asperities, but the main rupture developed in a down-dip direction and then proceeded in the northwest direction. By combining P and depth-phase sP waves recorded at dense arrays through back projection, [PERSON] et al. (2017) [4] revealed that the rupture process was unilateral (northwestward), and the source radiation was modeled as a single asperity with a length of 17 km. By considering ground motion displacement, [PERSON] et al. (2017) [5] revealed that the combined effects of crustal structure, rupture directivity, and source slip can amplify seismic waveforms to cause the serious damages in Tainan. By employing a densely distributed seismic network in a seismic array, [PERSON] et al. (2018) [1] derived earthquake rupture processes through finite fault inversion. They indicated that the Meinong earthquake was composed of double source events and that the mainshock source, directivity, and site effects were the main factors causing severe damage to Tainan City. [PERSON] et al. (2018) [2] derived a source model through the joint source inversion of waveforms and geodetic datasets, revealing that rupture directivity may generate large long-period velocity pulses, which were the major factors in the substantial damage to Tainan City. [PERSON] et al. (2019) [6] used the empirical Green function from a small aftershock (M\({}_{\rm L}\) 4.6) to estimate that the strong motion generation area (SMGA) of the Meinong earthquake was approximately 7.2 \(\times\) 7.2 km\({}^{2}\). They revealed that both the size of SMGA and rise time of the main event followed a self-similar empirical relationship formula derived by [PERSON] et al. (2003) [7]. By analyzing the aftershock sequence of the Meinong earthquake, [PERSON] et al. (2020) [8] provided evidence for fluid movement in southwestern Taiwan, concluding that fluids may be a important factor causing deep crustal earthquakes and affecting earthquake hazards. Through the joint inversion of InSAR, Global Positioning System (GPS; 1 Hz), and strong motion data, [PERSON] et al. (2021) [9] inverted the source model of the Meinong earthquake and suggested that this model has a simpler asperity than other models.  
On the basis of different methods and recorded data, there are various source models derived to demonstrate some characteristics of the rupturing process when earthquakes are occurring. However, the derived source models had some differences owing to the inversion methods and data used. To detect and understand the source process of an earthquake, [PERSON] et al. (2013) [10] developed the hybrid homomorphic deconvolution (HHD) method to invert the source model of the 1999 Chi-Chi earthquake (Taiwan), obtaining excellent results. They stated that their HHD method can invert and efficiently capture the main characteristics of an earthquake rupture. By employing the same method, [PERSON] and [PERSON] (2013) [11] inverted the source model of the 2011 Tohoku-Oki earthquake (Japan; M\({}_{\rm w}\) 9.0) whose epicentral location could be estimated in advance [12] by means of natural time analysis of Japanese seismicity [13,14] to obtain the source characteristics of that event. Using the aforementioned method, [PERSON] and [PERSON] (2016) [15] derived the source models of two moderate earthquakes (M\({}_{\rm L}\) 6.2 and 6.0) in 2013 in central Taiwan to calculate the Coulomb stress changes distributed around central Taiwan and predict potential earthquake locations. The aim of this study was to use HHD to invert a source model of the Meinong earthquake and employ the inverted model to calculate surface deformation and Coulomb stress changes at different depths and estimate seismic spectral intensities Sla and Slv. Finally, we compare our results with other results in the literature to determine the validity of our inverted model as an alternative approach to source inversion.