## 1 Introduction  
Thailand is not a highly earthquake-prone country compared to other countries. Relatively higher frequency and magnitude seismic activities are constrained to the northern and western parts of the country [1]. This research covered the study area of Chiang Rai, Chiang Mai, and some parts of Lumbun.  
There are many active faults located in these provinces (see Figure 1). In 2014, a moderate seismic eruption occurred in the Chiang Rai Province, however, the damage was severe due to inefficient seismic disaster management [2]. Past disaster management was not as effective as it should have been due to fewer experience of seismic activity, which has resulted in insufficient data, understanding, and expertise for seismic hazard planning in terms of mitigation and response. However, [PERSON] (2017) reported statistical information about disasters in Thailand, including fatalities and economic losses. The results of this study showed that financial losses due to damage caused by earthquakes are higher than for other hazards (e.g., flooding, storms, and heavy rainfall) that occur with greater frequency [3].  
Historically, seismic events did not cause noticeable damage, and thus, seismic disaster management awareness was overlooked. The moderate seismic activity that occurred in 2014 provoked strong local recognition of seismic disaster management. However, seismic analysis data and ground-truth data that provide information on the characteristics of each soil layer are not available in the area. Therefore, in this research, the soil characteristics were explored with field work and using time-averaged shear wave velocity data at 30 m (V\({}_{\rm S30}\)) from past research for seismic amplification analyses.  
V\({}_{\rm S30}\) is widely applied to understand soil characteristics and there are several methods for performing V\({}_{\rm S30}\) field observations. In this study, microtremor measurements were obtained using the microtremor array measurement with spatial autocorrelation (SPAC) method [4; 5; 6; 7; 8]. This technique is popular due to its convenience. The V\({}_{\rm S30}\) results in the field survey represent the subsoil under the sensor. The values were then transformed to seismic microzoning with the geomorphological base map. The relationship between V\({}_{\rm S30}\) and the amplification factor were referenced from the United States National Earthquake Hazards Reduction Program (NEAR) provisions. The geomorphological units were applied to create the polygon feature from the point feature of V\({}_{\rm S30}\), representative of the soil characteristics. A geomorphological map of the study area was not available. Therefore, the geomorphology classification proposed by [PERSON] and [PERSON] (2007) [9] was followed in this study, with different samples in the areas of interest (AOI) and resolutions to observe the influences. The classification was created with the automated decision tree method with a digital elevation model (DEM) as input. Additionally, the classification methodology of pixel-based classification (PBC) and object-based classification (OBC) were explored. Both PBC and OBC are methodologies for putting pixels of images into categories. OBC categorizes pixels based on the spatial relationship with the surrounding pixels, while PBC categorizes pixels based on information from a single pixel. The differences between PBC and OBC outputs are also compared with the geological map in the Discussion section; this geological map is currently the map most commonly used for seismic analysis and other applications in Thailand. Additionally, the sites severely damaged during the 2014 earthquake were overlaid on the seismic hazard map that was developed, for visual evaluation. In conducting this study, no specific grants were received from funding agencies in the public, commercial, or nonprofit sectors.