## 2 Data and Methods  
### GNSS and Seismicity Data  
We utilized GNSS stations situated in the eastern region of Java. These eight continuous stations are part of the Indonesian Continuous Operating Reference Station (Ina---CORS) network [21], along with 35 campaign stations provided by the Geospatial Information Agency of Indonesia. We comprehensively analyzed daily solutions from 2016 to 2023, utilizing the International Terrestrial Reference Frame 2014 (ITRF2014) as a reference framework [22]. This study aimed to enhance our understanding of the underlying active tectonics during this period. The GNSS data for this study were processed using the GAMIT/GLOBK version 10.7 software suite, a robust tool for high---precision geodetic analysis [23]. The processing incorporated data from eight International GNSS Service (IGS) stations and all observation sites. The IGS stations employed in the processing, situated in the surrounding region of Indonesia, consist of COCO, CCJ2, CUSV, DARW, DGAR, IISC, PIMO, and TOW2. Identifying and handling errors in GNSS data using TEQC is a crucial step in ensuring the reliability of data processed with GAMIT/GLOBK. TEQC is particularly effective for evaluating data quality through residual analysis, multipath metrics, and ionospheric delay indicators. To detect outliers, TEQC generates residuals that highlight observations that deviate significantly from expected patterns, enabling users to identify and exclude problematic data points [24]. The data workflow entailed an initial set of GAMIT computations to estimate the stations' positions, orbits, and atmospheric parameters, which were then refined in GLOBK. GLOBK combines the results of multiple sessions by applying temporal constraints and statistical adjustments to enhance the accuracy of its positioning. This methodology provided the reliable and precise geodetic solutions crucial for analyzing crustal deformations and tectonic dynamics in the region [25]. In GNSS data processing with GAMIT, a priori coordinates derived from precise point positioning (PPP) were utilized as the initial approximations. To obtain the most accurate results, we applied a series of corrections, including those related to ocean loading (FES2014) and factors concerning the pole, solid earth, and tidal forces (IERS2010) [26]. The selection of GNSS stations was made to achieve a precise estimation of the strain rate in the Eastern Java region. This careful selection process involved considering various factors, such as station distribution, historical data availability, and the geological characteristics of the area. By utilizing a network of strategically placed GNSS stations, we aimed to enhance our understanding of tectonic activity and monitor the surface deformations that could impact the region. In this study, we transformed the GNSS daily solutions into the Sunda block reference frame. The transformation was achieved by utilizing the Euler pole angular velocity (\(\varpi\)) of 0.337\({}^{\circ}\)/Myear and its position (45.63\({}^{\circ}\) N, \(-\)88.71\({}^{\circ}\) E), which are tied to ITRF2014 [27]. Thisapproach was adopted to represent local deformation [28]. The time---series plot depicting both the campaign and continuous GNSS observations offers a clear visualization of the trends in temporal displacement. The result is illustrated in detail in Figure 3, highlighting the deformation dynamics over time. It facilitates a detailed analysis of patterns and tectonic deformation. Seismicity data were obtained from the USGS Earth Explorer to identify significant earthquakes with magnitudes of 6.0 or greater, which were then used to visualize focal mechanisms (Figure 1). Furthermore, earthquake data with magnitudes below 6.0 were also obtained from the USGS. The focal mechanisms for the significant earthquakes were derived from the Global Centroid Moment Tensor (GCMT) catalog [14], which provides essential information on the source characteristics of these seismic events.