Academic journal article The Indonesian Journal of Geography

The Effect of Baseline Component Correlation on the Design of GNSS Network Configuration for Sermo Reservoir Deformation Monitoring

Academic journal article The Indonesian Journal of Geography

The Effect of Baseline Component Correlation on the Design of GNSS Network Configuration for Sermo Reservoir Deformation Monitoring

Article excerpt

(ProQuest: ... denotes formulae omitted.)

1. Introduction

Sermo reservoir is located in the western part of Yogyakarta, Indonesia. It was built by damming Ngrancah river and officially operated in 1997. It can hold 2S million cubic meters of water and serves a vital role as a water reservoir from which water is then distributed by the Water Utilities (PDAM) serving the needs for clean water, irrigation, and flood prevention.

The condition of the geological structure in the Sermo reservoir and surrounding have an interesting phenomenon. Overlaying geological map and Landsat imagery show that there are reverse and thrust faults which cross the reservoir (Figure 1). This condition is confirmed by (Widagdo, Pramumijoyo, Harijoko, & Setiawan, 2016) in their research about the geological structure of rock distribution in the area of Kulonprogo. They found that the secondary structure which controls the rock distribution in Kulonprogo mountain is in the form of Northwest-Southeast normal fault, SouthwestNortheast reverse fault, and North-Northwest lateral fault. The similar description is also found in the main report of Sermo Reservoir Project Details Design (Departemen Pekerjaan Umum, 198S).

The fault, henceforth referred to as the Sermo fault, potentially affects the Sermo Dam deformation. In the last three years, deformation monitoring has been carried out by conducting GNSS campaigns. However, those campaigns were not well designed. Observations were carried out simultaneously at IS monitoring stations distributed around the fault. With such a design, it took many instruments and spent much money. For the next GNSS campaign, it should be designed so that the optimal network configuration is obtained and the cost can be reduced.

In general, network optimization design can be classified into several orders, namely zero, first, second, and third orders (Halicioglu & Ozener, 2008; Kuang, 1996; Mehrabi & Voosoghi, 2014). A geodetic network needs to be designed to meet the criteria of accuracy, reliability, and low cost. However, a deformation monitoring network must meet one more criterion, that is, sensitivity to the occurring deformation (Benzao & Shaorong, 199S; Even-Tzur, 2002). Several study has been done to design the optimum geodetic and deformation monitoring network, wherein accuracy and reliability have been the most used criteria. Mehrabi and Voosoghi (2014) used the precision criteria with analytical methods and a quadratic programming algorithm to obtain the optimal GNSS baseline weight. By using this method, the number of baselines observed could be decreased by 36% so that the cost of measurement can be reduced. The use of three criteria simultaneously were studied by Alizadeh-Khameneh et al. (2015). Those three criteria were precision, reliability, and cost. The optimization design was performed through analytical methods using the single-objective, bi-objective, and multiobjective optimization models. Even-Tzur (2002) used the sensitivity criterion in developing a GPS network optimization design for deformation monitoring. Sensitivity analysis was carried out by including deformation data and geological parameters, such as deformation velocity and models. Benzao and Shaorong (1995) included the direction of the deformation movement as one of the parameters in optimization design. However, in such studies, it is quite common to optimize a network without considering correlations between baselines. Although the effect of ignoring this correlation was not significant, it should be considered for deformation monitoring purpose that requires a high level of accuracy. Furthermore, several studies have taken into account the correlation in the design of GNSS network optimization. Alizadeh-Khameneh et al. (2017) found that by considering it, a more efficient and precise network can be obtained. Craymer and Beck (1992) recommended GNSS processing method based on measurement sessions because through such, the correlations between baselines can be taken into account. …

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