Compared with other methods, the DTM creation time can be up to 5 times longer than the fast-moving average method (Maleika et al. The disadvantage of using the IDW method in processing large datasets is the computing time. The general premise of this method is that the attribute values of any given pair of points are related to each other, but their similarity is inversely related to the distance between the two locations (Lu and Wong 2008). The inverse distance weighting (IDW) method, a deterministic spatial interpolation model, is one of the most popular methods adopted by geoscientists and geographers and has been implemented in many GIS packages. The selection of the optimal interpolation method for unevenly distributed measurement data depends on a number of features that characterize the dataset: the homogeneity of data dispersion, number of points per area unit, population variance (degree of data changeability) and the type of surface reflected by the data. These methods use several different algorithms to determine the new values at grid nodes. 2004 Wlodarczyk-Sielicka and Stateczny 2016 Wlodarczyk-Sielicka et al. There are many methods of creating a grid based on measurement data, with the most commonly used interpolation methods being minimum curvature, natural neighbour, nearest neighbour, modified Shepard’s method, radial basis function, inverse distance to a power, triangulation with linear interpolation, moving average, kriging, polynomial regression and methods based on artificial intelligence (Chin-shung et al. In GIS systems, DTM of the seabed surface is often described using a grid structure (regular square network). The processing of such a large amount of data that is additionally irregularly dispersed in space requires the use of specially prepared numerical methods and appropriately selected data processing algorithms (Burrough and McDonnell 1998). These systems record the location and depth (spatial coordinates x, y, z) of many millions of points during one measurement session. multibeam echosounders-MBESs) can obtain a very large amount of information about the shape of the seabed in a relatively short time. Modern measuring systems with devices that enable the recording of observation results in a continuous manner (e.g. To create a seabed digital terrain model (DTM), it is first necessary to take marine measurements. Users of GIS systems, including those containing bathymetric data, demand reliable, accurate and up-to-date data and place high demands on the dynamics of data processing, visualization and analysis in real time. Research on seabeds is the most common and important task conducted by hydrographic institutions, and the created models are the basic information repository for further analysis and visualization. The experiments presented in the paper and the results obtained show the true potential of the IDW optimized method in the case of DTM estimation. Finally, the author proposed an optimization of the IDW method, which uses a new technique of choosing the nearest points during the interpolation process (named the growing radius). Several variants of IDW methods were analysed (dependent on the search radius, number of points in the interpolation, power of the interpolation and applied smoothing method). The goal of this optimization was to significantly accelerate the calculations, with a possible additional increase in the accuracy of the created model. In this study, the author optimized the IDW method used in the process of creating a DTM seabed based on measurement points from MBES. Between them is the IDW method, which gives satisfactory accuracy with a reasonable calculation time. In contrast, the MA method is the fastest, but the calculated models are less accurate. Kriging is often considered one of the best methods in interpolation of heterogeneous spatial data, but its use is burdened by a significantly long calculation time. There are many different methods for processing irregular measurement data into a grid-based DTM, and the most popular of these methods are inverse distance weighting (IDW), nearest neighbour (NN), moving average (MA) and kriging (K). This paper presents the optimization of the inverse distance weighting method (IDW) in the process of creating a digital terrain model (DTM) of the seabed based on bathymetric data collected using a multibeam echosounder (MBES).