{"id":2962716,"date":"2026-04-30T21:30:41","date_gmt":"2026-05-01T04:30:41","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2962716"},"modified":"2026-04-30T21:30:41","modified_gmt":"2026-05-01T04:30:41","slug":"interpolate-borehole-data-to-create-aquifer-top-and-base-surfaces","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/water\/interpolate-borehole-data-to-create-aquifer-top-and-base-surfaces","title":{"rendered":"Interpolate borehole data to create aquifer top and base surfaces"},"author":9692,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23771,22771,23051],"tags":[757981,781062,34131,781077,39061],"industry":[],"product":[36561],"class_list":["post-2962716","blog","type-blog","status-publish","format-standard","hentry","category-3d-gis","category-natural-resources","category-water","tag-aquifers","tag-boreholes","tag-interpolation","tag-radial-basis-functions","tag-subsurface","product-arcgis-pro"],"acf":{"authors":[{"ID":9692,"user_firstname":"Jie","user_lastname":"Chang","nickname":"Jie Chang","user_nicename":"jchang","display_name":"Jie Chang","user_email":"JChang@esri.com","user_url":"","user_registered":"2019-06-26 21:13:09","user_description":"Jie Chang is a Product Engineer on the 3D Analysis team at Esri. He specializes in 3D, lidar, imagery, spatial analysis, deep learning, and web GIS. He received his Ph.D. in Geospatial Information Sciences from the University of Texas at Dallas in 2011.","user_avatar":""}],"related_articles":"","show_article_image":false,"card_image":false,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/04\/aquifer-banner.png","short_description":"Turn borehole data into aquifer surfaces using RBF interpolation in ArcGIS Pro with geologic constraints.","flexible_content":[{"acf_fc_layout":"content","content":"

Lillian Wang from the Delaware Geological Survey (DGS) and I developed an ArcGIS Tutorials lesson, Create a 3D subsurface visualization of aquifers<\/a>. The lesson walks through building 3D aquifer models from aquifer top and base elevation surfaces, sharing them to ArcGIS Online, and exploring them in a web app.<\/p>\n

To keep the lesson focused, we begin with pregenerated aquifer top and base surfaces. These surfaces are interpolated from borehole data using the Radial Basis Functions<\/a> (RBF) method in ArcGIS Pro. We use the Completely Regularized Spline kernel function, a spline-based approach similar to the Spline tool in Spatial Analyst and 3D Analyst. Because method selection and parameter tuning depend on geological conditions, data density, and spatial distribution\u2014and often require expert judgment\u2014the interpolation is not included in the tutorial.<\/p>\n

This blog article fills that gap. It explains how to perform aquifer interpolation in ArcGIS Pro using borehole data and the RBF method, including why RBF was selected, how parameters were set, and how geological knowledge guided the process.<\/p>\n

Convert borehole records to elevation points<\/strong><\/h3>\n

Geologists first interpreted each borehole record to identify the top and base of individual aquifers using geophysical logs and lithologic descriptions. These depth-to-contact values are then converted to elevation by subtracting depth from a high-resolution digital elevation model (DEM) referenced to NAVD88. The result is a set of point datasets representing aquifer top and base elevations.<\/p>\n

Why we chose RBF<\/strong><\/h3>\n

ArcGIS Pro provides several interpolation methods, including deterministic approaches such as RBF and geostatistical methods such as kriging and empirical bayesian kriging (EBK). The appropriate method depends on the modeling objective, data distribution, and geological context.<\/p>\n

In this case, the goal is to model aquifer surfaces that reflect marine shelf deposits\u2014units that are regionally extensive and gradually dipping.<\/p>\n

Kriging-based methods explicitly model spatial autocorrelation and provide prediction uncertainty. They are well suited for complex variability or when quantifying uncertainty is critical. However, they require careful semivariogram modeling and assumptions about stationarity.<\/p>\n

RBF, in contrast, provides a more direct and flexible approach. It produces smooth surfaces influenced by nearby data points. Therefore, for stratigraphically continuous aquifers with predictable regional dip, this approach performs well. It preserves regional smoothness while maintaining local control through neighborhood and anisotropy settings.<\/p>\n

We evaluated universal kriging and EBK alongside RBF. While all methods captured the regional trend, kriging-based surfaces depend strongly on the variogram, search neighborhood, and anisotropy settings. In this case, we selected RBF to produce smooth, laterally continuous aquifer surfaces consistent with the underlying geology.<\/p>\n

Overall, the final decision combined cross-validation results with geological plausibility. Interpolation generates a mathematical surface, but geology determines whether that surface is meaningful.<\/p>\n

Interpolate surfaces with RBF<\/strong><\/h3>\n

We use the Cheswold aquifer as an example to demonstrate how to interpolate its top surface from elevation points. The Geostatistical Wizard is used here for its interactive parameter tuning, although the RBF geoprocessing tool can produce equivalent results.<\/p>\n

You can use the same workflow by completing the following steps:<\/p>\n

Open the Geostatistical Wizard and select Radial Basis Functions<\/strong>.<\/p>\n