DWR's Basin Characterization Program - BCTools

DWR has a long history of studying and characterizing California’s groundwater aquifers as a part of [California’s Groundwater (Bulletin 118)](https://water.ca.gov/Programs/Groundwater-Management/Bulletin-118 "California’s Groundwater (Bulletin 118)"). [California's Groundwater Basin Characterization Program](https://water.ca.gov/Programs/Groundwater-Management/Bulletin-118/Basin-Characterization "California's Groundwater Basin Characterization Program") provides updated data and information to help local communities better understand their aquifer systems and support local and statewide groundwater management. Datasets, maps, models, and workflows developed under the program are available on the [Basin Characterization Program Open Data Portal Page](https://data.cnra.ca.gov/dataset/dwr-basin-characterization "Basin Characterization Program Open Data Portal Page"). Under the Basin Characterization Program, new and existing data (including airborne electromagnetic (AEM), lithology logs, and geophysical logs) are integrated to create continuous maps and three‑dimensional models. These tools help groundwater managers understand how groundwater is stored and moves through the subsurface. The program develops state‑stewarded maps and models by integrating geologic, geophysical, and hydrogeologic datasets. This page provides access to Basin Characterization Tools (BCTools)—a collection of open‑source software tools used to develop hydrostratigraphic models, texture models, and aquifer parameters. The suite includes Data2HSM, Texture2Par, and an AEM Data Preprocessor. These tools can incorporate both lithology log data and AEM data into the model‑building process. All tools, user guides, and training materials are publicly available for local groundwater managers. Tools and documentation will be updated as improvements are made. #[BCTools](https://data.cnra.ca.gov/dataset/dwr-basin-characterization-bctools/resource/d9d4792a-6746-4ea0-8e8b-694bdb545fc7 "BCTools ") BCTools is a toolset that supports construction of hydrostratigraphic models, estimation of sediment textures, and assignment of aquifer parameters for groundwater flow models. BCTools includes three main components: + Data2HSM – hydrostratigraphic modeling tools + Texture2Par – texture and aquifer parameter modeling tools + AEM Data Preprocessor – formats AEM data for use in the two tool suites BCTools integrates these components into a single interface while allowing each to be run independently. BCTools runs on Windows® operating systems. Refer to the [BCTools Framework](https://data.cnra.ca.gov/dataset/ca0e3aae-a13c-444f-88c0-ad5ed25fec28/resource/d3e6c30e-0ca3-4b5c-b975-7d67f70acac1/download/bctools_framework.png "BCTools Framework") for an overview of the key components. ##[Data2HSM](https://data.cnra.ca.gov/dataset/dwr-basin-characterization-bctools/resource/73bb5449-b7a8-43f9-89c5-c7b69127594b "Data2HSM") Data2HSM is a flexible suite of machine‑learning tools for constructing hydrostratigraphic models (HSMs). It enables users to combine DWR’s statewide AEM datasets with other subsurface data to develop basin hydrostratigraphy. Possible outputs include: + Groundwater model layering prototypes + Hydrogeologic conceptual models + 3D lithologic models + Surfaces such as aquifer/aquitard bases, geologic formations, the water table, base of freshwater, or top of bedrock Data2HSM consists of one interface with three main tools: + Gaussian Mixture Model + Smart Interpretation + GeoPDNN ###Gaussian Mixture Model (GMM) GMM converts AEM data into 2D and 3D lithologic or stratigraphic models. Users can also include existing stratigraphic or texture models to guide the tool. GMM uses unsupervised machine learning to assign data points to user‑defined clusters that represent either lithologic groups (e.g., sand, clay, gravel) or stratigraphic units. When configured for stratigraphy, the tool uses prior geologic knowledge and a coordinate transformation to help maintain geologic consistency. ###Smart Interpretation (SI) Smart Interpretation helps users rapidly delineate hydrogeologic surfaces such as aquifer/aquitard bases, the water table, the base of freshwater, or the top of bedrock. Users provide a small set of interpretations—either from past work or from other datasets—and SI uses polynomial regression informed by AEM data to complete the surface. An interactive 3D viewer allows additional interpretations using AEM sections, boring logs, and geologic cross‑sections. Multiple surfaces can be interpreted in one session. ###Geological Pseudolabel Deep Neural Network (GeoPDNN) GeoPDNN is a deep‑learning tool that generates continuous 3D geological models from interval‑based datasets such as boring logs or AEM soundings. It takes in location coordinates and interval data and outputs a 3D stratigraphic model and surfaces marking the base of each unit. ##[Texture2Par](https://data.cnra.ca.gov/dataset/dwr-basin-characterization-bctools/resource/40b4f199-39d8-408b-9105-edc3b386f559 "Texture2Par") Texture2Par generates 3D texture models and aquifer parameter datasets for groundwater flow modeling. It consists of two tools—Texture Model (TM) and Aquifer Flow Parameters (AFP)—now combined into a single Texture2Par interface. Written in Fortran, Texture2Par can be used standalone or within a larger automated workflow such as groundwater model calibration. ###Texture Model (TM) Texture2Par:TM uses ordinary, block, or indicator kriging and co‑kriging to convert lithology logs, and geophysical data interpreted for texture (from AEM or geophysical logs) into 3D texture models. It supports anisotropic and isotropic variograms and cross‑variograms. ###Aquifer Flow Parameters (AFP) Texture2Par:AFP uses empirical relationships between texture and aquifer properties to estimate horizontal and vertical hydraulic conductivity, specific yield, and specific storage throughout a model domain. AFP can use co‑kriging to integrate texture data from well logs, boring logs, and AEM. The tool also supports a pilot‑point approach to incorporate spatially distributed aquifer parameter measurements, such as pumping test results. AFP can directly output parameter files for IWFM and MODFLOW. ##AEM Data Preprocessor The AEM Data Preprocessor prepares AEM datasets from DWR’s statewide surveys for use in Texture2Par:TM and Data2HSM’s GMM and SI tools. It takes resistivity models and coarse fraction models as inputs and outputs a single formatted file suitable for use in the modeling tools. The Preprocessor must be run before using AEM data in Data2HSM or Texture2Par.

DWR has a long history of studying and characterizing California’s groundwater aquifers as a part of [California’s Groundwater (Bulletin 118)](https://water.ca.gov/Programs/Groundwater-Management/Bulletin-118 "California’s Groundwater (Bulletin 118)"). [California's Groundwater Basin Characterization Program](https://water.ca.gov/Programs/Groundwater-Management/Bulletin-118/Basin-Characterization "California's Groundwater Basin Characterization Program") provides updated data and information to help local communities better understand their aquifer systems and support local and statewide groundwater management. Datasets, maps, models, and workflows developed under the program are available on the [Basin Characterization Program Open Data Portal Page](https://data.cnra.ca.gov/dataset/dwr-basin-characterization "Basin Characterization Program Open Data Portal Page"). Under the Basin Characterization Program, new and existing data (including airborne electromagnetic (AEM), lithology logs, and geophysical logs) are integrated to create continuous maps and three‑dimensional models. These tools help groundwater managers understand how groundwater is stored and moves through the subsurface. The program develops state‑stewarded maps and models by integrating geologic, geophysical, and hydrogeologic datasets. This page provides access to Basin Characterization Tools (BCTools)—a collection of open‑source software tools used to develop hydrostratigraphic models, texture models, and aquifer parameters. The suite includes Data2HSM, Texture2Par, and an AEM Data Preprocessor. These tools can incorporate both lithology log data and AEM data into the model‑building process. All tools, user guides, and training materials are publicly available for local groundwater managers. Tools and documentation will be updated as improvements are made. #[BCTools](https://data.cnra.ca.gov/dataset/dwr-basin-characterization-bctools/resource/d9d4792a-6746-4ea0-8e8b-694bdb545fc7 "BCTools ") BCTools is a toolset that supports construction of hydrostratigraphic models, estimation of sediment textures, and assignment of aquifer parameters for groundwater flow models. BCTools includes three main components: + Data2HSM – hydrostratigraphic modeling tools + Texture2Par – texture and aquifer parameter modeling tools + AEM Data Preprocessor – formats AEM data for use in the two tool suites BCTools integrates these components into a single interface while allowing each to be run independently. BCTools runs on Windows® operating systems. Refer to the [BCTools Framework](https://data.cnra.ca.gov/dataset/ca0e3aae-a13c-444f-88c0-ad5ed25fec28/resource/d3e6c30e-0ca3-4b5c-b975-7d67f70acac1/download/bctools_framework.png "BCTools Framework") for an overview of the key components. ##[Data2HSM](https://data.cnra.ca.gov/dataset/dwr-basin-characterization-bctools/resource/73bb5449-b7a8-43f9-89c5-c7b69127594b "Data2HSM") Data2HSM is a flexible suite of machine‑learning tools for constructing hydrostratigraphic models (HSMs). It enables users to combine DWR’s statewide AEM datasets with other subsurface data to develop basin hydrostratigraphy. Possible outputs include: + Groundwater model layering prototypes + Hydrogeologic conceptual models + 3D lithologic models + Surfaces such as aquifer/aquitard bases, geologic formations, the water table, base of freshwater, or top of bedrock Data2HSM consists of one interface with three main tools: + Gaussian Mixture Model + Smart Interpretation + GeoPDNN ###Gaussian Mixture Model (GMM) GMM converts AEM data into 2D and 3D lithologic or stratigraphic models. Users can also include existing stratigraphic or texture models to guide the tool. GMM uses unsupervised machine learning to assign data points to user‑defined clusters that represent either lithologic groups (e.g., sand, clay, gravel) or stratigraphic units. When configured for stratigraphy, the tool uses prior geologic knowledge and a coordinate transformation to help maintain geologic consistency. ###Smart Interpretation (SI) Smart Interpretation helps users rapidly delineate hydrogeologic surfaces such as aquifer/aquitard bases, the water table, the base of freshwater, or the top of bedrock. Users provide a small set of interpretations—either from past work or from other datasets—and SI uses polynomial regression informed by AEM data to complete the surface. An interactive 3D viewer allows additional interpretations using AEM sections, boring logs, and geologic cross‑sections. Multiple surfaces can be interpreted in one session. ###Geological Pseudolabel Deep Neural Network (GeoPDNN) GeoPDNN is a deep‑learning tool that generates continuous 3D geological models from interval‑based datasets such as boring logs or AEM soundings. It takes in location coordinates and interval data and outputs a 3D stratigraphic model and surfaces marking the base of each unit. ##[Texture2Par](https://data.cnra.ca.gov/dataset/dwr-basin-characterization-bctools/resource/40b4f199-39d8-408b-9105-edc3b386f559 "Texture2Par") Texture2Par generates 3D texture models and aquifer parameter datasets for groundwater flow modeling. It consists of two tools—Texture Model (TM) and Aquifer Flow Parameters (AFP)—now combined into a single Texture2Par interface. Written in Fortran, Texture2Par can be used standalone or within a larger automated workflow such as groundwater model calibration. ###Texture Model (TM) Texture2Par:TM uses ordinary, block, or indicator kriging and co‑kriging to convert lithology logs, and geophysical data interpreted for texture (from AEM or geophysical logs) into 3D texture models. It supports anisotropic and isotropic variograms and cross‑variograms. ###Aquifer Flow Parameters (AFP) Texture2Par:AFP uses empirical relationships between texture and aquifer properties to estimate horizontal and vertical hydraulic conductivity, specific yield, and specific storage throughout a model domain. AFP can use co‑kriging to integrate texture data from well logs, boring logs, and AEM. The tool also supports a pilot‑point approach to incorporate spatially distributed aquifer parameter measurements, such as pumping test results. AFP can directly output parameter files for IWFM and MODFLOW. ##AEM Data Preprocessor The AEM Data Preprocessor prepares AEM datasets from DWR’s statewide surveys for use in Texture2Par:TM and Data2HSM’s GMM and SI tools. It takes resistivity models and coarse fraction models as inputs and outputs a single formatted file suitable for use in the modeling tools. The Preprocessor must be run before using AEM data in Data2HSM or Texture2Par.