Stratigraphic forward modeling software package for research and education

TL;DR

SedSimple is an open-source stratigraphic forward modeling software designed for research and education, offering flexible algorithms, visualization tools, and rapid computations to facilitate geological simulations and workflows.

Contribution

It introduces a customizable, open-source modeling package with multiple sedimentary algorithms and visualization capabilities, enabling advanced research and educational applications.

Findings

Faster than commercial counterparts at similar resolution

Supports multiple sedimentary processes and their interactions

Suitable for workflows requiring multiple model realizations

Abstract

We have developed an open-source software package (SedSimple) for stratigraphic forward modeling. It is aimed for use in research and education. The package uses ASCII files for input and output to facilitate understanding, debugging and preparation of multiple data sets though simple user-developed scripts. A free graphic program is available to assist in editing input and rendering results in 3D plus geologic time animation. The open-source concept offers the ability to understand the algorithm in detail, and modify or extend it as needed. Several algorithms are available in the program to model various sedimentary environments and processes, including alluvial, fluvial, turbiditic, carbonatic, deep-sea clastic, wave action, vertical tectonics and sea-level changes. These processes can be run concurrently. Furthermore, the effect of some processes can be made to affect others (for example wave action can affect carbonate growth, while also causing erosion and redistribution of the deposits). The algorithms are fully deterministic. As most forward models of natural environments, the results cannot be readily conditioned to data. However, the model is much faster than similar commercial packages of the same resolution. Therefore, we foresee that one of the main uses of the package will be to study workflows that require multiple realizations to obtain statistical predictions, such as Monte Carlo methods, combination with geostatistical methods, artificial intelligence and machine learning that require multiple inputs of geologically realistic models.