| Autor: |
Jiaxin Yu, Tapan Mukerji, Per Avseth |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
SoftwareX, Vol 24, Iss , Pp 101567- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2352-7110 |
| DOI: |
10.1016/j.softx.2023.101567 |
| Popis: |
Rock physics aims to understand the relationship between the physical properties of rocks and geophysical observables under various conditions. The generic knowledge provides valuable insights into the behavior of subsurface rocks and has been applied in various fields. However, the availability of comprehensive open-source Python libraries for rock physics is quite limited. To address this limitation, we present rockphypy: a comprehensive and streamlined Python library that offers access to a vast array of rock physics models and workflows ranging from basic to sophisticated. The library is designed to be easily embedded in interdisciplinary fields such as deep neural networks and probabilistic frameworks, leveraging the rich resources of Python. Currently, rockphypy implements ten modules with over 100 methods, accessible through a straightforward and user-friendly API that facilitates various modeling tasks in rock physics. Its modular design allows easy extension to incorporate new features and functionalities. In addition to the versatility of the library, we have shown that rockphypy also greatly simplifies practical tasks that require many different rock physics models, enabling fast experimentation and iteration of research and practical programs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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