Update paper and bib

joss/paper.bib

@@ -8,7 +8,7 @@ @article{Ravasi:2020
     url = {https://www.sciencedirect.com/science/article/pii/S2352711019301086}
 }
 
-@article{Dalcin,
+@article{Dalcin:2021,
     author = {Dalcin, Lisandro and Fang, Yao-Lung L.},
     journal = {Computing in Science & Engineering},
     title = {mpi4py: Status Update After 12 Years of Development},
@@ -20,7 +20,7 @@ @article{Dalcin
     doi = {10.1109/MCSE.2021.3083216}
 }
 
-@article{Mortensen2019,
+@article{Mortensen:2019,
     doi = {10.21105/joss.01340},
     url = {https://doi.org/10.21105/joss.01340},
     year = {2019},
@@ -33,7 +33,7 @@ @article{Mortensen2019
     journal = {Journal of Open Source Software}
 }
 
-@article{Morgan2024,
+@article{Morgan:2024,
     doi = {10.21105/joss.06415},
     url = {https://doi.org/10.21105/joss.06415},
     year = {2024},
@@ -59,7 +59,7 @@ @article{mpi4jax
     journal = {Journal of Open Source Software}
 }
 
-@article{harris2020array,
+@article{Harris:2020,
     title = {Array programming with {NumPy}},
     author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
  van der Walt and Ralf Gommers and Pauli Virtanen and David
@@ -82,10 +82,36 @@ @article{harris2020array
     url = {https://doi.org/10.1038/s41586-020-2649-2}
 }
 
-@inproceedings{cupy_learningsys2017,
+@inproceedings{cupy,
     author = "Okuta, Ryosuke and Unno, Yuya and Nishino, Daisuke and Hido, Shohei and Loomis, Crissman",
     title = "CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations",
     booktitle = "Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS)",
     year = "2017",
     url = "http://learningsys.org/nips17/assets/papers/paper_16.pdf"
 }
+
+@article{Ravasi:2021,
+    author = {Ravasi, Matteo and Birnie, Claire},
+    title = "{A joint inversion-segmentation approach to assisted seismic interpretation}",
+    journal = {Geophysical Journal International},
+    volume = {228},
+    number = {2},
+    pages = {893-912},
+    year = {2021},
+    month = {09},
+    issn = {0956-540X},
+    doi = {10.1093/gji/ggab388},
+    url = {https://doi.org/10.1093/gji/ggab388},
+    eprint = {https://academic.oup.com/gji/article-pdf/228/2/893/40552446/ggab388.pdf},
+}
+
+@article{Nemeth:1999,
+  title={Least-squares migration of incomplete reflection data},
+  author={Nemeth, Tamas and Wu, Chengjun and Schuster, Gerard T},
+  journal={Geophysics},
+  volume={64},
+  number={1},
+  pages={208--221},
+  year={1999},
+  publisher={Society of Exploration Geophysicists}
+}

joss/paper.md

@@ -46,29 +46,21 @@ When addressing distributed inverse problems, we identify three distinct use cas
 flexible, scalable framework:
 
 - **Fully Distributed Models and Data**: Both the model and data are distributed across nodes, with minimal
-  communication during the modeling process. Communication
-  occurs mainly during the solver stage when dot products or regularization, such as the Laplacian, are applied. This
-  scenario is common
-  in [Post-Stack seismic inversion](https://pylops.readthedocs.io/en/stable/tutorials/poststack.html#sphx-glr-tutorials-poststack-py),
-  where each node handles a portion of the model and data, and communication only happens when adding spatial
-  regularizers.
+  communication during the modeling process. Communication occurs mainly during the solver stage when dot 
+  products or regularization, such as the Laplacian, are applied. In this scenario where each node
+  handles a portion of the model and data, and communication only happens between the model and data at each node.
 
 - **Distributed Data, Model Available on All Nodes**: In this case, data is distributed across nodes while the model is
-  available at all nodes. Communication is required
-  during the adjoint pass when models produced by each node need to be summed, and in the solver when performing dot
-  products on the data. This pattern is typical in fields
-  like [CT/MRI imaging](https://pylops.readthedocs.io/en/stable/tutorials/ctscan.html#sphx-glr-tutorials-ctscan-py)
-  and [seismic least-squares migration](https://pylops.readthedocs.io/en/stable/tutorials/lsm.html#sphx-glr-tutorials-lsm-py).
+  available at all nodes. Communication is required during the adjoint pass when models produced by each node need 
+  to be summed, and in the solver when performing dot products on the data.
 
 - **Model and Data Available on All Nodes or Master**: Here, communication is confined to the operator, with the master
-  node distributing parts of the model or data to
-  workers. The workers then perform computations without requiring communication in the solver. An example of this is
-  [MDC-based inversions](https://github.com/DIG-Kaust/TLR-MDC), which allow for the storage
-  of out-of-memory kernels.
+  node distributing parts of the model or data to workers. The workers then perform computations without requiring 
+  communication in the solver.
 
-Recent updates to mpi4py (version 3.0 and above) [@Dalcin] have simplified its integration, enabling more efficient data
+Recent updates to mpi4py (version 3.0 and above) [@Dalcin:2021] have simplified its integration, enabling more efficient data
 communication between nodes and processes.
-Some projects in the Python ecosystem, such as mpi4py-fft [@Mortensen2019], mcdc [@Morgan2024], and mpi4jax [@mpi4jax],
+Some projects in the Python ecosystem, such as mpi4py-fft [@Mortensen:2019], mcdc [@Morgan:2024], and mpi4jax [@mpi4jax],
 utilize MPI to extend its capabilities,
 improving the efficiency and scalability of distributed computing.
 
@@ -97,7 +89,7 @@ The main components of the library include:
 
 The `pylops_mpi.DistributedArray` class serves as the fundamental array class used throughout the library. It enables
 the
-partitioning of large NumPy[@harris2020array] or CuPy[@cupy_learningsys2017] arrays into smaller local arrays, which can
+partitioning of large NumPy[@Harris:2020] or CuPy[@cupy] arrays into smaller local arrays, which can
 be distributed across different ranks.
 Additionally, it allows for broadcasting the NumPy or CuPy array to multiple processes.
 
@@ -153,8 +145,14 @@ the need for explicit inter-process communication, thereby avoiding heavy commun
 
 # Use Cases
 
-- *Post Stack Inversion - 3D* - 
+- *Post Stack Inversion - 3D* - Post-stack inversion represents the quantitative characterization of the
+  subsurface [@Ravasi:2021]. In 3D, both the post-stack linear model and the data are three-dimensional. PyLops-MPI
+  solves this problem by distributing one of the axes across different ranks, allowing matrix-vector products and
+  inversions to take place at each rank, which are later gathered to obtain the inverted model.
 
-- *Least-Squares Seismic Migration* - 
+- *Least-Squares Seismic Migration (LSSM)* involves manipulating seismic data to create an image of subsurface
+  reflectivity[@Nemeth:1999]. PyLops MPI breaks this problem by distributing the sources across different MPI ranks.
+  Each rank applies the source modeling operator to perform matrix-vector products with the broadcasted reflectivity.
+  The resulting data is then inverted using the MPI-Powered solvers to produce the desired subsurface image.
 
-# References
+# References