Julia Lorenz
Julia Lorenz
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Julia Lorenz has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Ademir Marques Junior
Vizlab|X-Reality and Geoinformatics Lab, Department of Graduate Program in Applied Computing, UNISINOS University, São Leopoldo 93022-750, Brazil
1 shared publication - Alysson Soares Aires
Vizlab|X-Reality and Geoinformatics Lab, Department of Graduate Program in Applied Computing, UNISINOS University, São Leopoldo 93022-750, Brazil
1 shared publication - Daniel Capella Zanotta
Center of Excellence in Geoinformatics and Visual Computing, University of Vale Do Rio Dos Sinos, São Leopoldo, Rio Grande Do Sul, Brazil
1 shared publication - Delano Menecucci Ibanez
Petrobras Research and Development Center (CENPES), Rio de Janeiro 21941-915, Brazil
1 shared publication - Diego Mariani
Vizlab|X-Reality and Geoinformatics Lab, Department of Graduate Program in Applied Computing, UNISINOS University, São Leopoldo 93022-750, Brazil
1 shared publication - Graciela Racolte
Vizlab|X-Reality and Geoinformatics Lab, Department of Graduate Program in Applied Computing, UNISINOS University, São Leopoldo 93022-750, Brazil
1 shared publication
Latest Publications
- Open Access
Advancing Mineral Exploration: Robust and Interpretable Carbonate Quantification in Drill Cores via Hyperspectral Machine Learning
Accurate quantification of mineralogical composition in carbonate rocks is essential for reservoir characterization in the oil industry, directly influencing petrophysical properties such as porosity and permeability. However, traditional methods such as X-ray diffraction (XRD) are destructive and provide limited spatial sampling. The aim of this study was to develop and validate a workflow for the continuous quantification of calcite and dolomite in drill cores from the Brazilian pre-salt oil province by integrating short-wave infrared (SWIR) hyperspectral imaging (HSI) and Machine-Learning algorithms. A total of 80 m of cores were evaluated using 170 XRD-validated samples to calibrate linear, nonlinear, and ensemble models. The results showed that the combination of Multiplicative Scatter Correction (MSC) preprocessing with Multilayer Perceptron (MLP) and Support Vector Regression (SVR) achieved the best performance, reaching an of 0.84. Explainable Artificial Intelligence (SHAP) confirmed the relevance of diagnostic bands between 2330 and 2360 nm, improving geological interpretability of the predictions. The proposed methodology provides a non-destructive and high-resolution alternative for mineralogical profiling, supporting the evaluation of complex reservoirs and decision-making in the oil and gas industry. Although the workflow was validated using a specific pre-salt dataset, future studies should assess its transferability to other carbonate reservoirs and broader geological settings.Open AccessAdvancing Mineral Exploration: Robust and Interpretable Carbonate Quantification in Drill Cores via Hyperspectral Machine Learning
Accurate quantification of mineralogical composition in carbonate rocks is essential for reservoir characterization in the oil industry, directly influencing petrophysical properties such as porosity and permeability. However, traditional methods such as X-ray diffraction (XRD) are destructive and provide limited spatial sampling. The aim of this study was to develop and validate a workflow for the continuous quantification of calcite and dolomite in drill cores from the Brazilian pre-salt oil province by integrating short-wave infrared (SWIR) hyperspectral imaging (HSI) and Machine-Learning algorithms. A total of 80 m of cores were evaluated using 170 XRD-validated samples to calibrate linear, nonlinear, and ensemble models. The results showed that the combination of Multiplicative Scatter Correction (MSC) preprocessing with Multilayer Perceptron (MLP) and Support Vector Regression (SVR) achieved the best performance, reaching an of 0.84. Explainable Artificial Intelligence (SHAP) confirmed the relevance of diagnostic bands between 2330 and 2360 nm, improving geological interpretability of the predictions. The proposed methodology provides a non-destructive and high-resolution alternative for mineralogical profiling, supporting the evaluation of complex reservoirs and decision-making in the oil and gas industry. Although the workflow was validated using a specific pre-salt dataset, future studies should assess its transferability to other carbonate reservoirs and broader geological settings.