## 3 Methodology
### Data Preparation  
The Norne oil field is characterized by a combination of petrophysical and seismic data providing comprehensive insight into its structure and production potential. Petrophysical information obtained from logs of two main wells reveal properties such as compressional velocity (\(V_{P}\)), shear velocity (\(V_{S}\)), density (\(\rho\)), effective porosity (\(\phi\)), and clay content (Vsh). Despite sections with missing data and the presence of noise and outliers, these logs offer a detailed understanding of the geological formations present in the field. On the other hand, seismic data, depicted in Figure 2b, show a pronounced anticlinal geological structure, as well as the presence of olistric faults derived from the anticlinal. The presence of anomalies in the range of 2000 to 3200 milliseconds suggests the possible existence of gas or oil accumulations in these areas. The combination of petrophysical and seismic data provides a solid foundation for the assessment and efficient exploitation of resources in the Norne field. More details about the geology of the Norne field dataset can be found in [PERSON] (2016), [PERSON] and [PERSON] (2017), and [PERSON] et al. (2018). At this point, identifying, assessing, choosing, and filtering source data constitute the main preprocessing tasks (see phase I, Figure 1). The research includes well logs and three-dimensional seismic data from seventeen wells gathered from different points in the study region. Essential characteristics, including density, porosity, clay content, and P- and S-wave velocities, are included in these well logs. Both seismic data and well logs are subjected to a comprehensive review process. In addition, a synthetic seismic model is selected for investigation and testing, acting as a standard for different inversion processes. Processing Flow  
Estimating a wavelet requires careful consideration of many elements, including Earth filtering, data processing, and seismic source characteristics. This is especially true when dealing with unknown quantities, such as the noise component. Therefore, to guarantee correctness in analysis and interpretation, choosing and estimating a wavelet calls for a methodical and exacting procedure. The inversion and prediction processes are made more complex by these unknown characteristics, necessitating the development of strong estimating and application techniques. In contrast to the amplitude, the phase spectrum is more difficult (S  
Hence, ensuring the correct phase spectrum is essential for the results of various inversion and prediction processes. In this study, the Ricker wavelet was initially used but was later replaced with a wavelet determined by sparse spike deconvolution, developed in [29; 41]. The resulting wave exhibits high-frequency behavior, which allows the modeling of key features of the data that differ from the Ricker wavelet, which has a wide range of frequencies and facilitates the preservation of most of the geological features in the seismic data. This change ensures the precision required for the BLI processes and the predictive models employed for porosity estimation. To enhance the resolution and quality of the results, in this phase, apart from the Ricker wavelet, a wavelet sparse spike, extracted directly from the seismic data, was employed using the sparse spike deconvolution process developed by [29] (see Figure 3). At this stage, a meticulous cleaning was carried out on various well logs to identify and eliminate extreme and outlier data. Machine learning (ML) algorithms, coupled with the Savitzky-Golay filter algorithm [42], were employed for this purpose. Two distinct algorithms were assessed: the Isolation Forest (IF) algorithm and the Local Outlier Factor (LOF) [43]. Following thorough analysis utilizing a confusion matrix, the IF algorithm exhibited superior performance and was, thus, chosen for outlier detection and removal.