Automatic lithology identification in meteorite impact craters using machine learning algorithms.

Identifying lithologies in meteorite impact craters is an important task to unlock processes that have shaped the evolution of planetary bodies. Traditional methods for lithology identification rely on time-consuming manual analysis, which is costly and limits the efficiency of rapid decision-making. This paper utilizes different machine learning algorithms namely Random Forest, Decision Tree, K Nearest Neighbors, and Logistic Regression with Grid Search to classify rock lithologies using data from the Bosumtwi impact crater in Ghana. A repeated stratified k-fold cross-validation method is applied to Grid Search to select the best combination of hyperparameters. The findings demonstrate that the Random Forest algorithm achieves the most promising results in classifying lithologies in the meteorite impact crater with an accuracy score of 86.89%, a recall score of 84.88%, a precision score of 87.21%, and an F1 score of 85.48%. The findings also suggest that more high-quality data has the potential to further increase the accuracy scores of the machine learning algorithm. In conclusion, this study demonstrates the significant potential of machine learning techniques to revolutionize lithology identification in meteorite impact craters, thus paving the way for their influential role in future space exploration endeavors.

Seismic activities in Ghana: A systematic review.

Seismic activities pose significant challenges to societies globally. Therefore, it is crucial to understand their occurrence, patterns, and impacts. By studying seismic activities, including earthquakes, researchers can investigate their occurrence, distribution, and characteristics which can provide effective management and risk reduction strategies. The southern part of Ghana is prone to earthquakes and this study aims to shed more light into the nature of seismic events in the area and country at large. A systematic review was conducted using the PRISMA technique across three electronic databases (SCOPUS, Dimensions and Google Scholar) to identify relevant studies published between 2000 and 2023. Extraction of data and quality assessment were performed in order to ensure reliability and validity of included studies. Results identified only 17 papers from published records to meet the inclusion criteria. Despite the grave threat earthquakes pose to vital infrastructure and human life in Ghana, research in this area remains remarkably deficient. Our findings underscore the urgent need for further study given the catastrophic potential of seismic disasters in the region. Moreover, upon scrutinizing the methodologies deployed in extant literature concerning seismic activity in Ghana, a recurring constraint that emerged was the scarce availability of data. In essence, this study offers an indispensable panorama of earthquake research in Ghana, bridging the existing knowledge chasm on seismic phenomena in the region. The insights gleaned from this review promise to fortify our comprehension of Ghana's seismic activities, thereby bolstering the country's capabilities for more effective preparedness and response strategies.

Characterization of complex fluvio-deltaic deposits in Northeast China using multi-modal machine learning fusion.

Due to the lack of petroleum resources, stratigraphic reservoirs have become an important source of future discoveries. We describe a methodology for predicting reservoir sands from complex reservoir seismic data. Data analysis involves a bio-integrated framework called multi-modal machine learning fusion (MMMLF) based on neural networks. First, acoustic-related seismic attributes from post-stack seismic data were used to characterize the reservoirs. They enhanced the understanding of the structure and spatial distribution of petrophysical properties of lithostratigraphic reservoirs. The attributes were then classified as varied modal inputs into a central fusion engine for prediction. We applied the method to a dataset from Northeast China. Using seismic attributes and rock physics relationships as input data, MMMLF was performed to predict the spatial distribution of lithology in the Upper Guantao substrata. Despite the large scattering in the acoustic-related data properties, the proposed MMMLF methodology predicted the distribution of lithological properties through the gamma ray logs. Moreover, complex stratigraphic traps such as braided fluvial sandstones in the fluvio-deltaic deposits were delineated. These findings can have significant implications for future exploration and production in Northeast China and similar petroleum provinces around the world.