Harnessing Cloud Computing for Geophysical Exploration

Geophysical exploration methods are important in discovering essential resources like oil and gas. However, traditional exploration often involves environmentally detrimental practices. To address this, software solutions simulate seismic imaging techniques for oil detection. In this scenario, the industry is now transitioning these applications to cloud-based Software as a Service (SaaS) models, offering benefits like resource optimization, eco-friendliness, and advanced data analytics. This shift, however, presents challenges in performance, scalability, and cost management. This paper presents a case study on the Fletcher modeling application as a SaaS in geophysical exploration, exploiting cloud hardware heterogeneity. Through an extensive set of experiments on the Google Cloud instances with different multicore processors, we show that the Fletcher SaaS model scales with increased hardware resources, with AMD instances offering a better performance-cost trade-off than Intel.