188 Geophysics in Geothermal Exploration and the rigidity of the rocks involved. Unlike earlier scales, Mw does not saturate for large earthquakes, making it the preferred standard for global seismology. Modern earthquake magnitude estimation has benefited from advancements in seismic instrumentation and computational methods. Broadband seismometers, capable of capturing a wide range of frequencies, allow for detailed analysis of seismic waveforms across the globe. These instruments provide the data necessary for calculating magnitudes using regional and global network observations. Machine learning has recently entered the field of magnitude estimation, offering tools to analyze complex seismic datasets and refine magnitude calculations. Algorithms trained on historical seismic data can predict magnitudes with high accuracy, even for events with unconventional waveforms or sparse station coverage. These advancements underscore the ongoing progress in seismology, ensuring that magnitude estimates remain a critical tool for understanding earthquake dynamics and microseismicity. Focal Mechanism Determination The focal mechanism of an earthquake describes the orientation and type of faulting that occurs during the rupture, providing insights into the forces driving tectonic processes (Byerly, 1955). This information is typically represented by a “beachball diagram”, which visually depicts the fault’s geometry and slip direction based on seismic wave patterns. The analysis of focal mechanisms has been integral to seismology since the mid-20th century, offering valuable clues about earthquake dynamics and regional stress fields. Early methods for determining focal mechanisms relied on the first-motion polarity of seismic waves. When an earthquake occurs, compressional P-waves radiate outward, creating zones of compression (upward motion) and dilation (downward motion) recorded at seismic stations. By mapping these first-motion polarities, seismologists can infer the orientation of the fault plane and the direction of slip. While effective, first-motion polarity analysis required a dense distribution of seismic stations for reliable results, limiting its application in remote regions or areas with sparse networks. The advent of waveform modeling in the 1970s marked a major advance in focal mechanism determination. By analyzing the amplitude and shape of seismic waves, particularly the long-period components, scientists could model the faulting process more precisely. Waveform modeling also enabled the estimation of moment tensors, mathematical representations of the forces involved in an earthquake. Moment tensor inversion, introduced in the 1980s, uses seismic waveforms to solve for the fault plane orientation, slip direction, and seismic moment, providing a comprehensive description of the earthquake source. Modern techniques for determining focal mechanisms combine data from dense regional networks, broadband seismic stations, and advanced computational models. Automated systems, such as the Global Centroid Moment Tensor (GCMT) catalog, continuously process seismic data to generate focal mechanisms for significant earthquakes worldwide. These systems rely on inversion algorithms that use broadband waveform data to produce accurate and reliable solutions, offering insights into the faulting style and regional stress regime Machine Learning has recently been applied to focal mechanism analysis, offering tools to process vast amounts
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