186 Geophysics in Geothermal Exploration Detection of earthquakes Earthquake detection has undergone substantial development over the past decades, integrating traditional techniques with advanced computational tools to improve accuracy and reliability. The Short-Time Average/Long-Time Average (STA/LTA) method, introduced in the mid-20th century, remains a widely used and foundational approach for seismic monitoring. This method calculates the ratio of shortterm signal energy to long-term background noise to identify sudden energy surges indicative of seismic events. STA/LTA gained prominence in the 1970s with the advent of automated seismic networks, enabling real-time earthquake detection. However, while effective for detecting moderate to large earthquakes, STA/LTA is prone to false positives in environments with high cultural or natural noise, such as urban areas or regions affected by storms. To address these limitations, template matching emerged in the 1990s and has since become a critical tool for detecting smaller or repeating earthquakes. This technique involves comparing incoming seismic waveforms with a database of previously recorded seismic signals. When a match is identified, it confirms the occurrence of a similar event. Template matching is particularly effective in regions with dense seismic networks, such as California or Japan, where high-quality waveform libraries are available. Its ability to identify microseismic events and repeating patterns, such as slow-slip events or earthquake swarms, has proven invaluable for understanding seismicity in complex tectonic settings. In the 2010s, machine learning revolutionized earthquake detection by introducing powerful algorithms capable of analyzing vast amounts of seismic data with minimal human intervention. Neural networks are trained on labeled datasets to learn the features that distinguish seismic events from noise. These systems can process continuous seismic streams, detecting small-magnitude earthquakes and events buried within background noise (Mousavi et al., 2020). Hybrid approaches, combining STA/LTA, template matching, and machine learning, represent the cutting edge of earthquake detection. These integrated systems leverage the strengths of each method, ensuring high sensitivity to small and large earthquakes while reducing false alarms (Yue et al., 2021). Today’s detection frameworks reflect decades of innovation, enabling precise monitoring of seismic activity across diverse tectonic environments and laying the groundwork for improved earthquake response and mitigation efforts. Localization Earthquake localization, the process of determining the origin of an earthquake in time and space, has advanced dramatically over the past century. The foundational approach relies on analyzing the arrival times of seismic waves, particularly P-waves (primary waves) and S-waves (secondary waves), which travel at different speeds through the Earth. By measuring the time differences in their arrivals at multiple seismic stations, the distance to the earthquake’s epicenter can be estimated. This method, developed in the early 20th century, became a standard tool in global seismology with the
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