This paper presents a soft computing approach to detect discontinuities, with applications in seismic analysis and beyond. The authors propose using fuzzy logic to translate imprecise knowledge about discontinuities into precise strategies for detection. The main idea is to define continuity informally as small changes in input leading to small changes in output. Using fuzzy logic, they define a membership function to quantify the degree of confidence that a value is "small." This allows them to determine when a function is continuous or discontinuous by comparing the ratio of changes in output to changes in input with a threshold. The authors apply this approach to detect faults in seismic data. They use data from a 2014 study on the San Jacinto fault, where over 1000 seismic sensors were placed on a grid. By analyzing the seismic signals, they identified the location of the fault by detecting abrupt changes in signal amplitude. The method was tested on known fault locations and showed promising results. The approach is based on the idea that discontinuities in physical processes can be detected by comparing the ratio of changes in output to changes in input. When this ratio exceeds a certain threshold, it indicates a discontinuity. The method was applied to seismic data, where it successfully identified fault locations by detecting abrupt changes in signal amplitude. The authors conclude that soft computing techniques can be effective in detecting discontinuities, even when the exact equations governing the process are unknown. This approach has potential applications in various fields, including geosciences and civil engineering, where detecting discontinuities is crucial for understanding and predicting natural phenomena. The method is particularly useful in situations where the exact nature of the discontinuity is not known, but it is known that the process is discontinuous.This paper presents a soft computing approach to detect discontinuities, with applications in seismic analysis and beyond. The authors propose using fuzzy logic to translate imprecise knowledge about discontinuities into precise strategies for detection. The main idea is to define continuity informally as small changes in input leading to small changes in output. Using fuzzy logic, they define a membership function to quantify the degree of confidence that a value is "small." This allows them to determine when a function is continuous or discontinuous by comparing the ratio of changes in output to changes in input with a threshold. The authors apply this approach to detect faults in seismic data. They use data from a 2014 study on the San Jacinto fault, where over 1000 seismic sensors were placed on a grid. By analyzing the seismic signals, they identified the location of the fault by detecting abrupt changes in signal amplitude. The method was tested on known fault locations and showed promising results. The approach is based on the idea that discontinuities in physical processes can be detected by comparing the ratio of changes in output to changes in input. When this ratio exceeds a certain threshold, it indicates a discontinuity. The method was applied to seismic data, where it successfully identified fault locations by detecting abrupt changes in signal amplitude. The authors conclude that soft computing techniques can be effective in detecting discontinuities, even when the exact equations governing the process are unknown. This approach has potential applications in various fields, including geosciences and civil engineering, where detecting discontinuities is crucial for understanding and predicting natural phenomena. The method is particularly useful in situations where the exact nature of the discontinuity is not known, but it is known that the process is discontinuous.