The success of exploration and development in the oil and gas industry largely depends on the seismic method, which in turn is dependent on seismic interpretation. This success is because seismic data provide a detailed image of the subsurface, providing not only an image of the current reflector geometries, but through the geoscientist's understanding of geologic processes, of the geologic past. Whether analyzing migrated gathers or stacked traces, the processed seismic data are in the form of reflection amplitudes. Skilled interpreters identify the presence of hydrocarbons by recognizing anomalous high, low, or dim amplitudes. They identify lateral changes in thickness or lithology of thin layers by recognizing lateral changes in amplitude, frequency, and phase of the seismic wavelet. Interpreters also identify faults by recognizing abrupt lateral changes in wavelet alignment and/or reflector orientation, and they differentiate seismic noise by applying their understanding of seismic data acquisition and processing techniques. Not all interpreters are highly skilled in understanding the limitations of seismic acquisition and processing. Nor are all interpreters comfortable with how the seismic data respond to subtle changes in stratigraphy, to architectural elements of depositional systems, to structural components of tectonic deformation, to changes in reflection pattern due to diagenesis, or the amplitude response to hydrocarbons or CO 2 . Seismic attributes quantitively capture effects that a skilled interpreter sees in reflector amplitude, frequency, phase, bandwidth, continuity, orientation, and signal-to-noise ratio. These attributes are not magical; rather they quantitatively map predefined families of patterns seen in the seismic amplitude data. In 2024, seismic attributes and impedance inversion provide excellent images of subtle features in good-quality data that are easy to overlook, especially given the time constraints for interpretation projects. In contrast, attributes have limited value in the analysis of very noisy or poorly imaged data. Here, the human interpreter's ability in pattern recognition along with an understanding of geologic processes and artifacts in seismic data processing is superior to the fixed patterns programmed into today's attribute and impedance inversion algorithms. Seismic attributes extend the concepts of seismic geomorphology, enabling the mapping of structural and stratigraphic features. In combination with impedance, they also facilitate the recognition of seismic facies, and when calibrated with well data, they help predict lithology, porosity, and fluid content. The successful application of seismic attributes requires an understanding of not only their physical principles but also a grasp of seismic data quality and geologic processes. An interpreter may encounter hundreds of attributes ranging from RMS amplitude, spectral components, coherence, curvature, AVO, to impedance and attenuation. Many of these attributes are the same but masquerade with different names, while others are redundant, providing nearly identical results. Still others may prove to be of little practical value. The authors' aim is to provide clarity and organization to this overwhelming list of attributes and guide interpreters in selecting the most appropriate ones for specific objectives.