This thesis focuses on the value and use of various image processing techniques to clarify, discuss, and enhance the understanding of geophysical data, including: Image arithmetic operations: image addition, subtraction, multiplication, division, square, square root, logarithm, thresholding, and histogram equalization. The results of image arithmetic operations on geophysical data indicate that addition, multiplication, square, and thresholding can be useful for enhancing signal, while subtraction may be used to study noise and histogram equalization can be used to enhance image contrast. Image filters are also applied to geophysical data. All images filtered by low frequency show that different filters produce similar results if masks of order three or less are used. To identify different filters, higher than order 3 masks have to be used. The third area of study is by image morphological processing: dilation, erosion, interior/exterior outline, and salt and pepper noise removal techniques. For geophysical data, image interior/exterior outline is similar to edge detection, but differs in methodology. The image generated by salt and pepper noise removal is also similar to that produced by low frequency filters, but the methodology, again, is different. Both dilation and erosion can emphasize structural features in a geophysical image. Image edge detection discusses how to extract structural features. The detected images show that both the Prewitt and the Sobel techniques are the best candidates for future applications. The second level candidates are DOG and LOG. The Roberts and Laplacian techniques are generally good, but contain noise. The Kirsch technique is poor and should not be used.