Automatic Fault Detection for 2D Seismic Data Based on the Seismic Coherence of Mutative Scale Analysis Window

Part 7: Fault DiagnosisInternational audienceFault detection is a very challenging problem on seismic interpretation. In fact, the process of fault detection contains seismic attribute extraction, seismic attribute enhancement and fault line detection. It is clear that the extracted seismic attributes are key to the fault detection process. Traditionally, as an important seismic attribute, the seismic coherence is generally employed in the fault detection process, but the size of the analysis window often effects the value of the seismic coherence, and there is a trade-off between the vertical resolution and the lateral resolution. In order to overcome this problem, we propose a new kind of seismic coherence with a mutative scale analysis window, and utilize it to locate the fault lines. It is demonstrated by the experimental results that our proposed seismic coherence is more suitable for the fault line detection in comparison with the traditional seismic coherence

TOMOGRAPHIC INVERSION OF NORMALIZED DATA: DOUBLE-TRACE TOMOGRAPHY ALGORITHMS

Tomography is widely used in geophysics as a technique for imaging geological structures by means of data that are line integrals of physical characteristics. In some transmission measurements, due to various kinds of normalization, the measured data are related to two (the current and the reference) raypaths and can be expressed as a function of differences between line integrals. This is the case, for example, in seismo-acoustic emission measurements, when (since the exact start time is unknown) only the differences between traveltimes (differences between line integrals of the slowness) can be determined. Similarly the use of normalized Fourier amplitudes results in data dependent upon the difference between line integrals of the absorption coefficient (computed along the actual and the reference raypaths). In order to invert these data the ordinary tomography algorithms should be modified. Some generalizations are presented for series expansion tomography methods in order to make them applicable to reconstruction problems in which the input data are differences between two line integrals. The conjugate gradient and the simultaneous iterative reconstruction technique (SIRT) methods were adapted and tested. It is shown that the modified tomography algorithms are stable and sufficiently accurate for practical use. In the reconstruction of noise-free difference data, the conjugate gradient algorithm is found to be faster and more accurate while, in the case of noisy difference data, the modified SIRT algorithm is more stable and insensitive to noise