1996
DOI: 10.1190/1.1444030
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A fractal‐based algorithm for detecting first arrivals on seismic traces

Abstract: A new algorithm is proposed for the automatic picking of seismic first arrivals that detects the presence of a signal by analyzing the variation in fractal dimension along the trace. The "divider-method" is found to be the most suitable method for calculating the fractal dimension. A change in dimension is found to occur close to the transition from noise to signal plus noise, that is the first arrival. The nature of this change varies from trace to trace, but a detectable change is always found to occur. The … Show more

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Cited by 161 publications
(89 citation statements)
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“…These waves pass through the earth surface and receivers are used to collect the data reflected off of the rock layers [15]. This data can be processed and analysed to develop a clear understanding of the rock surface and other geological properties of the earth.…”
Section: A First Arrival-pickingmentioning
confidence: 99%
“…These waves pass through the earth surface and receivers are used to collect the data reflected off of the rock layers [15]. This data can be processed and analysed to develop a clear understanding of the rock surface and other geological properties of the earth.…”
Section: A First Arrival-pickingmentioning
confidence: 99%
“…(13) Make (14) The benefit is that, it can separate large variables from small in the calculation, avoid the small variables are ignored when they are added with large, ensure the calculation accuracy. So (15) From equation (10) and (15), we can get (16) When j is large enough, we can get more accurate inverse matrix F -1 .…”
Section: Using the Precise Integration Methods To Get Inverse Matrixmentioning
confidence: 99%
“…So far the effective inversion algorithms mainly include: Cao [10] proposed self-incentive simultaneous algebraic reconstruction algorithm(SASIRT) which was suitable for ray distribution was not uniform or measurement error was larger, however, it had slow calculation speed and low accuracy; Saad and Schultz [11] proposed generalized minimal residual method(GMRES) which could be used for solving non symmetric linear equations, the calculation process would not be interrupted generally until obtaining the exact solution, nevertheless, there may be not converge; Van and Vorst [12] proposed the double stable conjugate gradient method (BICGSTAB), which could be used for solving linear equations whose coefficient matrix was asymmetric, it used short recursive method to reduce residual progressively, so the advantage was it occupied less memory, but the convergence was irregular, the convergence rate may be amplified severely under the condition of finite precision; LSQR with damping factor method (DLSQR) proposed by Yang [13], it improved the inversion precision effectively, avoided numerical instability of LSQR algorithm when the measurement error was large, it was especially suitable for solving the equations whose coefficient matrix was large and sparse, compared with other iteration method, it could obtain faster convergence rate and better acceptable results in solving singular or illconditioned problems, currently it is practical inversion method which was most commonly used, however, the occupation of computer memory was large and accuracy should be further improved. In recent years, artificial intelligence method was also applied to the inversion algorithm [14], such as Simulated Annealing(SA) or Genetic Algorithm(GA), but it had strong dependence on the initial model, easily influenced by random disturbance, the distribution reconstruction effect on slightly more complex medium was poor.…”
Section: Introductionmentioning
confidence: 99%
“…Certain characteristics are repeatedly calculated within successive sections of the time series, producing a time dependent function. The TOF is usually identified by an obvious change in the behavior of this function ( [7,8]). The third type of auto-picker relies on using the coherence characteristic between traces.…”
Section: Introductionmentioning
confidence: 99%