Phan mem midas gen6/21/2023 ![]() An extension of conventional crossplotting involves the use of a convolutional operator to resolve frequency differences between the target logs and the seismic data. In the linear mode, the transform consists of a series of weights derived by least-squares minimization. ![]() The thread that ties all this work together is my interest in seismic inversion. Initially this meant deterministic inversion using the convolutional model, applied to both poststack and prestack seismic data. To estimate the reliability of the derived multiattribute transform, crossvalidation is used. In this process, each well is systematically removed from the training set, and the transform is rederived from the remaining wells. The prediction error for the hidden well is then calculated. ![]() The validation error, which is the average error for all hidden wells, is used as a measure of the likely prediction error when the transform is applied to the seismic volume. The method is applied to two real data sets. ![]() In each case, we see a continuous improvement in predictive power as we progress from single-attribute regression to linear multiattribute prediction to neural network prediction. This improvement is evident not only on the training data but, more importantly, on the validation data. In addition, the neural network shows a significant improvement in resolution over that from linear regression.
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